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CVPR/2025/SegAgent_ Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories/full.md

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+# SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories
+
+Muzhi Zhu $^{1,2}$  Yuzhuo Tian $^{1}$  Hao Chen $^{1*}$  Chunluan Zhou $^{2}$  Qingpei Guo $^{2*}$  Yang Liu $^{1}$  Ming Yang $^{2}$  Chunhua Shen $^{1,2*}$
+
+1 Zhejiang University, China 2 Ant Group
+
+# Abstract
+
+While MLLMs have demonstrated impressive image understanding capabilities, they still struggle with pixel-level comprehension, limiting their practical applications. Current evaluation tasks such as VQA and visual grounding remain too coarse to assess fine-grained pixel comprehension accurately. Although segmentation is foundational for pixel-level understanding, existing methods often require MLLMs to generate implicit tokens, decoded through external pixel decoders. This approach disrupts the MLLM's text output space, potentially compromising language capabilities and reducing flexibility and extensibility while failing to reflect the model's intrinsic pixel-level understanding. Thus, we introduce the Human-Like Mask Annotation Task (HLMAT), a new paradigm where MLLMs mimic human annotators using interactive segmentation tools. Modelling segmentation as a multi-step Markov Decision Process, HLMAT enables MLLMs to iteratively generate text-based click points, achieving high-quality masks without architectural changes or implicit tokens. Through this setup, we develop SegAgent, a model fine-tuned on human-like annotation trajectories, which achieves performance comparable to SoTA methods and supports additional tasks like mask refinement and annotation filtering. HLMAT provides a protocol for assessing fine-grained pixel understanding in MLLMs and introduces a vision-centric, multi-step decision-making task that facilitates the exploration of MLLMs' visual reasoning abilities. Our adaptations of policy improvement method StaR and PRM guided tree search further enhance model robustness in complex segmentation tasks, laying a foundation for future advancements in fine-grained visual perception and multi-step decision-making for MLLMs. Code can be found at https://github.com/aim-uofa/SegAgent.
+
+# 1. Introduction
+
+Recent advancements [33, 43] in large language models (LLMs) have brought them to near-human levels in many domains. However, multimodal LLMs (MLLMs) still face significant limitations in visual tasks [44, 45], especially in complex pixel-level understanding [56], which restricts their applicability in real-world scenarios. Despite efforts to develop MLLM vision capabilities through tasks like Visual Question Answering (VQA)[19, 20], and visual grounding [2, 46], these tasks fail to assess fine-grained pixel-level comprehension effectively. On the other hand, tasks like UI Operations [64, 66] and Robotic Control [40] require pixel-level precision but are often constrained to specialized environments with limited, heterogeneous data, making them difficult to scale to complex, open-world scenarios.
+
+Segmentation [11, 28, 29, 68, 70-72] is one of the most critical visual tasks for reflecting pixel-level understanding capabilities. Moreover, there is a wealth of segmentation data [15] available, which can sufficiently meet the needs of complex open-world scenarios. Establishing a method that enables MLLMs to effectively leverage this large-scale data is essential to further explore and enhance their pixel-level understanding capabilities. However, current approaches [16, 38, 49, 50, 67] integrating MLLMs with segmentation tasks often rely on implicit token outputs and require additional pixel decoders. While effective, these methods alter the original MLLM output space, potentially compromising semantic generalization by shifting away from language-based outputs. The use of implicit tokens also limits extensibility, complicating efforts to implement a unified textual framework across diverse tasks. Furthermore, reliance on an extra pixel decoder makes it challenging to accurately reflect the MLLM's intrinsic pixel understanding capabilities [4, 41]. There are also methods [6, 18, 26, 36] that attempt to perform segmentation using purely textual outputs, such as polygon vertex sequences or sampled points along the mask contour. However, this strategy limits mask precision, and generating lengthy coordinate sequences is particularly challenging. Even for a skilled human annotator, tracing
+
+the mask contour point by point is time-intensive and laborious [23]. Recent annotation practices [15] have introduced interactive segmentation tools [15, 27] that reduce workload by allowing iterative mask refinement through positive and negative clicks. It's natural to ask whether current MLLMs can perform segmentation by emulating the trajectories of human annotators using interactive segmentation tools. This approach would enable the model to complete the segmentation task more easily in a pure-text format.
+
+Therefore, we propose a new segmentation paradigm, enabling MLLMs to imitate human annotators using interactive segmentation tools by modeling the segmentation task as a multi-step Markov Decision Process. We refer to this as the Human-Like Mask Annotation Task (HLMAT). Compared to previous vision tasks, HLMAT imposes greater demands on the model's pixel-level understanding for both input and output. This paradigm allows MLLMs to iteratively generate text-based coordinates without relying on specialized architectures or implicit tokens, supporting fair comparisons across different MLLMs. We believe HLMAT offers a novel evaluation protocol to explore and advance MLLMs' fine-grained visual capabilities.
+
+We first develop an automated algorithm to transform existing datasets into annotation trajectories. Using the trajectories, we fine-tune MLLMs, resulting in our model, SegAgent. Our experiments show that, after fine-tuning, various MLLMs achieve performance comparable to state-of-the-art methods. Additionally, we demonstrate that SegAgent not only excels at segmentation tasks but also supports mask refinement and annotation filtering effectively.
+
+Furthermore, HLMAT, as a multi-step decision-making task, directly assesses pixel-level visual skills, unlike prior MLLM decision-making tasks that focus primarily on textual reasoning [63] or simple UI-based operations [1]. Our study lays the foundation for exploring MLLMs as vision-centered, multi-step decision-making agents, addressing the gap in pixel-level visual understanding in multi-step contexts. In this work, we first examine commonly used referring segmentation datasets like RefCOCO [57] and find that their annotation complexity and quality are insufficient for multi-step decision-making. To support deeper exploration, we introduce high-quality reference segmentation datasets.
+
+Next, we investigate whether certain enhancement methods for text-based LLM decision-making can be adapted to our proposed HLMAT task. We select StaR [62], a straightforward yet representative RL-style policy improvement method, and modify it for our purposes. Our findings reveal that incorporating the StaR algorithm, as opposed to solely relying on SFT data, significantly improves the model's performance on the test dataset. Additionally, inspired by frameworks such as Tree-of-Thoughts [55], we explore inference-stage techniques. By combining process reward modeling (PRM) [22, 30] with tree search,
+
+we demonstrate that for complex scene segmentation tasks, implementing tree search effectively mitigates errors arising from inaccurate predictions, substantially enhancing the model's robustness in challenging segmentation scenarios. This work lays a strong foundation for advancing vision-centered, multi-step decision-making agents in future research.
+
+Our main contributions are as follows:
+
+- We propose Human-Like Mask Annotation Task (HLMAT), a new segmentation framework where MLLMs imitate human annotators using interactive tools, modeled as a multi-step Markov Decision Process. HLMAT raises pixel understanding demands and only requires text-based coordinate outputs, allowing fair comparison across MLLMs and serving as a new protocol for fine-grained visual capability assessment.  
+- Using an automated algorithm to generate human-like annotation trajectories from existing datasets, we fine-tune MLLMs to create SegAgent. Experiments show SegAgent achieves competitive performance, supporting segmentation, mask refinement, and annotation filtering.  
+- HLMAT lays a foundation for multi-step, vision-centered decision-making research. We introduce the HRES dataset, suitable for complex reasoning, and demonstrate that text-based decision improvement methods like StaR+ (training) and tree search with PRM (inference) effectively enhance segmentation performance.
+
+# 2. Related Work
+
+# 2.1. Pixel Understanding in MLLMs
+
+Many studies have explored pixel understanding in MLLMs by combining these models with segmentation tasks. However, most approaches [17, 38, 39, 61, 65, 67] require MLLMs to learn an implicit token and involve additional fine-tuning with a separate pixel decoder. Among them, LISA [17] was the first to integrate MLLMs with SAM [15], while subsequent works [38, 67] introduced additional tasks and larger training datasets, so we did not include them in our experimental comparisons. Other methods attempt to let MLLMs perform segmentation through text-based outputs, such as using polygon vertex sequences [36] or randomly sampled points [9] where the model predicts if a point lies within the mask. However, this approach inherently limits mask precision. Our work, on the other hand, achieves segmentation by imitating the trajectory of human annotators using interactive segmentation tools. This approach enables the model to perform highly detailed segmentation tasks through simple text-based outputs.
+
+# 2.2. MLLMs as Decision-Making Agents
+
+In the text domain, extensive research has explored how to adopt LLMs as decision-making agents and enhance
+
+their reasoning and decision-making capabilities. Some works [48, 53, 54] improve LLM reasoning through prompt engineering during inference, while others [62, 69] investigate policy improvement methods during training. However, research on decision-making agents in the visual domain remains limited. A few studies integrate MLLMs [7, 32] into training processes, but they train simple MLP or transformer layers to align action spaces without using text-based actions.
+
+As RL4VLM [63] emphasized, prior work primarily examined MLLM capabilities in non-interactive tasks. RL4VLM represents the first systematic approach to fine-tuning an entire VLM as a decision-making agent using reinforcement learning, directly interacting with the environment through open-ended text. Our work shares this advantage, offering a novel perspective on evaluating MLLM decision-making in interactive tasks. However, unlike RL4VLM, which focuses on poker-like tasks that are primarily text-based, our proposed task directly involves fine-grained visual perception, laying the groundwork for further exploration of vision-centered, multi-step decision-making agents.
+
+# 2.3. Interactive Segmentation Tools
+
+The objective of interactive segmentation [8, 10, 13, 27, 42] is to segment regions of interest based on user-provided inputs, such as clicks, boxes, and scribbles. RITM [42] leverages COCO [3] and LVIS [12] as training data, achieving substantial segmentation quality. SimpleClick [27] was the first to explore the application of plain Vision Transformers (ViT) in interactive segmentation. InterFormer [13] decouples feature encoding and prompt fusion, effectively mitigating the high-latency issue observed in SimpleClick. SAM [15] adopts a similar design, trained on the large-scale SA1B dataset, and achieves notable generalization capabilities, supporting box inputs. However, existing interactive segmentation models, while capable of generating accurate masks, require human input. In contrast, our work aims to teach MLLMs to perform segmentation by imitating human annotators using interactive segmentation tools.
+
+# 3. Preliminaries and Task Formulation
+
+Given an image  $I$  and a text prompt  $P$ , a professional human annotator is able to accurately draw the mask  $M$  corresponding to the object indicated by the text prompt  $P$ . However, drawing the mask  $M$  directly is time-intensive. Modern annotators commonly utilize an interactive segmentation tool, iteratively providing a series of positive and negative click points based on the current image  $I$  and mask  $M$ , making it easier to create a high-quality mask. This entire process can be modeled as a Markov Decision Process (MDP) consisting of  $(S, A, T, R, \gamma)$ , where:
+
+- State  $s_t \in S$ : Represents the current status of the mask
+
+$M_{t}$  in the segmentation process, along with the history of all previous actions  $a_{0:t - 1}$ , i.e.,  $s_t = (M_t,a_{0:t - 1})$ . The initial state  $s_0$  corresponds to an empty mask, while the goal state  $s_{\mathrm{goal}}$  corresponds to the optimal target mask  $M_{\mathrm{target}}$ .
+
+- Action  $a_{t} \in \mathcal{A}$ : Here, each action  $a_{t}$  refers to an annotator's click operation, consisting of two components: an attribute  $\alpha_{t}$  and a coordinate  $\mathbf{c}_{t}$ . The attribute  $\alpha_{t} \in \{+1, -1\}$  indicates whether the point is a positive or negative click, while the coordinate  $\mathbf{c}_{t} \in [0,1]^{2}$  represents the relative position of the point within the image, scaled to the [0, 1] range for both  $x$  and  $y$  axes.
+
+- Transition Function  $T: \mathcal{S} \times \mathcal{A} \rightarrow \mathcal{S}$ : Represents the environment's response to the current state-action pair  $(s_t, a_t)$ , mapping it to the next state  $s_{t+1}$ . In this task, the environment's response is determined by the input image  $I$  and the interactive segmentation network  $F_{iter}$ . Specifically, the next mask  $M_{t+1}$  is generated by  $F_{iter}(I, s_t, a_t)$ , with  $s_{t+1} = (M_{t+1}, a_{0:t})$ .
+
+- Reward Function  $R: \mathcal{S} \to \mathbb{R}$ : The reward function evaluates the accuracy of the current mask  $M_t$  relative to the target mask  $M_{\text{target}}$  using the Intersection over Union (IoU) metric. Specifically,  $R(s_t) = \mathrm{IoU}(M_t, M_{\text{target}})$ , where a higher IoU score indicates a better match between  $M_t$  and  $M_{\text{target}}$ .
+
+Since we are focused solely on the quality of the final mask, we omit the discount factor  $\gamma$  in our setup. Given that the action space can be fully represented by text, and the state space can be fully represented by images, a competent MLLM should be able to perform this task as effectively as a human annotator. Our objective is to train a policy  $\pi_{\theta}(a_t|s_t,I,P)$  for the MLLM that, given an image  $I$  and a text prompt  $P$ , outputs an action  $a_{t}$  at each time step  $t$ . The goal is to maximize the expected reward  $R(s_{T})$  within a limited number of steps  $T$ . We refer to this as the Human-Like Mask Annotation Task (HLMAT). In practice, to enable the MLLM to support more flexible use cases—such as mask refinement—we remove the dependency on  $a_{0:t-1}$  in  $s_{t}$ , allowing the model to determine the next action based solely on the current mask  $M_{t}$ .
+
+# 4. SegAgent
+
+As mentioned in Sec. 3, our goal is to train an MLLM to learn a policy  $\pi_{\theta}(a_t|s_t,I,P)$  that imitates the annotator's trajectory. In practice, to enable the MLLM to support more flexible use cases—such as mask refinement—we remove the dependency on  $a_{0:t-1}$  in  $s_t$ , allowing the model to determine the next action based solely on the current mask  $M_t$ , so the policy becomes  $\pi_{\theta}(a_t|M_t,I,P)$ .
+
+As illustrated in Fig. 1, the current mask  $M_{t}$  and the original image  $I$  are processed as visual inputs to the MLLM. Following common visualization techniques used by human annotators, we overlay the current segmentation result  $M_{t}$
+
+![](images/24c161d13ca27450c1c9d4d8ef95c8d4bfaa5852ebe07a81458301e3f6294238.jpg)  
+Figure 1. The overall framework of SegAgent. The image below shows a complete set of trajectories. We visualize current action  $a_{t}$  and the resulting mask  $M_{t + 1}$  in one image.
+
+as a semi-transparent green mask on the original image  $I$  to create a new composite image  $I_{t}$ . This image  $I_{t}$  serves as the final input for the MLLM, allowing the model to simultaneously perceive information about  $M_{t}$  and  $I$  in a single frame.
+
+The text input  $P$  to the MLLM includes two key components: (1) a description of the task and action space, and (2) a description of the target object to be segmented. This prompt design provides the model with context about the overall objective and the segmentation target, ensuring it can interpret both the visual input and the segmentation goal. Detailed prompt design can be found in the appendix.
+
+After processing and analyzing the inputs, the MLLM outputs the next action  $a_{t+1}$  in text format, such as "Positive point: (175,483)." The action is then converted into a format compatible with the interactive segmentation model via a predefined post-processing function. Subsequently, all previous actions  $a_{0:t}$ , the current mask  $M_t$ , and the original image  $I$  are fed into the interactive segmentation model  $F_{iter}$ , which generates the updated mask  $M_{t+1}$ . This iterative process continues until the segmentation achieves a satisfactory result.
+
+The remainder of this section is organized as follows: First, we describe our approach for collecting human-like annotation trajectories (Sec. 4.1). Next, we analyze the two essential capabilities that a robust SegAgent should possess (Sec. 4.2). Inspired by traditional RL-style policy improvement methods, we then adapt the StaR method to our task (Sec. 4.3). Finally, we combine the process reward model with tree search to enhance the model's robustness in complex scenarios (Sec. 4.4).
+
+# 4.1. Human Annotator Trajectories Generation
+
+Suppose we have an annotated dataset  $D_{seg} = \{(I, M_{target}, P)\}$ , where each sample includes an image  $I$ , a target mask  $M_{target}$ , and a text prompt  $P$ . Ideally, recording all state-action pairs  $(s_t, a_t)$  in real-time during the human annotation process would yield the de
+
+![](images/c6d0af4aa8060dd1a4aecf3e0afa4b57ea3eabb72441a46a70ef894d0d16d99e.jpg)  
+Figure 2. An example of generated trajectory. We visualize current action  $a_{t}$  and the resulting mask  $M_{t + 1}$  in one image. Due to the noise from GT Mask, the action for Iteration 3,4 is meaningless
+
+sired trajectory  $[s_0, a_0, s_1, a_1, \dots, s_T, a_T]$ . However, existing datasets lack such trajectories, and re-hiring human annotators to annotate the data while recording trajectories would be very costly. Thus, we consider whether it is possible to derive the trajectory dataset  $D_{traj} = \{(I, M_{target}, P, [s_0, a_0, s_1, a_1, \dots, s_T, a_T])\}$  from the existing dataset  $D_{seg} = \{(I, M_{target}, P)\}$  using a rule-based method. Thanks to researchers in interactive segmentation, an iterative click simulation strategy has been developed to automate the evaluation of interactive segmentation models. This strategy can be understood as a function  $F_{sim}$ , which takes as input the current mask  $M_t$  and the ground truth mask  $M_{target}$ , and outputs the next action  $a_{t+1}$ , i.e.,  $a_{t+1} = F_{sim}(M_t, M_{target})$ . Specifically, this function computes the false positive and false negative regions between the current mask  $M_t$  and the ground truth mask  $M_{target}$ , placing the next click action at the center of the error region based on the size and position of these regions. With the help of function  $F_{sim}(\ref{eq:2})$ , we can simulate trajectories  $[s_0, a_0, s_1, a_1, \dots, s_T, a_T]$  based on  $M_{target}$ . However, the annotation quality of  $M_{target}$  may vary, potentially generating suboptimal trajectories. This issue is especially pronounced toward the end of a trajectory (see Fig. 2), where noise in  $M_{target}$  can cause erroneous actions that misguide the MLLM during training. To address this, we introduce the following steps during generation:
+
+- Limit Maximum Trajectory Length: We set a maximum trajectory length  $T$ . For lower-quality datasets, we reduce  $T$  to avoid excessive noisy actions at the trajectory's end.  
+- Terminate on Satisfactory Reward: We calculate the reward  $R(s_{t})$  for the current state  $s_{t}$ , and if  $R(s_{t})$  reaches a threshold  $\tau_{stop}$ , we terminate the trajectory generation.  
+- Discard Low-Impact Actions: We evaluate the reward difference before and after executing an action  $a_{t}$ . If the reward difference  $R(s_{t + 1}) - R(s_{t}) < \tau_{diff}$ , we discard action  $a_{t}$ . This is because abnormal actions can cause the interactive segmentation model to collapse, significantly reducing the quality of the resulting mask  $M_{t + 1}$ .
+
+To accommodate segmentation datasets of varying quality, we can adjust these three parameters to reduce noise in the generated trajectories. Following this process, we can generate the annotation trajectory dataset  $D_{traj}$  from the existing segmentation dataset  $D_{seg}$  and use these data to fine-tune the MLLM through instruction tuning, resulting in our SegAgent.
+
+# 4.2. What Makes a Good SegAgent?
+
+Now that we have obtained the annotation trajectory dataset  $D_{traj}$ , in theory, we can fine-tune any MLLM to become a SegAgent. Here, we identify that HLMAT primarily relates to two main competencies of an MLLM:
+
+- Coarse-Grained Localization Ability Based on Text: This capability is primarily demonstrated when the MLLM executes the first or initial actions, where it must use the text prompt and global image context to locate the approximate position of the object. This is especially critical for complex or ambiguous prompts, such as "the second black motorcycle on the right." If the model fails in this initial localization, the final IoU score will likely be very low. This skill is closely related to tasks commonly known as Visual Grounding or Referring Expression Comprehension (REC), where previous work [2, 46] has shown that many different architectures of MLLMs can achieve good performance when trained on sufficient data. The only difference in our setup is that while other tasks typically output bounding box coordinates, our task supports point-based coordinates. Our experiments further reveal that the initial action can be represented as either a box or a point. However, this localization ability itself is not the primary focus of our work, as it has already been widely studied and optimized.
+
+- Fine-Grained Pixel Understanding and Mask Refinement: This capability is demonstrated when the MLLM performs subsequent actions, where it must accurately adjust the mask boundaries based on the current mask and local image information to gradually improve mask quality. This process requires the model to have an exceptionally detailed perception and understanding of both the input image and mask, as well as the ability to make precise localization decisions during output. Few studies have focused on this fine-grained pixel understanding of MLLMs, making this a key focus of our research.
+
+To better investigate the second capability, a complex and high-quality segmentation dataset is essential. Here, complexity refers to the requirement that the dataset should necessitate relatively long annotation trajectories to reach the predefined threshold  $\tau_{stop}$ . High quality refers to minimal noise in annotations, ensuring accuracy at the trajectory endpoints. Our analysis of the commonly used Refcoco dataset revealed that it lacks the necessary complexity and annotation quality (see Sec. 5.2). Consequently, we select two alternative datasets that better meet these criteria: DIS5K [37] and ThinObject5K [21]. Our further exploration will primarily focus on these datasets.
+
+# 4.3. Policy Improment
+
+Training the MLLM with the annotation trajectory dataset  $D_{traj}$  generated in Sec. 4.1 equips the model with foundational capabilities for the HLMAT task. However, because
+
+Algorithm 1 SegAgent Policy Improvement with StaR+  
+1: Input: SegAgent model trained generated trajectory dataset  $D_{\mathrm{traj}} = \{(I^i,M_i^i,\mathcal{T}^i)\}_{i = 1}^m$    
+2:  $S_0\gets \mathrm{SegAgent}$  // Initialize SegAgent model   
+3:  $D_0\gets D_{\mathrm{traj}}$  // Initialize trajectory dataset   
+4: for  $n = 1$  to  $N$  do   
+5:  $\hat{\mathcal{T}}^i\gets S_{n - 1}(I^i,M_i^i,\mathcal{T}^i)$  for all  $i\in [1,m]$  // Generate trajectories   
+6:  $D_{n}\gets \{(I^{i},M_{\mathrm{target}}^{i},P^{i},\mathrm{Refine}(\hat{\mathcal{T}}^{i}))\}_{i = 1}^{m}$  //Filter and correct trajectories based on reward function and  $F_{sim}$    
+7:  $S_{n}\gets \mathrm{train}(S_{0},D_{0}\cup D_{n})$  // Fine-tune on combined dataset   
+8: end for
+
+these generated trajectories are nearly optimal and lack diversity, the model's generalization and robustness remain constrained. It is worth noting that our framework can directly interact with the environment, allowing the MLLM to engage in online learning or offline reinforcement learning (RL) to continually refine its policy through interaction. In this paper, we choose to implement and explore a straightforward RL-style policy improvement method, specifically the StaR [62] algorithm.
+
+As shown in Alg. 1, we start with a trained SegAgent model  $S_0$  and the annotation trajectory dataset  $D_{traj}$ . In each iteration, we first use  $S_{n-1}$  to perform rollouts on the training images, generating a set of new trajectories  $\hat{T}^i$ . Next, we filter and refine these trajectories based on the reward function and  $F_{\mathrm{sim}}$ , producing an updated trajectory dataset  $D_n$ . Finally, we merge  $D_n$  with  $D_{traj}$  and fine-tune the model on this combined dataset to obtain an improved SegAgent model  $S_n$ . Unlike the original StaR algorithm (see comparison in Appendix), which filters based on the overall correctness of the trajectory, our StaR+ method calculates the reward change for each individual action. We retain actions that increase the reward, while for actions that decrease the reward, we replace them with actions generated by  $F_{\mathrm{sim}}$  as corrections.
+
+# 4.4. Process Reward Model and Tree Search
+
+Process-supervised Reward Models [22] (PRM) refer to the techniques where a reward model is trained to evaluate the reward at each step of a multi-step task. PRM can guide the training process by integrating with approaches like RLHF [34] or reject sampling [60]. Previous work [30] has also shown that PRM can be used as guidance during the inference stage to enhance the model's search and reasoning capabilities.
+
+In our work, since the reward at each step can be directly obtained from the environment during training, PRM is un
+
+Algorithm 2 PRM with Heuristic Greedy Search  
+1: Input: SegAgent model  $S$ , image  $I$ , text prompt  $P$   
+2:  $M_0 \gets$  initialize_mask(I) // Initialize the mask  
+3:  $R_0 \gets \mathrm{PRM}(S, I, M_0, P)$  // Predict the initial reward  
+4: for  $t = 1$  to  $T$  do  
+5:  $[a^1, a^2, \dots, a^K] \gets \text{get\_candidate}(S, I, M_{t-1}, P)$  // Get  $K$  candidate actions  
+6: for  $i = 1$  to  $K$  do  
+7:  $M^i \gets F_{iter}(I, M_{t-1}, a^i)$   
+8:  $R^i \gets \mathrm{PRM}(S, I, M^i, P)$   
+9: end for  
+10: best_index  $\leftarrow \operatorname{argmax}(R^1, R^2, \dots, R^K)$  // Get the best action guided by PRM  
+11:  $M_t \gets M^{\text{best\_index}}$  // Predict the next mask  
+12:  $R_t \gets R^{\text{best\_index}}$  // Predict the next reward  
+13: end for  
+14: best_index  $\leftarrow \operatorname{argmax}(R_0, R_1, \dots, R_T)$  // Get the best step  
+15: return  $M_{\text{best\_index}}$
+
+necessary for the training phase. Therefore, we primarily explore the application of PRM techniques during the inference stage. There are two main reasons motivating our use of PRM techniques:
+
+- Providing a Stop Signal: We use PRM to provide a stop signal for the task. An alternative approach would be to add a stop action to the action space, but this would increase the complexity of the action space, thereby increasing the difficulty of model training. This is especially challenging when  $M_{\text{target}}$  contains noise, as it becomes difficult to determine the exact stopping point, making it hard for the model to generate an accurate stop action. By directly predicting the reward, PRM allows the model to decide when to stop without adding complexity to the action space.  
+- Enhancing Robustness in Complex Scenarios: In complex scenarios, since the model cannot backtrack or undo actions, executing an inaccurate or incorrect action at any step can adversely affect subsequent actions, leading to lower quality in the final result. With the help of PRM, we can combine the model with advanced search strategies, such as Breadth-First Search (BFS) and Monte Carlo Tree Search (MCTS), to improve the model's robustness in complex situations.
+
+We implement PRM by asking the MLLM perform an additional text prediction, where the MLLM learns to predict the mIoU score of the current state before generating the next action, for example, "Current mIoU: 0". Based on this, we design a simple heuristic greedy search strategy, as shown in Alg. 2. At each step, we first use Multino
+
+mial Sampling to generate  $K$  candidate actions. For each action, we perform one interaction with the segmentation model to obtain a new mask and calculate the reward using PRM. Finally, we select the action with the highest reward as the next action. This process continues until the maximum number of steps  $T$  is reached or until the reward converges. We then return the mask with the highest reward as the final output. A more detailed illustration can be found in the appendix. Note that, in the algorithm description, we omit the historical actions included in the input of  $F_{iter}$  for simplicity. It is worth noting that more complex search strategies, such as BFS and MCTS, are also feasible. However, there exists a trade-off between computational complexity and performance. Our experiments demonstrate that the simplest and most efficient heuristic greedy search strategy already provides significant performance improvements. We leave the exploration of more complex search strategies for future work.
+
+# 5. Experiments
+
+# 5.1. Evaluation Protocol
+
+As mentioned earlier, HLMAT serves as a novel evaluation protocol for assessing and advancing MLLMs' fine-grained pixel understanding capabilities. It also provides a framework to explore MLLMs as vision-centered, multi-step decision-making agents. We select two representative MLLMs as initialization models for SegAgent: a linear projector-based model, LLaVA-v1.5-7B [24, 25], and a stronger Q-former-based model, Qwen-VL-7B. We fine-tune each MLLM on the generated trajectory dataset for 2 epochs, keeping the image encoder frozen while allowing the LLM and projector layers to update. We use DeepSpeed's Zero2 mode for multi-GPU training, with all training experiments conducted on 8 NVIDIA 80GB GPUs.
+
+Other training parameters follow the default settings provided by the official repositories; details can be found in the Appendix. We used two main interactive segmentation models for this task: SimpleClick [27] and SAM [15]. SimpleClick supports point input only, while SAM also supports box input. During trajectory generation, we created trajectories for both segmentation models. As discussed in Sec. 4.2, SegAgent's first action can be either a point or a box. Our preliminary experiments demonstrated that SAM achieves better performance when provided with an additional box input. Therefore, unless otherwise specified, we include box input when using SAM in subsequent experiments.
+
+We conduct experiments in two settings: the widely adopted Referring Expression Segmentation (RES) and our newly proposed High-quality Referring Expression Segmentation (HRES).
+
+For inference evaluation, we set a fixed maximum num
+
+Table 1. Comparison of methods on RES dataset. We indicate which models use SAM. "SClick" denotes the use of SimpleClick as the interactive segmentation model.  
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Venue</td><td colspan="3">refCOCO</td><td colspan="3">refCOCO+</td><td colspan="2">refCOCOg</td></tr><tr><td>val</td><td>testA</td><td>testB</td><td>val</td><td>testA</td><td>testB</td><td>val(U)</td><td>test(U)</td></tr><tr><td colspan="10">traditional methods</td></tr><tr><td>MAttNet [59]</td><td>CVPR&#x27;18</td><td>56.51</td><td>62.37</td><td>51.70</td><td>46.67</td><td>52.39</td><td>40.08</td><td>47.64</td><td>48.61</td></tr><tr><td>LAVT [51]</td><td>CVPR&#x27;22</td><td>72.7</td><td>75.8</td><td>68.8</td><td>62.1</td><td>68.4</td><td>55.1</td><td>61.2</td><td>62.1</td></tr><tr><td>CRIS [47]</td><td>CVPR&#x27;22</td><td>70.5</td><td>73.2</td><td>66.1</td><td>65.3</td><td>68.1</td><td>53.7</td><td>59.9</td><td>60.4</td></tr><tr><td>PolyFormer-L [26]</td><td>CVPR&#x27;23</td><td>76.94</td><td>78.49</td><td>74.83</td><td>72.15</td><td>75.71</td><td>66.73</td><td>71.15</td><td>71.17</td></tr><tr><td>X-Decoder [73]</td><td>CVPR&#x27;23</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>64.6</td><td>-</td></tr><tr><td>SEEM [74]</td><td>NeurIPS&#x27;23</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>65.7</td><td>-</td></tr><tr><td colspan="10">LLM based methods</td></tr><tr><td>LISA(SAM) [16]</td><td>CVPR&#x27;24</td><td>74.9</td><td>79.1</td><td>72.3</td><td>65.1</td><td>70.8</td><td>58.1</td><td>67.9</td><td>70.6</td></tr><tr><td>PixelLM [39]</td><td>CVPR&#x27;24</td><td>73.0</td><td>76.5</td><td>68.2</td><td>66.3</td><td>71.7</td><td>58.3</td><td>69.3</td><td>70.5</td></tr><tr><td>PerceptionGPT [35]</td><td>CVPR&#x27;24</td><td>75.1</td><td>78.6</td><td>71.7</td><td>68.5</td><td>73.9</td><td>61.3</td><td>70.3</td><td>71.7</td></tr><tr><td>GSVA(SAM) [49]</td><td>CVPR&#x27;24</td><td>77.2</td><td>78.9</td><td>73.5</td><td>65.9</td><td>69.6</td><td>59.8</td><td>72.7</td><td>73.3</td></tr><tr><td>SAM4MLLM(Qwen) [9]</td><td>ECCV&#x27;24</td><td>77.1</td><td>80.9</td><td>72.5</td><td>71.5</td><td>76.8</td><td>64.7</td><td>74.5</td><td>75.2</td></tr><tr><td>Qwen box [2] + SAM</td><td>-</td><td>71.79</td><td>75.20</td><td>67.29</td><td>65.39</td><td>71.62</td><td>59.12</td><td>66.93</td><td>68.94</td></tr><tr><td colspan="10">Our methods</td></tr><tr><td>SegAgent-LLaVA+SAM</td><td></td><td>79.20</td><td>81.44</td><td>75.72</td><td>71.53</td><td>76.68</td><td>65.44</td><td>74.80</td><td>74.90</td></tr><tr><td>SegAgent-Qwen+SAM</td><td></td><td>78.01</td><td>80.34</td><td>74.98</td><td>70.86</td><td>75.52</td><td>65.75</td><td>74.49</td><td>74.62</td></tr><tr><td>SegAgent-LLaVA+SClick</td><td>this work</td><td>77.81</td><td>80.03</td><td>74.12</td><td>66.73</td><td>71.16</td><td>59.89</td><td>70.45</td><td>71.25</td></tr><tr><td>SegAgent-Qwen+SClick</td><td></td><td>79.69</td><td>81.35</td><td>76.57</td><td>72.49</td><td>75.80</td><td>66.89</td><td>75.11</td><td>75.20</td></tr></table>
+
+ber of steps  $T$  and, following LISA [16], used cumulative IoU (cIoU) as the evaluation metric. For RES, due to its relatively simple masks, we set  $T = 7$  and  $K = 1$  in Alg. 2. For HRES, with more complex segmentation requirements, we set  $T = 11$  and  $K = 3$ .
+
+# 5.2. Datasets
+
+We first conduct a fair comparison between our method and other state-of-the-art (SOTA) methods on the Referring Expression Segmentation (RES) dataset to demonstrate the effectiveness of our approach. Subsequently, we extend our method to more complex scenarios by experimenting on the proposed High-quality Referring Expression Segmentation (HRES) dataset. We begin by introducing the basic information of these two datasets and analyzing their characteristics to highlight the necessity of using the HRES dataset.
+
+Referring Expression Segmentation (RES): The RES dataset is composed of three subsets: refCOCO [58], refCOCO+ [58], and refCOCOg [31]. All three datasets are based on images from the COCO dataset, with differences in the types of referring expressions provided. RefCOCO is the earliest dataset, featuring relatively simple descriptions. Compared to RefCOCO, RefCOCO+ prohibits annotators from using location-based descriptions, making the descriptions more focused on object appearance and attributes. RefCOCOg includes longer descriptions with more detailed information. However, the mask quality in refCOCO(+/g) is relatively low, with many masks containing significant noise. This makes it challenging to generate long and precise annotation trajectories. On the other hand, the complexity of the datasets does not meet the requirements for
+
+![](images/01b01f9f32ba8e4bd6bee0f69d3c519bbea38cd639967d63af561b422312a37d.jpg)  
+Figure 3. Comparison of dataset complexity.
+
+the multi-step iteration in HLMAT. In most cases, only 1-2 steps are sufficient to complete the task.
+
+To address these limitations, we introduce a new dataset, the High-quality Referring Expression Segmentation (HRES) dataset, designed to facilitate further exploration and better assess the performance of MLLMs under more complex conditions.
+
+High-quality Referring Expression Segmentation (HRES): Our proposed HRES dataset features higher annotation quality, with more detailed and complex masks, which require additional steps to complete. The data in HRES is derived from the HQSeg-44K dataset proposed by HQ-SAM [14]. We selected two subsets, DIS5K [37] and ThinObject5K [21], as they contain texts or category descriptions with minimal ambiguity. The DIS5K (Dichotomous Image Segmentation) subset consists of high-resolution images and high-quality binary segmentation masks, specifically designed to handle challenging segmentation tasks. It includes 5,470 images spanning 22 groups and 225 fine-grained object categories. The ThinObject5K
+
+subset focuses on objects with thin, elongated structures, such as insect legs and bicycle spokes, which are typically challenging for segmentation models.
+
+As shown in Fig. 3, we use the number of clicks (required to reach the target IoU) as a measure of dataset complexity. The HRES dataset is significantly more complex than the RES dataset, requiring more steps to achieve the target IoU.
+
+# 5.3. Main Results
+
+In this section, we present the main results of SegAgent on the HLMAT task, comparing it with other state-of-the-art (SOTA) methods on RES datasets in Tab. 1. We compare SegAgent with both traditional and LLM-based methods. Our results demonstrate that SegAgent achieves competitive IoU scores, particularly SegAgent-LLaVA+SAM and SegAgent-Qwen+SClick, which outperforms all other methods except for GLaMM.
+
+We also implement a simple baseline method, Qwen box + SAM, by leveraging Qwen-VL's grounding capability to obtain a bounding box, which is then passed to SAM to generate the mask. For fair comparison, our SegAgent-LLaVA(Qwen) + SAM also uses the same box as the initial action to evaluate SegAgent's ability to refine the mask. Results show that SegAgent-LLaVA(Qwen) + SAM outperforms the Qwen box + SAM baseline across all datasets, indicating that our approach effectively refines the mask.
+
+An interesting observation is that although Qwen is trained on a larger dataset and generally outperforms LLaVA on standard vision-language tasks, LLaVA surpasses Qwen in mask refinement capability. We attribute this to Qwen's reliance on the Q-former structure, which appears to struggle with pixel-level tasks. Recent studies [5, 52] have reported similar limitations, suggesting that the vanilla QFormer might lose spatial locality during semantic abstraction. We envision HLMAT as a future protocol for assessing MLLM's fine-grained pixel-level visual abilities, inspiring further exploration into improving the pixel-level understanding abilities of these models.
+
+Additionally, when using SimpleClick as the interactive segmentation model, we observe that SegAgent-Qwen significantly outperforms SegAgent-LLaVA. This aligns with the first capability discussed in Sec. 4.2, as it plays a more dominant role here. Qwen's pretraining on large-scale visual grounding tasks gives it an advantage in locating the target during the initial interactions, leading to better overall performance.
+
+# 5.4. Exploration on More Complex Scenarios
+
+As discussed in Sec. 5.2, we validate our proposed PolicyImprovement strategy, as well as the effectiveness of PRM and Tree Search, on a more complex and accurate dataset. Here, we take the model fine-tuned on generated trajectories
+
+![](images/b398af5bb3872cf07ece9f048a058bcff88cea84cf5c59628d9c30d7ca76b45d.jpg)  
+Figure 4. Comparison of different strategies on the HRES dataset with SFT (Supervised Fine-Tuning) and apply a fixed-step greedy decoding during testing as our baseline.
+
+Policy-Improvement Strategies: As mentioned in Alg. 1, we use the baseline model trained on generated trajectories, and then conduct policy improvement using  $\mathrm{StaR + }$  to enhance the model's performance. Although we only use a single round of  $\mathrm{StaR + }$ , we find that this strategy significantly improves the model's performance. The improvement is particularly pronounced on the ThinObject5K dataset, where our model achieves an IoU of 86.57 compared to the baseline IoU of 71.45.
+
+PRM and Tree Search: As shown in the figure, introducing PRM as a stopping signal improves model performance compared to the baseline, which relies on fixed-step greedy decoding. Specifically, with PRM alone (equivalent to setting  $K = 1$ ), the model performance on DIS increases by 2.36 and on ThinObject by 4.09. Furthermore, when we set  $K = 3$  and apply our proposed Tree Search algorithm, the model performance is further enhanced, achieving an additional increase of 7.43 on DIS and 10.59 on ThinObject compared to using only PRM. These results align with our hypothesis that Tree Search effectively prevents the model from getting trapped in local optima when making uncertain actions, thus boosting performance in complex scenarios.
+
+# 6. Conclusion
+
+In this work, we introduced the Human-Like Mask Annotation Task (HLMAT), a novel segmentation paradigm that models MLLMs as multi-step decision-making agents, imitating human annotation paths in interactive segmentation. Through HLMAT, we assess and enhance MLLMs' pixel-level understanding without requiring additional model structures. We developed SegAgent by fine-tuning MLLMs on human-like trajectories and demonstrated its competitive performance across various segmentation tasks, including mask refinement and annotation filtering. Additionally, we introduced the high-quality HRES dataset and showed that decision-enhancement techniques like  $\mathrm{StaR + }$  and tree search with PRM can further boost performance. Our study paves the way for future exploration of vision-centered, multi-step decision-making with MLLMs.
+
+# Acknowledgement
+
+This work is in part supported by the National Key R&D Program of China(No. 2022ZD0160101) and the National Natural Science Foundation of China (No. 62206244). This work is also supported by Ant Group Research Intern Program.
+
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+# SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images
+
+Kaiyu Li $^{1}$ , Ruixun Liu $^{2}$ , Xiangyong Cao $^{2,4\dagger}$ , Xueru Bai $^{6}$ , Feng Zhou $^{6}$ , Deyu Meng $^{3,4,5}$ , Zhi Wang $^{1}$ $^{1}$ School of Software Engineering, Xi'an Jiaotong University, Xi'an, 710049, China  
+ $^{2}$ School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China  
+ $^{3}$ School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China  
+ $^{4}$ Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, 710049, China  
+ $^{5}$ Pengcheng Laboratory  $^{6}$ Xidian University
+
+![](images/4c12ca0ce3e074ee06c160cf061cc53a4e212b25043051a242e4ba904ef576be.jpg)
+
+![](images/c77a0d8576e6add75545b3ca3562e0995a2f17372a4c43748c9e1cc827212b1a.jpg)  
+Figure 1. Visualization and performance of SegEarth-OV on open-vocabulary semantic segmentation of remote sensing images. We evaluate our SegEarth-OV on 17 remote sensing datasets (including semantic segmentation, building extraction, road extraction, and flood detection tasks), and our SegEarth-OV consistently generates high-quality segmentation masks.
+
+![](images/e51ddbbbc291eb43ee21a2584514a771b5990a78ac0eea8f82e577dab1b1ceee.jpg)
+
+![](images/d1fc99ced470f990d402b4ddc264696fc14422ce6fbe7b8931c2b5f33e681397.jpg)
+
+![](images/0d42cf80436a41f84bc4042d7c2ad7b123080c20f1b4ee1c148dd0a559b8e736.jpg)
+
+![](images/d63e08da74277d2dac19fe7e87f6ddcecec2d965dc0098dc304d885438318ae5.jpg)
+
+![](images/313e2abca4e659a814db0a53c9bb5e5628a511af14744d3aeca9c59421fde1fe.jpg)
+
+![](images/9dcfa7ebc4aaa75ce3f725bdfc31778a3fc032fa31b49e9bbb52f132fdab51f5.jpg)
+
+![](images/78adede973ff9d1db6ca437a2b1e9932a4017b41c1f31c8bb8a2fa5a57d12e5d.jpg)
+
+# Abstract
+
+Current remote sensing semantic segmentation methods are mostly built on the close-set assumption, meaning that the model can only recognize pre-defined categories that exist in the training set. However, in practical Earth observation, there are countless new categories, and manual annotation is impractical. To address this challenge, we first attempt to introduce training-free<sup>1</sup> open-vocabulary semantic segmentation (OVSS) into the remote sensing context. However, due to the sensitivity of remote sensing images to low-resolution features, distorted target shapes and ill-fitting boundaries are exhibited in the prediction mask. To tackle these issues, we propose a simple and universal upsampler, i.e. SimFeatUp, to restore lost spatial information of deep features. Specifically, SimFeatUp only needs to learn from a few unlabeled images, and can upsample arbitrary remote sensing image features. Furthermore, based on the observation of the abnormal response
+
+of patch tokens to the [CLS] token in CLIP, we propose to execute a simple subtraction operation to alleviate the global bias in patch tokens. Extensive experiments are conducted on 17 remote sensing datasets of 4 tasks, including semantic segmentation, building extraction, road detection, and flood detection. Our method achieves an average of  $5.8\%$ ,  $8.2\%$ ,  $4.0\%$ , and  $15.3\%$  improvement over state-of-the-art methods on the 4 tasks. Code is available at https://likyoo.github.io/SegEarth-0V.
+
+# 1. Introduction
+
+Remote sensing images have changed the way humans observe and understand the Earth. It enables us to monitor land cover/use types, respond effectively to natural disasters (e.g., fires, earthquakes, floods), gain insight into food and water resources, etc. Among the 17 Sustainable Development Goals (SDGs) $^{2}$  issued by the United Nations, remote sensing images can provide important data support for several goals including "Zero Hunger", "Clean Water
+
+![](images/566b62fbaa0f0f3901a9c958d2403f3748168d1ae40f4e60b0cffcb82dd6271b.jpg)  
+(a) MaskCLIP
+
+![](images/f9f58440daea50bc8b21815b0ea005c9aa46bf36c749d0423ca2f56af40e6c12.jpg)  
+(b) ClearCLIP  
+Figure 2. Limitations of state-of-the-art OVSS methods in remote sensing images. The two predictions on the left present distorted target shapes and ill-fitting boundaries (best viewed digitally with zoom, especially for the object edges).
+
+![](images/73428195efd77642028eb1a5d9d8ef0c1c7e411d9e05d13efee626e50b8005ba.jpg)  
+(c) SegEarth-OV
+
+![](images/9c49065108bb666ec57d7eb8fe6890c8e6164a3de62094108d072620293e47d8.jpg)  
+(d) GT
+
+and Sanitation", "Industry, Innovation and Infrastructure", "Climate Action", "Life on Land", etc. [60]. Notably, remote sensing data can be considered as a distinct modality in computer vision. It involves more diverse spatial resolutions (from centimeters to kilometers), temporal dimensions (from hours to decades), and object perspectives (overhead and oriented) than natural images. Therefore, solutions designed for other data modalities (e.g., natural images) may be sub-optimal for remote sensing data [53].
+
+Recently, raw remote sensing images have been available from various sources (e.g., Landsat, Sentinel), but obtaining large-scale annotated categories is still a challenge due to countless potential categories and expensive manual costs. In addition, on the broad surface of the earth, "stuff" (e.g., grassland, forest, etc.) occupies much more area than "things" (e.g. buildings, ships, etc.) [74]. Therefore, for remote sensing images, pixel-level perception, i.e., segmentation, is applied more frequently than instance-level perception, and the demand for pixel-level annotation exacerbates the difficulty of obtaining large-scale labels. OpenStreetMap [21] is a popular solution that aims to create a freely usable, editable, and shareable map of the world. However, the completeness of the annotations in OpenStreetMap is affected by regional income levels, resulting in limited data availability [22]. The rise of vision language model (VLM) brings us new inspirations with its capabilities of open-vocabulary semantic segmentation (OVSS). However, through some exploratory experiments, we find that the solution designed for natural images is sub-optimal on remote sensing images. A notable phenomenon is the presence of distorted target shapes and ill-fitting boundaries in the prediction mask, as shown in Fig. 2 (a)(b).
+
+Empirically, these issues can be largely attributed to the excessively low resolution of the features [19, 76]. In the current CLIP-based OVSS paradigm, the feature maps from CLIP [51] are downsampled to  $1/16th$  of the original image (ViT-B/16). Hence, in this paper, we propose a simple and general feature upsampler, i.e. SimFeatUp, which is trained with the goal of reconstructing content-invariant high-resolution (HR) features on a few unlabeled images and can upsample arbitrary remote sensing image features after training. Owing to this property of SimFeatUp, it can be used as a universal external unit for training-free
+
+OVSS framework. Further, CLIP is trained at the image level, it uses the [CLS] token as a representation of the entire image, and attaches global properties to the local token [48, 52, 62]. However, this global property biases local features against patch-level inference in OVSS. To effectively reduce global bias, we propose a simple subtraction operation of local patch features and global features. Extensive quantitative and qualitative experiments demonstrate the superior segmentation quality of our method over other state-of-the-art methods.
+
+In summary, our contributions are as follows.
+
+- We propose SimFeatUp, a general feature upsampler for training-free OVSS, which robustly upsamples low-resolution (LR) features and maintains semantic consistency with image content.  
+- We propose an extremely simple and straightforward way to alleviate the global bias problem of CLIP, i.e., executing subtraction operations of local and global tokens.  
+- Our proposed SegEarth-OV model can achieve state-of-the-art performance on 17 remote sensing datasets of 4 tasks, including semantic segmentation, building extraction, road extraction, and flood detection.
+
+# 2. Related Work
+
+Vision-Language Model. Recently, foundation models, especially visual language models, have energized the field of computer vision. One phenomenal advance is contrastive language-vision pretraining, i.e., CLIP [51], which elegantly bridges the gap between images and natural language. By training with massive data in a multimodal embedding space, CLIP gains strong transfer capabilities, achieving leaps in zero-shot learning and making OV learning possible [67]. Subsequently, related research has gradually emerged, from the data [10, 58, 70, 73], training [16, 35, 73] or model [33, 34] side. However, CLIP focuses only on global [CLS] tokens, and even though patch-level tokens can be generated, they are inevitably contaminated by global bias [48, 52, 62], which is detrimental to dense prediction. In addition, several remote sensing VLMs emerge, they adapt general VLMs to remote sensing contexts [37, 49, 65, 75] or mine the characteristics of remote sensing data [23, 50].
+
+Supervised semantic segmentation. Semantic segmentation aims to discriminate images at the pixel level. The prediction head (aka decoder), as an essential component of segmentation models, is able to upsample LR feature maps into HR predictions. Typical prediction heads contain upsampling operators (e.g., bilinear interpolation, JBU [29]) and HR encoder features (as guidance), e.g., UNet [55], UperNet [69], Semantic FPN [27], MaskFormer [9], etc. Some works [38, 41, 78] focus on dynamic, learnable upsampling operators that make this process content-aware. FeatUp [17] constructs a model-agnostic upsam
+
+pling scheme that uses multi-view consistency loss with deep analogies to NeRFs [45]. However, it only explores the condition with labels. Inspired by FeatUp and built on top of it, the SimFeatUp proposed in this work is able to significantly improve OVSS without any labels.
+
+Open-Vocabulary Semantic Segmentation. As VLMs have shown remarkable zero-shot inference in image classification [51], which naturally extends to semantic segmentation. They empower the segmentation pipeline to recognize seen and unseen categories, and users can segment almost any category in an image using prompt vocabulary [67, 79]. We divide current CLIP-based OVSS methods into two groups: training-required and training-free. The former allows models to be trained on some base classes in a supervised or weakly supervised manner. Typically, some works [18, 42, 48, 52] try to train a localization-aware CLIP which can naturally make dense predictions, while others [11, 15, 32, 39, 71, 72] select a subset of the CLIP's pre-trained parameters and/or introduce a limited number of trainable parameters into the frozen CLIP, i.e., fine-tuning the CLIP to adapt to dense prediction on base classes. Still, training-free OVSS methods emphasize tapping into CLIP's inherent localization capabilities with limited surgery of features or structures. MaskCLIP [77] pioneers the removal of query and key projections at the attention pooling layer of CLIP's image encoder. Following it, subsequent studies [3, 30, 36, 62] adequately explore self-self attention (i.e.,  $q$ - $q$ ,  $k$ - $k$  or  $\nu$ - $\nu$  self-attention), and these modifications somewhat mitigate noisy activations and spatial invariant perception of CLIP. Another stream [2, 25, 56, 59] is the two-stage method, which first generates category-agnostic mask proposals and then classifies the masks. Besides, some other foundation models (e.g. SAM [28], Stable Diffusion [54]) can be introduced to enhance the localization ability of CLIP, and these explorations also make sense [2, 31, 64].
+
+Different from previous methods, we focus on the inherent characteristics of remote sensing images rather than the general attributes of natural images. The only contemporaneous work is [6], but it is training-required, like [11, 72]. Our SimFeatUp component, although it needs to be trained on a few images-only data beforehand, this process is independent of the semantic segmentation process and the trained weights can be used for almost any remote sensing data (like the foundation model in other works [2, 31]), so our method can still be seen as a training-free method.
+
+# 3. Preliminaries
+
+# 3.1. CLIP
+
+In ViT-based CLIP, the image encoder consists of a series of Transformer blocks. Let  $X = [x_{cls}, x_1, \dots, x_{h \times w}]^\top \in \mathbb{R}^{(hw + 1, d)}$  denotes the input of the last block, where  $h$  and  $w$  denote the height and width of the feature map,  $d$  denotes
+
+![](images/08b9ab25b328affae114290a784a9d0bef74755a1bf443b56ee799da637cff98.jpg)  
+(a)
+
+![](images/3924625ea5d7e36cda908b93a0785d7869857ad6512c90d08a1de223ed52d189.jpg)  
+(b)  
+Figure 3. Illustration of the proposed method. (a) is the training process of SimFeatUp. CLIP is frozen and only SimFeatUp is useful in reasoning. (b) is the reasoning process of SegEarth-OV. The LR feature maps from CLIP are upsampled by SimFeatUp and then the [CLS] token is subtracted to alleviate global bias. For better presentation, the color renderings follow [17].
+
+the dimension of tokens, and  $x_{cls}$  is a learnable global token and the others are local tokens from different image patches. The forward process of the last Transformer block can be formulated as follows:
+
+$$
+\begin{array}{l} \boldsymbol {q} = \operatorname {E m b} _ {q} (X), \boldsymbol {k} = \operatorname {E m b} _ {k} (X), \boldsymbol {v} = \operatorname {E m b} _ {v} (X), \\ \boldsymbol {y} = X + \operatorname {S A} (\boldsymbol {q}, \boldsymbol {k}, \boldsymbol {v}), \tag {1} \\ \boldsymbol {z} = \boldsymbol {y} + \operatorname {F F N} (\ln (\boldsymbol {y})) \\ \end{array}
+$$
+
+where  $q, k, v$  denote Query, Key, and Value, respectively. Emb consists of a layer normalization (LN) layer and a linear layer, and FFN denotes the feed-forward network. SA denotes a standard self-attention module, i.e.,  $\mathrm{SA}(\pmb{q},\pmb{k},\pmb{v}) = \mathrm{softmax}\left(\frac{\pmb{q}\cdot\pmb{k}^{\top}}{\sqrt{d}}\right)\cdot \pmb{v}$ . Then, a projection layer maps  $\pmb{z}$  to a multi-modal embedding space:
+
+$$
+\mathcal {O} = \operatorname {P r o j} (z), \tag {2}
+$$
+
+where  $\mathcal{O} = [o_{cls}, o_1, \dots, o_{h \times w}]^\top \in \mathbb{R}^{(hw + 1, c)}$  denotes the output of the image encoder,  $c$  denotes token dimension after the projection layer, and  $c < d$ . During CLIP training,  $o_{cls}$  is used for image-level learning; while during OVSS inference,  $\mathcal{O}[1:hw + 1]$  is used for patch-level prediction.
+
+# 3.2.FeatUp
+
+FeatUp [17] aims to train a model-agnostic upsampler. It executes an upsampling operation on LR features  $\mathcal{O}[1: h w + 1]$  from a frozen backbone network via a learnable upsampler  $\sigma_{\uparrow}$ , and then reconstructs the LR features using a learnable downsampler  $\sigma_{\downarrow}$ . Its critical insights can
+
+![](images/dc5d39782045cc704a240631c6f9dd716638d0a08be7af95c6850e9a92ee6a8c.jpg)  
+Figure 4. Comparison of with and without image reconstruction loss (Eq. (4)). The LR prediction is obtained directly using the output of CLIP (without bilinear interpolation). Color: building, tree, cropland, grass.
+
+be briefly summarized by the following loss function:
+
+$$
+\mathcal {L} _ {r e c} = \left\| \mathcal {O} [ 1: h w + 1 ] - \sigma_ {\downarrow} \left(\sigma_ {\uparrow} \left(\mathcal {O} [ 1: h w + 1 ]\right)\right) \right\| _ {2} ^ {2}. \tag {3}
+$$
+
+FeatUp instantiates  $\sigma_{\uparrow}$  as stacked parameterized JBU operators [29]. The upsampled HR feature element is estimated by weighting the neighboring elements of the LR feature. For weight generation, JBU considers two factors, the similarity and distance between neighboring elements and the center element in the guidance feature, corresponding to kernel  $k_{range}$  and  $k_{spatial}$ . For brevity, we omit the multi-view consistency constraint in [17].
+
+# 4. Method
+
+In the following, we introduce SegEarth-OV by first describing SimFeatUp's training, design and explaining why it is suitable for OVSS. Second, we discuss the impact of global token on dense prediction and present our method for alleviating global bias.
+
+# 4.1. SimFeatUp
+
+FeatUp provides us with an excellent training paradigm for general upsamplers. However, it lacks some considerations for the training-free setting, resulting in sub-optimal for the OVSS task, especially in remote sensing contexts.
+
+Image content retention. As described in Sec. 3.2, the goal of FeatUp is to minimize the original LR features and the LR features after the up-down-sampling (i.e.  $\sigma_{\downarrow}(\sigma_{\uparrow}(\mathcal{O}[1:h w + 1]))$ ). Since both  $\sigma_{\uparrow}$  and  $\sigma_{\downarrow}$  are learnable, with such a weak constraint, the up-down-sampling process becomes a black box, and there is no guarantee that the intermediate HR features are complete and consistent with the original image in content. A direct example is shown in Fig. 4,
+
+where a small building in the original image is present in the LR prediction but disappears in the HR prediction (top right). To solve this issue, we introduce an additional image reconstruction loss to constrain the HR features:
+
+$$
+\mathcal {L} _ {i m g} = \left\| I - \operatorname {C R N} \left(\sigma_ {\uparrow} \left(\mathcal {O} [ 1: h w + 1 ]\right)\right) \right\| _ {2} ^ {2}, \tag {4}
+$$
+
+where  $I$  denotes the input image, CRN denotes a content retention net. CRN is a very lightweight network that receives HR features as input and reconstructs the original image. Specifically, CRN consists of two 2D convolutional layers with normalization and a  $\mathrm{Tanh}$  activation layer, where the  $\mathrm{Tanh}$  layer is designed to constrain the output to [-1, 1], cf. VAEs [26]. Finally, the loss for training SimFeatUp consists of  $\mathcal{L}_{rec}$  and  $\mathcal{L}_{img}$  with a weight  $\gamma$ , i.e.,
+
+$$
+\mathcal {L} = \mathcal {L} _ {\text {r e c}} + \gamma \mathcal {L} _ {\text {i m g}}. \tag {5}
+$$
+
+Which feature to upsample? FeatUp takes the final output of CLIP, i.e.,  $\mathcal{O}[1:h w + 1]$  in Eq. (2), as input to the upsampler. This can work well in training-based settings, e.g., linear probe [1]. However, in training-free OVSS, as described in Sec. 2, vanilla self-attention leads to inferior performance. Therefore, the current OVSS method modulates it to self-self attention, and this law also works in remote sensing images. Under this premise, the SA in Eq. (1) would be replaced by other modules, and direct upsampling of  $\mathcal{O}[1:h w + 1]$  would lead to the mismatch between training and inference. Motivated by this, we propose to upsample CLIP features at an earlier layer. Specifically, we select the input of the last Transformer block of the CLIP's image encoder, i.e.,  $X[1:h w + 1]$  in Eq. (1). Furthermore, the high dimension of tokens in  $X$  leads to a high-cost upsampler. Therefore, we retain the projection layer. Ultimately, the features  $\mathcal{O}'$  which need upsampling are formulated as:
+
+$$
+\mathcal {O} ^ {\prime} = \operatorname {P r o j} (X [ 1: h w + 1 ]). \tag {6}
+$$
+
+Larger upsampling kernel. We follow the upsampling operator in FeatUp, i.e., the parameterized JBU. As mentioned in Sec. 3.2, the upsampling kernels  $k_{range}$  and  $k_{spatial}$  of the JBU are computed from the elements within a window in the guidance feature. The generation of  $k_{range}$  and  $k_{spatial}$  can be formulated as follows:
+
+$$
+k _ {\text {s p a t i a l}} (p, q) = \exp \left(\frac {- \| p - q \| _ {2} ^ {2}}{2 \tau_ {\text {s p a t i a l}} ^ {2}}\right), \tag {7}
+$$
+
+$$
+k _ {r a n g e} (p, q) =
+$$
+
+$$
+\operatorname {s o f t m a x} _ {(a, b) \in \Omega} \left(\frac {1}{\tau_ {\text {r a n g e}} ^ {2}} M L P (G [ i, j ]) \cdot M L P (G [ a, b ])\right), \tag {8}
+$$
+
+where  $(p,q)$  denotes the position in the kernel.  $\Omega$  denotes a window centered at  $(i,j)$  in the guidance feature  $G$ , which
+
+![](images/5740bc8de396ed7586134fee0b7ac33be5b4592f3d19b02649007a6eaaeabe42.jpg)  
+Figure 5. Comparison of before and after alleviating the global bias. (a) is the similarity map of patch tokens and cls tokens, some "non-building" regions also present high response, (b) is the original RGB image. Note that the right-hand histograms stretch the raw values for better presentation.
+
+is extracted from HR RGB image.  $\tau_{\text{spatial}}$  and  $\tau_{\text{range}}$  are learnable factors. In remote sensing images, unlike natural images, the size of the target presents a logarithmic scale spanning from the meter scale (e.g., trees, gardens) to the kilometer scale (e.g., forests, rangelands) [53]. Therefore, we set larger upsampling kernels to obtain a wider receptive field. Here, we expand the window size to  $11 \times 11$ , compared to  $7 \times 7$  in FeatUp. A possible concern is that a larger receptive field may introduce more irrelevant context, but with  $k_{\text{spatial}}$ , more distant points consistently contribute lower weights, which makes it more reasonable to use larger upsampling kernels.
+
+Simplify. On the structural side, we simplify the components inFeatUp. InFeatUp, the parameterized JBU modules are stacked 4 times for  $16 \times$  upsampling, and the parameters of each JBU module are independent. Although we fed HR features into the CRN to ensure the integrity of its content, the behavior of each JBU module is indeterminable. Therefore, in SimFeatUp, we change "JBUSTACK" to "JBU-One", i.e., only one parameterized JBU is used for upsampling. If  $16 \times$  upsampling is required, then it only needs to repeat the execution 4 times. Further, "JBU-One" significantly reduces the number of trainable parameters in the upsampler and provides the possibility of upsampling arbitrary multiples.
+
+# 4.2. Alleviating global bias
+
+As described in Sec. 3.1, in the training phase of CLIP, the [CLS] token, which contains the global information of the
+
+whole image, is optimized with the text embedding in the multi-modal space via contrastive learning. However, in the inference phase of OVSS, [CLS] token is generally discarded and only patch tokens are used for similarity computation with the prompt vocabulary. This means that there is a gap between training and inference. Indeed, previous work [48, 52, 62] also demonstrates that: each local visual token in CLIP focuses on a wide range of positions, and the attention maps typically share similar patterns. This suggests that the global attribute is attached to the patch tokens in CLIP. This property is generally not a concern in classification task, but it significantly impairs performance in dense prediction.
+
+The visualization in Fig. 5 demonstrates the above elaboration. We extract the [CLS] token using CLIP for the RGB image in Fig. 5(b), and compute its similarity with the candidate text embeddings. The image is recognized as the building, which is reasonable because the building covers the maximum range in the image. Then, we calculate the similarity of the [CLS] token with patch tokens as shown in Fig. 5(a). The highly responsive regions are not only the regions with buildings, some roads and pavements are also activated, which indicates that the global bias contaminates the local patch tokens. Motivated by this observation, we propose to "subtract" some global bias from the patch token. This solution is very straightforward and simple, it can be formulated as follows.
+
+$$
+\hat {\mathcal {O}} = \mathcal {O} [ 1: h w + 1 ] - \lambda \mathcal {O} [ 0 ], \tag {9}
+$$
+
+where  $\lambda$  denotes an intensity factor.  $\mathcal{O}[0]$  is broadcast to the same dimension as  $\mathcal{O}[1:h\omega +1]$
+
+# 5. Experiments
+
+# 5.1. Dataset
+
+In remote sensing application contexts, not only multi-class semantic segmentation but also extraction of certain land cover types (e.g., buildings, roads, water bodies) is required, e.g., Google's Open Buildings project. Therefore, we select 17 typical datasets covering common semantic segmentation, building extraction, road extraction, and water body segmentation (flood detection) tasks.
+
+Semantic segmentation. We evaluate SegEarth-OV on 8 remote sensing semantic segmentation datasets including OpenEarthMap [68], LoveDA [63], iSAID [66], Potsdam, Vaihingen $^{4}$ , UAVid [43], UDD5 [8] and VDD [5]. Among them, the first 5 datasets consist of mainly satellite images and the last 3 consist of UAV images. They contain custom foreground classes and a background class. Detailed descriptions of these datasets can be found in Appendix 7.1.
+
+Table 1. Open-vocabulary semantic segmentation quantitative comparison on remote sensing datasets. Evaluation metric: mIoU. Best and second best performances are highlighted.  
+
+<table><tr><td>Methods</td><td></td><td>OpenEarthMap</td><td>LoveDA</td><td>iSAID</td><td>Potsdam</td><td>Vaihingen</td><td>UAVidimg</td><td>UDD5</td><td>VDD</td><td>Average</td></tr><tr><td>CLIP [51]</td><td>ICML&#x27;21</td><td>12.0</td><td>12.4</td><td>7.5</td><td>14.5</td><td>10.3</td><td>10.9</td><td>9.5</td><td>14.2</td><td>11.4</td></tr><tr><td>MaskCLIP [77]</td><td>ECCV&#x27;22</td><td>25.1</td><td>27.8</td><td>14.5</td><td>31.7</td><td>24.7</td><td>28.6</td><td>32.4</td><td>32.9</td><td>27.2</td></tr><tr><td>SCLIP [62]</td><td>arXiv&#x27;23</td><td>29.3</td><td>30.4</td><td>16.1</td><td>36.6</td><td>28.4</td><td>31.4</td><td>38.7</td><td>37.9</td><td>31.1</td></tr><tr><td>GEM [3]</td><td>CVPR&#x27;24</td><td>33.9</td><td>31.6</td><td>17.7</td><td>36.5</td><td>24.7</td><td>33.4</td><td>41.2</td><td>39.5</td><td>32.3</td></tr><tr><td>ClearCLIP [30]</td><td>ECCV&#x27;24</td><td>31.0</td><td>32.4</td><td>18.2</td><td>40.9</td><td>27.3</td><td>36.2</td><td>41.8</td><td>39.3</td><td>33.4</td></tr><tr><td>SegEarth-OV</td><td>Ours</td><td>40.3</td><td>36.9</td><td>21.7</td><td>47.1</td><td>29.1</td><td>42.5</td><td>50.6</td><td>45.3</td><td>39.2</td></tr></table>
+
+Single-class extraction. We select 4 building extraction datasets (i.e., WHU $^{Aerial}$  [24], WHU $^{Sat.II}$  [24], Inria [44], and xBD [20]), 4 road extraction datasets (i.e., CHN6-CUG [80], DeepGlobe [14], Massachusetts [46], and SpaceNet [61]), and 1 flood detection dataset (i.e., WBS-SI $^5$ ) for the evaluation of single-class extraction. These datasets contain 1 foreground class (building, road or flood) and 1 background class. Detailed descriptions are in Appendix 7.2-7.4.
+
+Training dataset for SimFeatUp. SimFeatUp requires only image data for training, moreover, to avoid unfair comparisons, we use a public remote sensing image classification dataset, Million-AID [40], which is mainly collected from Google Earth. We randomly selected only 16k of these images to train SimFeatUp.
+
+# 5.2. Setup
+
+Implementation. Our implementations are based on MM-Segmentation [12] toolkit. If not specified, we use the original pretrained weights of CLIP (ViT-B/16) provided by OpenAI. For the text part, we use the OpenAI ImageNet template as input for the text encoder, e.g., "a photo of a {class name}". In addition, since the definition of certain classes may vary in some datasets, we use slight class rename tricks for all methods. For example, we rename "clutter" to "background" and "building" to {"building", "house", "roof"}, and the sub-class with the highest probability in {} will be the probability of that class. Detailed prompt class names for all datasets are listed in Appendix Tab. 7. For the image part, we resize input images with a long side of 448 and perform slide inference with a  $224 \times 224$  window and 112 stride. For SimFeatUp training, we randomly crop  $224 \times 224$  image patches on the original image. We use two 4090 GPUs to train 1 epoch with batch size set to 8. We retain the multi-view consistency constraint in FeatUp, and random flipping, translation and zoom are applied. For the hyper-parameters mentioned, the value of  $\gamma$  is set to 0.1 and  $\lambda$  is set to 0.3 for all datasets.
+
+Evaluation. We evaluate the semantic segmentation using the mean intersection over union (mIoU) metric. For single-class extraction, the IoU of the foreground class is used.
+
+Baseline. We take some lessons from natural image OVSS, which are also suitable for remote sensing scenes: we remove the FFN and residual connection of the last Transformer block, insights from [36] and [30]. In addition, the last self-attention is replaced by our modulated attention, i.e., the summation of  $q - q$ ,  $k - k$  and  $\nu - \nu$  as the weights of  $\nu$ :
+
+$$
+\operatorname {M} - \operatorname {S A} (\boldsymbol {q}, \boldsymbol {k}, \boldsymbol {v}) = \sum_ {\boldsymbol {i} \in \{\boldsymbol {q}, \boldsymbol {k}, \boldsymbol {v} \}} \operatorname {s o f t m a x} \left(\frac {\boldsymbol {i} \cdot \boldsymbol {i} ^ {\top}}{\sqrt {d}}\right) \cdot \boldsymbol {v}. \tag {10}
+$$
+
+# 5.3. Comparison to State-of-the-art
+
+Since the proposed SegEarth-OV is a training-free method and there is no previous OVSS method designed for remote sensing images, we select 5 state-of-the-art training-free OVSS models of natural images for comparison, including vanilla CLIP [51], MaskCLIP [77], SCLIP [62], GEM [3] and ClearCLIP [30].
+
+Semantic segmentation. As listed in Tab. 1, SegEarth-OV achieves the best performance on all 8 semantic segmentation datasets. SegEarth-OV achieves more than  $40\%$  mIoU on 5 datasets and more than  $50\%$  on the UDD5 dataset, which implies that the OVSS method is feasible in remote sensing scenarios. Compared to the previous method, SegEarth-OV achieves a performance gain of more than  $5\%$  on 5 datasets and an average gain of  $5.8\%$  on 8 datasets. On the iSAID dataset, the mIoU of SegEarth-OV is only  $21.7\%$ , which is due to the fine-grained category delineation in this dataset, which covers 16 categories (see Appendix Tab. 7).
+
+Single-class extraction. In the building extraction task, the increase delivered by SegEarth-OV is more significant, as listed in Tab. 2. Considering that the "building" class occupies a small area (see Appendix Fig. 1), we evaluate the setup for larger scale images, i.e., resizing the long side of the input image to  $896 \times 896$ . This setup significantly improves the IoU of Inria and xBD, which on the other hand supports our view that spatial detail preservation is essential for remote sensing OVSS. In the road extraction task, although SegEarth-OV achieves the best IoU, overall, the performance of all methods on the 4 datasets is unsatisfactory, with a best IoU of only  $35.4\%$ . There may be two reasons for this phenomenon: (1) The special shape of the road makes it difficult to be extracted in a training-free OVSS manner; (2) The labels of some data are generated based on
+
+![](images/2bbed41f4b9a9c7ec01448a52363608613c2198740e86e064569b843b9ae8b22.jpg)  
+Figure 6. Qualitative comparison between different training-free OVSS methods on OpenEarthMap [68], UDD5 [8] and WHU<sup>Aerial</sup> [24] datasets (best viewed digitally with zoom, especially for the edges of the object).
+
+Table 2. Open-vocabulary building / road / flood extraction quantitative comparison on remote sensing datasets. Evaluation metric: IoU of the foreground class, i.e. building, road or flood. Best and second best performances are highlighted.  
+
+<table><tr><td rowspan="2">Method</td><td colspan="4">Building Extraction</td><td colspan="4">Road Extraction</td><td rowspan="2">Flood Detection WBS-SI</td></tr><tr><td>WHU Aerial</td><td>WHU Sat.II</td><td>Inria</td><td>xBD pre</td><td>CHN6-CUG</td><td>DeepGlobe</td><td>Massachusetts</td><td>SpaceNet</td></tr><tr><td colspan="10">448 × 448:</td></tr><tr><td>CLIP [51]</td><td>17.7</td><td>3.5</td><td>19.6</td><td>16.0</td><td>7.7</td><td>3.9</td><td>4.9</td><td>7.1</td><td>18.6</td></tr><tr><td>MaskCLIP [77]</td><td>29.8</td><td>14.0</td><td>33.4</td><td>29.2</td><td>28.1</td><td>13.2</td><td>10.6</td><td>20.8</td><td>39.8</td></tr><tr><td>SCLIP [62]</td><td>33.4</td><td>21.0</td><td>34.9</td><td>25.9</td><td>21.1</td><td>7.0</td><td>7.4</td><td>14.9</td><td>32.1</td></tr><tr><td>GEM [3]</td><td>24.4</td><td>13.6</td><td>28.5</td><td>20.8</td><td>13.4</td><td>4.7</td><td>5.1</td><td>11.9</td><td>39.5</td></tr><tr><td>ClearCLIP [30]</td><td>36.6</td><td>20.8</td><td>39.0</td><td>30.1</td><td>25.5</td><td>5.7</td><td>6.4</td><td>16.3</td><td>44.9</td></tr><tr><td>SegEarth-OV</td><td>49.2</td><td>28.4</td><td>44.6</td><td>37.0</td><td>35.4</td><td>17.8</td><td>11.5</td><td>23.8</td><td>60.2</td></tr><tr><td colspan="10">896 × 896:</td></tr><tr><td>SegEarth-OV</td><td>49.9</td><td>-</td><td>48.9</td><td>43.1</td><td>32.8</td><td>20.1</td><td>17.2</td><td>29.1</td><td>57.9</td></tr></table>
+
+OpenStreetMap vector shapes with fixed widths attached, which are inherently imprecise. Again, the extraction of roads can generally benefit from larger size images. For the flood detection task, where "water" class features can be easily recognized, the IoU of SegEarth-OV is improved by  $15.3\%$  over the previous best method, up to  $60.2\%$ . Due to the small size of the original images in the WBS-SI dataset, resizing to a larger size does not result in a positive gain.
+
+Qualitative results. We present qualitative results for MaskCLIP, ClearCLIP, and SegEarth-OV in Fig. 6. Some observations are summarized as follows: (1) There are some incorrect category predictions in MaskCLIP, e.g., water on the road and bareland on the cropland. (2) ClearCLIP can generate correct category predictions, but lacks precise localization capability, with distorted target shapes and ill-fitting boundaries of the prediction mask. (3) SegEarth-OV is capable of generating more fine-grained masks that fit the target edges and maintain correct category discrimination. More visualizations can be found in Appendix Fig. 8-10.
+
+# 5.4. Ablation Study and Analysis
+
+Plug and play. Two key insights of this work, SimFeatUp and global bias alleviation, which can be attached to other OVSS methods as plug-and-play modules. As listed in Tab. 4, a revealing observation is that on both the Ope
+
+nEarthMap and WHU $^{Aerial}$  datasets, as the base capability of the model improves (from MaskCLIP to ClearCLIP), the increases delivered by our method also improve ( $\uparrow 3.3$ ,  $\uparrow 5.1$ ,  $\uparrow 8.1$  on OpenEarthMap,  $\uparrow 5.6$ ,  $\uparrow 6.1$ ,  $\uparrow 14.5$  on WHU $^{Aerial}$ ). This suggests that our method has the potential to improve localization and discrimination for stronger models.
+
+Ablation study. To assess each of the proposed components, we perform a detailed ablation analysis, as listed in Tab. 5. FitUp (CLIP) denotes the original FitUp upsampler, which provides a  $1.5\%$  improvement on OpenEarthMap but decreases the performance on  $\mathrm{WHU}^{Sat.II}$  and WBS-SI (more comparisons between FitUp and the proposed method are shown in the bottom-right of Fig. 1). FitUp (MaskCLIP) denotes using  $\mathbf{v}$  of self-attention as the upsampled feature, which somewhat mitigates the possible negative effects of FitUp (CLIP). In SimFitUp, the input feature  $X$  of the last block is used to upsample, which presents a significant improvement in all 3 datasets. A substantial improvement is also delivered after replacing the training material for the upsampler from natural images to remote sensing images. "JBU-One" reduces the parameters by nearly  $4\times$  while delivering a slight IoU gain (only  $< 0.3\mathrm{M}$  parameters). The introduction of CRN with image reconstruction loss brings  $1.7\%$ ,  $0.4\%$ , and  $1.6\%$  improvement on 3 datasets, respectively. Note that the CRN only
+
+Table 3. Quantitative comparison of vanilla CLIP and remote sensing CLIPs (ViT-B/32). Evaluation metric: mIoU.  
+
+<table><tr><td colspan="2">Models</td><td>OpenEarthMap</td><td>LoveDA</td><td>iSAID</td><td>Potsdam</td><td>Vaihingen</td><td>UAVidimg</td><td>UDD5</td><td>VDD</td><td>Average</td></tr><tr><td>CLIP [51]</td><td>ICML&#x27;21</td><td>25.7</td><td>27.2</td><td>16.2</td><td>40.0</td><td>25.1</td><td>31.6</td><td>39.7</td><td>39.1</td><td>30.6</td></tr><tr><td>RemoteCLIP [37]</td><td>TGRS&#x27;23</td><td>18.2</td><td>37.8</td><td>18.9</td><td>21.9</td><td>22.9</td><td>16.1</td><td>27.1</td><td>28.1</td><td>23.9</td></tr><tr><td>GeoRSCLIP [75]</td><td>TGRS&#x27;24</td><td>35.0</td><td>30.8</td><td>23.6</td><td>38.0</td><td>22.3</td><td>34.0</td><td>39.1</td><td>40.5</td><td>32.9</td></tr><tr><td>SkyCLIP [65]</td><td>AAAI&#x27;24</td><td>28.6</td><td>33.0</td><td>15.3</td><td>41.7</td><td>24.1</td><td>31.6</td><td>38.2</td><td>35.8</td><td>31.0</td></tr></table>
+
+Table 4. The proposed method is a plug-and-play module. “+ ours” indicates using SimFeatUp (Sec. 4.1) and alleviating the global bias (Sec. 4.2).  
+
+<table><tr><td>Methods</td><td>OpenEarthMap</td><td>WHU Aerial</td><td>WBS-SI</td></tr><tr><td>MaskCLIP</td><td>25.1</td><td>29.8</td><td>39.8</td></tr><tr><td>+ ours</td><td>28.4↑3.3</td><td>35.4↑5.6</td><td>48.8↑9.0</td></tr><tr><td>SCLIP</td><td>29.3</td><td>33.4</td><td>32.1</td></tr><tr><td>+ ours</td><td>34.4↑5.1</td><td>39.5↑6.1</td><td>53.4↑21.3</td></tr><tr><td>ClearCLIP</td><td>31.0</td><td>36.6</td><td>44.9</td></tr><tr><td>+ ours</td><td>39.1↑8.1</td><td>51.1↑14.5</td><td>60.4↑15.5</td></tr></table>
+
+Table 5. Detailed ablation results for each component. "X"↑ indicates upsampling earlier stage features, i.e. Eq. (6). "+" RS Data" indicates using Million-AID [40] to train the upsampler, before using the images in COCO-Stuff [4].  
+
+<table><tr><td></td><td>OpenEarthMap</td><td>WHU Sat. II</td><td>WBS-SI</td></tr><tr><td>Baseline</td><td>32.4</td><td>22.7</td><td>46.9</td></tr><tr><td>FeatUp (CLIP)</td><td>33.9</td><td>20.2</td><td>39.6</td></tr><tr><td>FeatUp (MaskCLIP)</td><td>33.8</td><td>25.2</td><td>45.9</td></tr><tr><td>“X”↑</td><td>34.6</td><td>26.0</td><td>54.2</td></tr><tr><td>+ RS Data</td><td>36.0 ↑1.4</td><td>26.2</td><td>56.4</td></tr><tr><td>+ JBU-One</td><td>36.3 ↑0.3</td><td>26.0</td><td>57.1</td></tr><tr><td>+ Rec. Image</td><td>37.6 ↑1.3</td><td>26.4</td><td>58.7</td></tr><tr><td>+ Alleviate Global Bias</td><td>39.3 ↑1.7</td><td>27.9</td><td>59.5</td></tr><tr><td>+ Large Kernel</td><td>40.3 ↑1.0</td><td>28.4</td><td>60.2</td></tr></table>
+
+participates in SimFeatUp's training and is discarded during inference. Global bias alleviation shows significant improvement for all 3 datasets, with an average  $1.3\%$  improvement. Finally, expanding the upsampling kernel to  $11 \times 11$  also exhibits consistent improvement across all datasets.
+
+Results on natural images. We evaluate SimFeatUp as an external unit on 3 natural image datasets: PASCAL Context59 [47], COCOStuff [4] and Cityscapes [13]. As listed in Tab. 6, after upsampling the visual features of MaskCLIP, SCLIP, and ClearCLIP using SimFeatUp, their mIoUs are improved by  $5.7\%$ ,  $1.2\%$ , and  $1.2\%$ , respectively. This reveals the potential of our method to inspire general vision.
+
+Remote sensing CLIPs for OVSS. We evaluate the performance of remote sensing CLIPs on OVSS, including RemoteCLIP [37], GeoRSCLIP [75], and SkyCLIP [65], which are trained on 0.8M, 5M, and 2.6M remote sensing data, respectively, without changing the model structure of
+
+Table 6. OVSS quantitative comparison on natural image datasets. The basic results are cited from [30].  
+
+<table><tr><td>Methods</td><td>Context59 [47]</td><td>Stuff [4]</td><td>Cityscapes [13]</td><td>Average</td></tr><tr><td>TCL [7]</td><td>30.3</td><td>19.6</td><td>23.1</td><td>24.3</td></tr><tr><td>Reco [57]</td><td>22.3</td><td>14.8</td><td>21.1</td><td>19.4</td></tr><tr><td>MaskCLIP</td><td>26.4</td><td>16.4</td><td>12.6</td><td>18.5</td></tr><tr><td>+ SimFeatUp</td><td>28.7</td><td>18.0</td><td>25.8</td><td>24.2 ↑5.7</td></tr><tr><td>SCLIP</td><td>33.0</td><td>21.1</td><td>29.1</td><td>27.7</td></tr><tr><td>+ SimFeatUp</td><td>34.1</td><td>22.0</td><td>30.5</td><td>28.9 ↑1.2</td></tr><tr><td>ClearCLIP</td><td>35.9</td><td>23.9</td><td>30.0</td><td>29.9</td></tr><tr><td>+ SimFeatUp</td><td>37.5</td><td>25.1</td><td>30.7</td><td>31.1 ↑1.2</td></tr></table>
+
+CLIP. Since these works do not provide the ViT-B/16, we uniformly use ViT-B/32. Hence, we repeat the JBU operation 5 times in SimFeatUp. For fair comparison, we train the respective SimFeatUp for each model. As listed in Tab. 3, RemoteCLIP performs suboptimally to vanilla CLIP, which indicates that a small amount of domain data diminishes the model's transfer capability. GeoRSCLIP achieves the best performance against SkyCLIP, which suggests that domain VLMs can benefit from more diverse domain-specific data. Moreover, the OVSS task effectively reflects the model's discrimination and localization capabilities, and can serve as an evaluation metric for remote sensing VLMs.
+
+# 6. Conclusion
+
+In this paper, we present SegEarth-OV, a training-free OVSS method for remote sensing images. The design of SegEarth-OV was motivated by the observation that OVSS methods currently used for natural images do not perform well on remote sensing images. The two key insights of SegEarth-OV, i.e., SimFeatUp and global bias alleviation, exhibit consistent improvements on 17 remote sensing datasets spanning the tasks of semantic segmentation, building extraction, road extraction, and flood detection, well beyond the previous state-of-the-art methods. More importantly, as the first exploration of training-free OVSS method in remote sensing scenarios, this work demonstrates that the OVSS solution is feasible in earth perception tasks even if the VLMs are pre-trained on natural images. We expect that this work will inspire more OVSS methods and more capable remote sensing VLMs, and open up new possibilities for the Earth vision community.
+
+Acknowledgment. This work is supported by the National Key R&D Program of China (2022YFA1004100), National Natural Science Foundation of China (No. 62272375, No. 62425113, No. 62192781), Major Key Project of PCL (PCL2024A06), and Tianyuan Fund for Mathematics of the National Natural Science Foundation of China (No. 12426105).
+
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+# SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation
+
+Yunxiang Fu* Meng Lou* Yizhou Yu†  
+School of Computing and Data Science, The University of Hong Kong  
+yunxiang@connect.hku.hk, loumeng@connect.hku.hk, yizhouy@acm.org
+
+# Abstract
+
+High-quality semantic segmentation relies on three key capabilities: global context modeling, local detail encoding, and multi-scale feature extraction. However, recent methods struggle to possess all these capabilities simultaneously. Hence, we aim to empower segmentation networks to simultaneously carry out efficient global context modeling, high-quality local detail encoding, and rich multi-scale feature representation for varying input resolutions. In this paper, we introduce SegMAN, a novel linear-time model comprising a hybrid feature encoder dubbed SegMAN Encoder, and a decoder based on state space models. Specifically, the SegMAN Encoder synergistically integrates sliding local attention with dynamic state space models, enabling highly efficient global context modeling while preserving fine-grained local details. Meanwhile, the MMSCopE module in our decoder enhances multi-scale context feature extraction and adaptively scales with the input resolution. Our SegMAN-B Encoder achieves  $85.1\%$  ImageNet-1k accuracy  $(+1.5\%)$  over VMamba-S with fewer parameters). When paired with our decoder, the full SegMAN-B model achieves  $52.6\%$  mIoU on ADE20K  $(+1.6\%)$  over SegNeXt-L with  $15\%$  fewer GFLOPs),  $83.8\%$  mIoU on Cityscapes  $(+2.1\%)$  over SegFormer-B3 with half the GFLOPs), and  $1.6\%$  higher mIoU than VWFormer-B3 on COCO-Stuff with lower GFLOPs. Our code is available at https://github.com/yunxiangfu2001/SegMAN.
+
+# 1. Introduction
+
+As a core computer vision task, semantic segmentation needs to assign a categorical label to every pixel within an image [35]. Accurate semantic segmentation demands three crucial capabilities. First, global context modeling establishes rich contextual dependencies regardless of spatial distance, enabling overall scene understanding [13]. Sec-
+
+![](images/51c3fad2e271e962b3996bc33fd3c9d4ef841a8f425698b5ebdeca43687023f0.jpg)
+
+![](images/04f67f0763cde4fe9f86c544c46a6caa8959873d929907ddb54fe9073a935a96.jpg)
+
+![](images/c0221811783acdd824af8726764f1033ed09282b198a59578ef62115774cb042.jpg)  
+Figure 1. SegMAN Encoder classification performance compared with representative vision backbones alongside semantic segmentation results of the full SegMAN model compared to prior state-of-the-art models.
+
+![](images/b3dc0d9af186a4795ab03b602ec1eb4a0649a8cf634de2155990532799098023.jpg)
+
+ond, local detail encoding endows fine-grained feature and boundary representations, crucial for differentiating semantic categories and localizing boundaries between adjacent regions with different semantic meanings [59]. Third, context modeling based on multiple intermediate scales promotes semantic representations across multiple scales, addressing intra-class scale variations while enhancing interclass discrimination [2, 57, 63].
+
+Recent methods of semantic segmentation have been unable to simultaneously encapsulate all three of these capabilities effectively. For instance, VWFormer [57] introduces a varying window attention (VWA) mechanism that effectively captures multi-scale information through cross-attention between local windows and multiple windows with predefined scales. However, at higher input resolutions, the global context modeling capability of VWA diminishes because the predefined window sizes fail to
+
+![](images/f72c363bff6dcb884f6dd6a85788f7eb4b8a116f9d04c9d1b9c9091f7affb00b.jpg)  
+Figure 2. Qualitative analysis of receptive field patterns and segmentation performance for small-sized models (27M-29M parameters). Left: Visualization of effective receptive fields (ERF) on the Cityscapes validation set at  $1024 \times 1024$  resolution, illustrating SegMAN's stronger global context modeling capacity in comparison to existing state-of-the-art models. Right: Segmentation maps highlighting SegMAN's superior capacity to encode fine-grained local details that are often missed by existing approaches.
+
+![](images/17a947d2086f3a7fa19b74bc534e8818df350732814b0a58d6191eafc65a7036.jpg)
+
+![](images/3c5f359fc11268f75effdcefecea150e22e04114f03cac8795bdf0a4c25d40bf.jpg)
+
+maintain full feature map coverage. In addition, larger windows significantly increase the computational cost as self-attention exhibits quadratic complexity. Likewise, EDAFormer [60] proposes an embedding-free spatial reduction attention (SRA) mechanism to implement global attention efficiently and an all-attention decoder for global and multi-scale context modeling. However, fine-grained details are lost due to its reliance on downsampled features for token-to-region attention. Moreover, the absence of dedicated local operators in the feature encoder limits fine-grained feature learning [56, 60].
+
+To present a more intuitive understanding of the aforementioned issues, we visualize the effective receptive field (ERF) maps and segmentation maps of recent state-of-the-art models [56, 57, 60] with 27M-29M parameters. As depicted in Figure 2 (Left), the ERF of VWFormer [57] and EDAFormer [60] has limited feature map coverage when processing high-resolution Cityscapes images. Meanwhile, the segmentation maps in Figure 2 (Right) reveal that existing methods have a limited ability to recognize fine-grained local details, which we attribute to insufficient local feature modeling in these architectures.
+
+In this work, we aim to encapsulate omni-scale context modeling for a varying input resolution within a semantic segmentation network. As a result, we propose SegMAN, a novel segmentation network capable of carrying out efficient global context modeling, high-quality local detail encoding, and rich context representation at diverse scales
+
+simultaneously. Our SegMAN introduces two novel subnetworks: (1) a hybrid feature encoder that integrates Local Attention and State Space models (LASS) in the token mixer, and (2) a decoder with a Mamba-based Multi-Scale Context Extraction (MMSCopE) module. In the SegMAN Encoder, LASS leverages a two-dimensional state space model (SS2D) [28] for global context modeling and neighborhood attention [19] (Natten) for local detail encoding, both functioning in linear time. The dynamic global scanning in our method always covers the full feature map, enabling robust global context modeling across varying input resolutions and all encoder layers. Meanwhile, by performing local attention within a sliding window, Natten exhibits an outstanding capability in fine-grained local detail encoding. Our decoder complements the encoder with robust feature learning across multiple scales. The core MMSCopE module first aggregates semantic context from multiple regions of the encoder feature map. Then it uses SS2D to scan each regionally aggregated feature map to learn multiscale contexts at linear complexity. For higher efficiency, the feature maps at different scales are reshaped to the same resolution and concatenated so that scanning only needs to be performed on a single feature map. Note that our method differs from existing methods in two aspects. First, unlike multi-kernel convolution [2] and multi-scale window attention [57], our design adaptively scales with the input resolution. Second, by performing SS2D-based scanning over entire multi-scale feature maps, our method better preserves
+
+fine-grained details that are typically compromised by pooling operations [36, 63] or spatial reduction attention [60] in existing methods.
+
+As shown in Figure 1, our SegMAN Encoder shows superior performance compared to representative vision backbones on ImageNet-1k. When paired with our novel decoder, the full SegMAN model establishes new state-of-the-art segmentation performance on ADE20K [65], Cityscapes [8], and COCO-Stuff-164k [1]. To summarize, our contributions are threefold:
+
+- We introduce a novel encoder architecture featuring our LASS token mixer that synergistically combines local attention with state space models for efficient global context modeling and local detail encoding.  
+- We propose MMSCopE, a novel decoder module that operates on multi-scale feature maps that adaptively scale with the input resolution, surpassing previous approaches in both fine-grained detail preservation and omni-scale context learning.  
+- We demonstrate through comprehensive experiments that SegMAN, powered by LASS and MMSCopE, establishes new state-of-the-art performance while maintaining competitive computational efficiency across multiple challenging semantic segmentation benchmarks.
+
+# 2. Related work
+
+Segmentation encoders. Prior research in semantic segmentation architectures typically comprises two key components: an encoder for feature extraction from input images and a decoder for multi-scale context learning and pixel-wise classification. While conventional approaches often adapt popular classification networks such as ResNet [20], ConvNeXt [30], PVT [49], and SwinTransformer [29], along with advanced backbones [31-33, 41] as encoders, improvements in classification performance do not necessarily translate to enhanced segmentation capabilities [21], primarily due to the requirement for fine-grained spatial information in segmentation tasks. This observation has motivated the development of specialized segmentation-oriented encoders [18, 39, 45, 53, 56, 58, 60, 64]. In this paper, we introduce a novel segmentation encoder that integrates local attention with dynamic State Space Models (Mamba) [16]. In contrast to previous works, our design enables the simultaneous modeling of fine-grained details and global contexts while maintaining linear time complexity.
+
+Segmentation decoders. Multi-scale feature learning plays a fundamental role in semantic segmentation, leading to numerous innovations in decoder architectures. Seminal works established various approaches to multi-scale feature aggregation, including the adaptive pooling strategy of PSPNet [63], atrous convolutions in the DeepLab series [2-4], and feature pyramid network (FPN) based decoders
+
+such as UPerNet [54] and Semantic FPN [23]. Contemporary architectures have advanced both efficiency and performance through diverse approaches to multi-scale feature fusion, including MLP-based methods (Segformer [56]), Matrix decomposition modules (SegNeXt [15, 18]), Transformers (FeedFormer [43], VWFormer [57]), and convolutions (CGRSeg [36]). An approach relevant to ours is VWFormer [57], which employs cross-attention between local windows and multiple larger windows with predefined scales to capture multi-scale contexts. However, its reliance on fixed window scales limits its global context modeling capability: as the input resolution increases, the predefined windows fail to maintain complete feature map coverage. In contrast, our proposed decoder adaptively extracts context features at different scales while maintaining full feature map coverage regardless of the input resolution.
+
+Vision Mamba. Dynamic state space models represented by Mamba [16] have demonstrated promising performance in sequence modeling. Mamba combines structured state space models [17, 44] with selective scanning, enabling global context modeling with dynamic weights and linear time complexity. These favorable properties have led to promising results on vision tasks. For example, VMamba [28] and ViM [67] are Mamba-based vision backbone networks. Other works have explored Mamba for medical imaging [26, 40], 3D point clouds [27, 62], and image/video generation [12, 14, 46]. In this paper, we leverage Mamba to learn global contexts in the encoder and multiscale contexts in the decoder for segmentation.
+
+# 3. Method
+
+# 3.1. Overall Architecture
+
+As shown in Figure 3, our proposed SegMAN consists of a newly designed feature encoder and decoder. Specifically, we propose a novel hybrid feature encoder based on core mechanisms from both Transformer and dynamic State Space Models [16], and a Mamba-based Multi-Scale Context Extraction module (MMSCopE) in the decoder. The hierarchical SegMAN Encoder integrates Neighborhood Attention (Natten) [19] and the 2D-Selective-Scan Block (SS2D in VMamba) [28] within the token mixer termed Local Attention and State Space (LASS), enabling comprehensive feature learning at both global and local scales across all layers while maintaining linear computational complexity. Meanwhile, MMSCoPE dynamically and adaptively extracts multi-scale semantic information by processing feature maps at varying levels of granularity using SS2D [28], with the scales adaptively adjusted according to the input resolution. Collectively, the SegMAN Encoder injects robust global contexts and local details into a feature pyramid, where feature maps are progressively downsampled and transformed to produce omni-scale fea
+
+![](images/9814e5c48a327bbb74e528d1a4cf1ee1fe640fb3b411388efb26af3b9f9dd8d9.jpg)  
+Figure 3. Overall Architecture of SegMAN. (a) Hierarchical SegMAN Encoder. (b) LASS for modeling global contexts and local details with linear complexity. (c) The SegMAN Decoder. (d) The MMSCopE module for multi-scale contexts extraction.
+
+![](images/15ed16b7245d6b9a34af397fd2d8300c9044612461ce46d752f10e2cc58d5b0c.jpg)
+
+tures at different pyramid levels. This feature pyramid is then fed into MMSCopE, where features are further aggregated spatially as well as across scales, resulting in comprehensive multi-scale representations that can be used for dense prediction.
+
+# 3.2. Feature encoder
+
+Overview. As shown in Figure 3, our SegMAN Encoder is a standard four-stage network [29, 30], where each stage begins with a strided  $3 \times 3$  convolution for spatial reduction, followed by a series of LASS Blocks. Each LASS Block includes pre-layer normalization, a novel LASS module, and a feedforward network (FFN) [10, 19, 29]. Our proposed LASS represents the first integration of local self-attention (Natten [19]) and state space models (SS2D [28]) for semantic segmentation, capturing local details and global contexts simultaneously with linear time complexity. Ablation studies (Table 5) confirm the necessity of both components.
+
+Local attention and state space module. Long-range dependency modeling is crucial for semantic segmentation as they enable comprehensive context learning and scene understanding, as demonstrated by the success of Transformer-based semantic segmentation models [34, 39, 56, 60, 64]. To efficiently learn global contexts across all network layers, we leverage the SS2D block in VMamba [28]. This variant of a dynamic state space model named Mamba [16] adopts four distinct and complementary scanning paths, enabling each token to integrate informa
+
+<table><tr><td>SegMAN Encoder</td><td>Channels</td><td>Blocks</td><td>Params (M)</td><td>FLOPs (G)</td></tr><tr><td>Tiny</td><td>[32, 64, 144, 192]</td><td>[2, 2, 4, 2]</td><td>3.5</td><td>0.65</td></tr><tr><td>Small</td><td>[64, 144, 288, 512]</td><td>[2, 2, 10, 4]</td><td>25.5</td><td>4.05</td></tr><tr><td>Base</td><td>[80, 160, 364, 560]</td><td>[4, 4, 18, 4]</td><td>45.0</td><td>9.94</td></tr><tr><td>Large</td><td>[96, 192, 432, 640]</td><td>[4, 4, 28, 4]</td><td>81.0</td><td>16.8</td></tr></table>
+
+Table 1. Configurations of the three SegMAN Encoder variants. FLOPs were measured at the  $224 \times 224$  resolution.
+
+tion from all other tokens in four different directions.
+
+However, while Mamba [16] achieves linear time complexity by compressing the global context of each channel into a fixed-dimensional hidden state [16], this compression inherently leads to information loss, in particular, loss of fine-grained local spatial details, that are crucial in segmentation to accurately localize region boundaries. To this end, we utilize Neighborhood Attention (Natten) [19], where a sliding-window attention mechanism localizes every pixel's attention span to its immediate neighborhood. This approach retains translational equivalence, effectively captures local dependencies, and has linear time complexity. In practice, we serially stack Natten and SS2D, and a shortcut around SS2D is employed to merge local and global information. The merged output is then fed into a  $1 \times 1$  convolution layer for further global-local feature fusion.
+
+Network architecture. Our SegMAN Encoder produces a feature pyramid. The spatial resolution of  $F_{i}$  is  $\frac{H}{2^{1 + i}} \times \frac{W}{2^{1 + i}}$ , where  $H$  and  $W$  are the height and width
+
+of the input image, respectively. We design three SegMAN Encoder variants with different model sizes, each with computational complexity comparable to some existing semantic segmentation encoders [18, 56, 60]. As the feature map resolution at Stage 4 is  $(\frac{H}{32} \times \frac{W}{32})$ , global self-attention [48] becomes computationally feasible. Therefore we replace SS2D with this more powerful global context modeling mechanism. The configurations of these models are presented in Table 1.
+
+# 3.3. Feature Decoder
+
+Overview. As shown in Figure 3, our proposed decoder first aggregates features at various levels of abstraction (from low-level  $F_{2}$  to high-level  $F_{4}$ ) to obtain a feature map  $F$  of size  $\frac{H}{8} \times \frac{W}{8}$ , as in prior works [56, 57]. Specifically, each feature map  $F_{i}$  is projected to a lower dimension using a  $1 \times 1$  convolution followed by batch normalization and ReLU, collectively denoted as 'Conv' in Figure 3. Subsequently,  $F_{3}$  and  $F_{4}$  are upsampled using bilinear interpolation to match the spatial dimensions of  $F_{2}$ . The upsampled features  $F_{up3}$  and  $F_{up4}$  are concatenated together with  $F_{2}$  and passed through a Conv layer for channel dimension reduction, resulting in  $F$  with size  $\frac{H}{8} \times \frac{W}{8} \times C$ , where  $C$  is the number of channels. The feature map  $F$  is then processed by our proposed MMSCopE module to extract rich multi-scale context features, producing a new feature map  $F'$ . We then add the feature map  $F'$  to  $F_{2}$ ,  $F_{up3}$ , and  $F_{up4}$  to enrich their multi-scale contexts. The final prediction pathway concatenates  $F'$  with these context-enhanced features and the global pooling result on  $F_{up4}$ , passes the combined features through a two-layer MLP, and the result is upsampled to the input resolution.
+
+Mamba-based multi-scale context extraction. To effectively extract contexts at different scales, we propose a Mamba-based Multi-Scale Context Extraction (MMSCopE) module. As shown in Figure 3, we apply strided convolutions to downsample the feature map  $F$ , generating  $F_{s2}$  and  $F_{s4}$  with a resolution equal to  $1/2$  and  $1/4$  of that of  $F$ , respectively. The motivation behind this is to obtain multiple regionally aggregated contexts through two derived feature maps:  $F_{s2}$ , which aggregates features from  $3 \times 3$  neighborhoods using a convolution with stride 2, and  $F_{s4}$ , which aggregates features from  $5 \times 5$  neighborhoods with stride 4. Then, motivated by the observation that Mamba scans each channel independently, we use a single Mamba scan to simultaneously extract contexts from the three feature maps  $F$ ,  $F_{s2}$ , and  $F_{s4}$ . The main idea is to concatenate these feature maps along the channel dimension and perform a single Mamba scan, which is more efficient on the GPU than processing each feature map separately. However, the spatial dimensions of these feature maps are not consistent, which prevents direct concatenation. To resolve this, we employ lossless downsampling via the Pixel
+
+Unshuffle [42] operation to reduce the spatial dimensions of  $F$  and  $F_{s2}$  to match that of  $F_{s4}$ , which is  $\frac{H}{32} \times \frac{W}{32}$ . Specifically, this operation rearranges non-overlapping  $4 \times 4$  patches from  $F$  and  $2 \times 2$  patches from  $F_{s2}$  into their respective channel dimension, increasing the channel depth by a factor of 16 and 4 respectively, while preserving complete spatial information. The transformed feature maps retain information at their original scales. To reduce computational costs, we project each feature map to a fixed channel dimension  $C$  through  $1 \times 1$  convolutions. This step assigns equal importance to the context information from each scale. Then we concatenate the projected feature maps along the channel dimension, and pass the combined feature map to SS2D [28] to achieve multi-scale context extraction in a single scan. The resulting feature map is of size  $\frac{H}{8} \times \frac{W}{8} \times 3C$ , where the three segments along the channel dimension correspond to earlier feature maps at three distinct scales ( $F$ ,  $F_{s2}$ , and  $F_{s4}$ ). To facilitate context mixing across scales, we append a  $1 \times 1$  convolution layer after the SS2D scan. Finally, we use bilinear interpolation to up-sample the mixed features to  $1/8$  of the input resolution, and project the channel dimension down to  $C$  using another  $1 \times 1$  convolution. This yields the new feature map  $F'$  with mixed multi-scale contexts.
+
+Multi-scale fusion. Instead of directly using the feature map  $F'$  to predict pixel labels, we further exploit stage-specific representations from the encoder by adding  $F'$  to  $F_2$ ,  $F_{up3}$ , and  $F_{up4}$ . This effectively injects multi-scale contexts into stage-specific feature maps, which inherits information from various levels of abstraction. Next, we concatenate the resulting context-enhanced feature maps from each stage, the multi-scale context feature  $F'$ , and the global average pooling result on  $F_{up4}$ . For pixel-wise label prediction, the diverse concatenated features are fused through a two-layer MLP, and further bilinearly interpolated to restore the original input resolution.
+
+# 4. Experiments
+
+# 4.1. Setting
+
+Datasets. Following previous works [18, 56], we firstly pre-train our SegMAN Encoder on the ImageNet-1K dataset [9]. Then, we evaluate the semantic segmentation performance on three commonly used datasets: ADE20K [65], a challenging scene parsing benchmark consisting of 20,210 images annotated with 150 semantic categories; Cityscapes [8], an urban driving dataset comprising 5,000 high-resolution images with 19 semantic categories; and COCO-Stuff-164K [1], which contains 164,000 images labeled across 172 semantic categories.
+
+Implementation details. We conducted ImageNet pretraining using the Timm library [52] with hyperparameters and data augmentation settings identical to those of Swin
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Params(M)</td><td colspan="2">ADE20K</td><td colspan="2">Cityscapes</td><td colspan="2">COCO-Stuff</td></tr><tr><td>GFLOP</td><td>mIoU</td><td>GFLOP</td><td>mIoU</td><td>GFLOP</td><td>mIoU</td></tr><tr><td>Segformer-B0 [56]</td><td>3.8</td><td>8.4</td><td>37.4</td><td>125.5</td><td>76.2</td><td>8.4</td><td>35.6</td></tr><tr><td>SegNeXt-T [18]</td><td>4.3</td><td>7.7</td><td>41.1</td><td>61.6</td><td>78.9</td><td>7.7</td><td>38.7</td></tr><tr><td>VWFormer-B0 [57]</td><td>3.7</td><td>5.8</td><td>38.9</td><td>112.4</td><td>77.2</td><td>5.8</td><td>36.2</td></tr><tr><td>EDAFormer-T [60]</td><td>4.9</td><td>5.8</td><td>42.3</td><td>151.7</td><td>78.7</td><td>5.8</td><td>40.3</td></tr><tr><td>CGRSeg-T† [36]</td><td>9.4</td><td>4.8</td><td>42.5</td><td>65.5</td><td>78.3</td><td>4.8</td><td>40.4</td></tr><tr><td>SegMAN-T</td><td>6.4</td><td>6.2</td><td>43.0</td><td>52.5</td><td>80.3</td><td>6.2</td><td>41.3</td></tr><tr><td>Swin UperNet-T [29]</td><td>60.0</td><td>236.0</td><td>44.4</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ViT-CoMer-S [53]</td><td>61.4</td><td>296.1</td><td>46.5</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OCRNet [61]</td><td>70.5</td><td>164.8</td><td>45.6</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Segformer-B2 [56]</td><td>27.5</td><td>62.4</td><td>46.5</td><td>717.1</td><td>81.0</td><td>62.4</td><td>44.6</td></tr><tr><td>MaskFormer [5]</td><td>42.0</td><td>55.0</td><td>46.7</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Mask2Former [6]</td><td>47.0</td><td>74.0</td><td>47.7</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SegNeXt-B [18]</td><td>27.6</td><td>34.9</td><td>48.5</td><td>279.1</td><td>82.6</td><td>34.9</td><td>45.8</td></tr><tr><td>FeedFormer-B2 [43]</td><td>29.1</td><td>42.7</td><td>48.0</td><td>522.7</td><td>81.5</td><td>-</td><td>-</td></tr><tr><td>VWFormer-B2 [57]</td><td>27.4</td><td>46.6</td><td>48.1</td><td>415.1</td><td>81.7</td><td>46.6</td><td>45.2</td></tr><tr><td>EDAFormer-B [60]</td><td>29.4</td><td>32.1</td><td>49.0</td><td>605.9</td><td>81.6</td><td>32.1</td><td>45.9</td></tr><tr><td>CGRSeg-T† [36]</td><td>35.7</td><td>16.5</td><td>47.3</td><td>199.7</td><td>80.2</td><td>16.5</td><td>45.2</td></tr><tr><td>SegMAN-S</td><td>29.4</td><td>25.3</td><td>51.3</td><td>218.4</td><td>83.2</td><td>25.3</td><td>47.5</td></tr><tr><td>Segformer-B3 [56]</td><td>47.3</td><td>79.0</td><td>49.4</td><td>962.9</td><td>81.7</td><td>79.0</td><td>45.5</td></tr><tr><td>SegNeXt-L [18]</td><td>48.9</td><td>70.0</td><td>51.0</td><td>577.5</td><td>83.2</td><td>70.0</td><td>46.5</td></tr><tr><td>VWFormer-B3 [57]</td><td>47.3</td><td>63.3</td><td>50.3</td><td>637.1</td><td>82.4</td><td>63.3</td><td>46.8</td></tr><tr><td>SegMAN-B</td><td>51.8</td><td>58.1</td><td>52.6</td><td>479.0</td><td>83.8</td><td>58.1</td><td>48.4</td></tr><tr><td>Swin UperNet-B [29]</td><td>121.0</td><td>261.0</td><td>48.1</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ViT-CoMer-B [53]</td><td>144.7</td><td>455.4</td><td>48.8</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Segformer-B5 [56]</td><td>84.7</td><td>110.3</td><td>51.0</td><td>1149.8</td><td>82.4</td><td>110.3</td><td>46.7</td></tr><tr><td>VWFormer-B5 [57]</td><td>84.6</td><td>96.1</td><td>52.0</td><td>1139.6</td><td>82.8</td><td>96.1</td><td>48.0</td></tr><tr><td>SegMAN-L</td><td>92.4</td><td>97.1</td><td>53.2</td><td>796.0</td><td>84.2</td><td>97.1</td><td>48.8</td></tr></table>
+
+Table 2. Comparison with state-of-the-art semantic segmentation models on ADE20K, Cityscapes, and COCO-Stuff-164K. FLOPs are calculated at  $512 \times 512$  (ADE20K, COCO-Stuff) and  $2048 \times 1024$  (Cityscapes) resolutions.  
+
+<table><tr><td>Models</td><td>Params(M)</td><td>GFLOP</td><td>Acc</td></tr><tr><td>MiT-B0 [56]</td><td>3.8</td><td>0.60</td><td>70.5</td></tr><tr><td>EFT-T [60]</td><td>3.7</td><td>0.60</td><td>72.3</td></tr><tr><td>MSCAN-T [18]</td><td>4.2</td><td>0.89</td><td>75.9</td></tr><tr><td>SegMAN-T Encoder</td><td>3.5</td><td>0.65</td><td>76.2</td></tr><tr><td>MiT-B2 [56]</td><td>24</td><td>4.0</td><td>81.6</td></tr><tr><td>EFT-B [60]</td><td>26</td><td>4.2</td><td>82.4</td></tr><tr><td>MSCAN-B [18]</td><td>27</td><td>4.4</td><td>83.0</td></tr><tr><td>Swin-T [29]</td><td>28</td><td>4.5</td><td>81.2</td></tr><tr><td>ConvNeXt-T [30]</td><td>28</td><td>4.5</td><td>82.1</td></tr><tr><td>InternImage-T [51]</td><td>30</td><td>5.0</td><td>83.5</td></tr><tr><td>ViM-S [67]</td><td>26</td><td>-</td><td>81.6</td></tr><tr><td>VMamba-T [28]</td><td>29</td><td>4.9</td><td>82.6</td></tr><tr><td>SparX-Mamba-T [32]</td><td>27</td><td>5.2</td><td>83.5</td></tr><tr><td>SegMAN-S Encoder</td><td>26</td><td>4.1</td><td>84.0</td></tr><tr><td>MiT-B3 [56]</td><td>45</td><td>6.9</td><td>83.1</td></tr><tr><td>MSCAN-L [18]</td><td>45</td><td>9.1</td><td>83.9</td></tr><tr><td>Swin-S [29]</td><td>50</td><td>8.7</td><td>83.2</td></tr><tr><td>ConvNeXt-S [30]</td><td>50</td><td>8.7</td><td>83.1</td></tr><tr><td>InternImage-S [51]</td><td>50</td><td>8.0</td><td>84.2</td></tr><tr><td>VMamba-S [28]</td><td>50</td><td>8.7</td><td>83.6</td></tr><tr><td>SparX-Mamba-S [32]</td><td>47</td><td>9.3</td><td>84.2</td></tr><tr><td>SegMAN-B Encoder</td><td>45</td><td>9.9</td><td>85.1</td></tr><tr><td>MiT-B5 [56]</td><td>82</td><td>11.8</td><td>83.8</td></tr><tr><td>Swin-B [29]</td><td>88</td><td>15.4</td><td>83.5</td></tr><tr><td>ConvNeXt-B [30]</td><td>89</td><td>15.4</td><td>83.8</td></tr><tr><td>InternImage-B [51]</td><td>97</td><td>16.7</td><td>84.9</td></tr><tr><td>ViM-B [67]</td><td>98</td><td>-</td><td>83.2</td></tr><tr><td>VMamba-B [28]</td><td>89</td><td>15.4</td><td>83.9</td></tr><tr><td>SparX-Mamba-B [32]</td><td>84</td><td>15.9</td><td>84.5</td></tr><tr><td>SegMAN-L Encoder</td><td>81</td><td>16.8</td><td>85.5</td></tr></table>
+
+Table 3. Comparison with state-of-the-art vision backbones on ImageNet-1K. FLOPs are calculated at  $224 \times 224$  resolution.
+
+Transformer [29]. Regarding semantic segmentation, we trained all models using the MMSegmentation library [7], combining the pre-trained SegMAN Encoder with a randomly initialized decoder. Following the Segformer training protocol [56], we employed standard data augmentation techniques, including random horizontal flipping and random scaling (ratio of 0.5 to 2.0). We implemented dataset-specific random cropping with dimensions of  $512 \times 512$  for ADE20K and COCO-Stuff, and  $1024 \times 1024$  for Cityscapes. We trained the models using the AdamW optimizer for 160K iterations with batch sizes of 16 for ADE20K and COCO-Stuff, and 8 for Cityscapes. The learning rate is set to 1e-6 with 1500 warmup iterations. We evaluated segmentation performance using mean intersection over union (mIoU) and computed FLOPs using the fvcore library [11]. All models were trained on 8 NVIDIA H800 GPUs.
+
+# 4.2. Comparison with state-of-the-art methods
+
+This section presents a comprehensive comparison of our proposed SegMAN with state-of-the-art segmentation methods on three widely used datasets.
+
+Baselines. We compare our method with other strong competitors including EDAFormer [60], VWFormer [57] and CGRSeg [36], Segformer [56], and SegNeXt [18].
+
+Note that the Efficientformer-v2 [25] used in CGRSeg [36] significantly benefits from knowledge distillation from RegNetY-16GF [38] (83.6M parameters, 15.9 GFLOPs) and extended training duration of 450 epochs compared to the conventional 300 epochs, providing a stronger encoder network compared to other methods. To ensure a fair comparison, we retrained Efficientformer-v2 on ImageNet-1K using the same training configurations as Swin [29]. We report results for CGRSeg by fine-tuning this retrained encoder following official implementations.
+
+Results. Table 2 shows the superior performance of SegMAN across all three datasets: ADE20K, Cityscapes, and COCO-Stuff-164K. Among lightweight models, SegMAN-T achieves the best performance, with  $43.0\%$  mIoU on ADE20K,  $80.3\%$  mIoU on Cityscapes, and  $41.3\%$  mIoU on COCO-Stuff, while maintaining comparable computational costs. Our SegMAN-S model achieves significant performance improvements compared to state-of-the-art methods. On the ADE20K benchmark, it surpasses the recently introduced EDAFormer-B [60] by  $2.3\%$  mIoU, and even outperforms the larger SegNeXt-L [18] model, which has 48.9M parameters, by  $0.3\%$  mIoU. The performance gains extend to the COCO-Stuff dataset, where SegMAN-S demonstrates improvements of  $1.6\%$  mIoU over EDAFormer-B and  $1.0\%$
+
+<table><tr><td>Model</td><td>Params (M)</td><td>GFLOPs</td><td>FPS</td><td>mIoU</td></tr><tr><td>Segformer-B0 [56]</td><td>3.8</td><td>125.5</td><td>21.8</td><td>76.2</td></tr><tr><td>SegNext-T [18]</td><td>4.3</td><td>61.6</td><td>25.8</td><td>78.9</td></tr><tr><td>VWFormer-B0 [57]</td><td>3.7</td><td>112.4</td><td>21.1</td><td>77.2</td></tr><tr><td>EDAFourner-T [60]</td><td>4.9</td><td>151.7</td><td>12.7</td><td>78.7</td></tr><tr><td>CGRSeg-T [36]</td><td>9.3</td><td>65.5</td><td>27.6</td><td>78.3</td></tr><tr><td>SegMAN-T</td><td>6.4</td><td>52.5</td><td>34.9</td><td>80.3</td></tr><tr><td>Segformer-B2 [56]</td><td>27.5</td><td>717.1</td><td>10.2</td><td>81.0</td></tr><tr><td>SegNext-B [18]</td><td>27.6</td><td>279.1</td><td>14.1</td><td>82.6</td></tr><tr><td>VWFormer-B2 [57]</td><td>27.4</td><td>415.1</td><td>9.87</td><td>81.7</td></tr><tr><td>EDAFourner-B [60]</td><td>29.4</td><td>605.9</td><td>6.6</td><td>81.6</td></tr><tr><td>CGRSeg-B [36]</td><td>35.6</td><td>199.0</td><td>11.8</td><td>80.2</td></tr><tr><td>SegMAN-S</td><td>29.4</td><td>218.4</td><td>13.8</td><td>83.2</td></tr><tr><td>Segformer-B3 [56]</td><td>46.3</td><td>962.9</td><td>6.7</td><td>81.7</td></tr><tr><td>SegNext-L [18]</td><td>48.9</td><td>577.5</td><td>10.1</td><td>83.2</td></tr><tr><td>VWFormer-B3 [57]</td><td>47.3</td><td>637.1</td><td>6.3</td><td>82.4</td></tr><tr><td>SegMAN-B</td><td>51.8</td><td>479.0</td><td>7.2</td><td>83.8</td></tr><tr><td>Segformer-B5 [56]</td><td>84.7</td><td>1460.4</td><td>3.5</td><td>81.7</td></tr><tr><td>VWFormer-B5 [57]</td><td>84.6</td><td>1139.6</td><td>3.7</td><td>82.4</td></tr><tr><td>SegMAN-L</td><td>92.4</td><td>796.0</td><td>5.2</td><td>84.2</td></tr></table>
+
+mIoU over SegNeXt-L. Notably, SegMAN-S achieves these superior results while reducing computational complexity by over  $20\%$ , requiring only 25.3 GFLOPs compared to EDAFormer-B. On Cityscapes, SegMAN-S achieves  $83.2\%$  mIoU, surpassing EDAFormer-B by  $1.6\%$ . Moreover, SegMAN-B surpasses previous best performing models by  $1.6\%$  on both ADE20K and COCO-Stuff while incurring lower computational cost. On Cityscapes, SegMAN-B improves SOTA by  $0.6\%$  mIoU using  $17\%$  less GFLOPs. Similarly, our largest SegMAN-L model achieves  $+1.2\%$ ,  $+1.0\%$ , and  $+0.8\%$  gains over prior SOTA on ADE20K, Cityscapes, and COCO-Stuff, respectively. The consistent performance gains across different datasets and model scales validate the effectiveness of our proposed SegMAN, which can simultaneously capture global contexts, local details, and multi-scale information through its LASS-based encoder and MMSCopE-based decoder.
+
+Speed Analysis. We benchmark inference speed on Cityscapes [8] using an NVIDIA L40S GPU, averaging Frames Per Second (FPS) over 128 steps with batch size 2. As shown in Table 4, SegMAN-T achieves  $+1.6\%$  mIoU at approximately 3 times the speed of EDAFormer-T, highlighting an excellent balance between performance and speed.
+
+# 4.3. Comparison with state-of-the-art backbones
+
+In Table 3, we evaluate the classification performance of our proposed SegMAN Encoder on ImageNet-1K [9], comparing against two categories of architectures: state-of-the-art segmentation encoders (MiT [56], MSCAN [56], EFT [60]) and representative vision backbones (Swin
+
+Table 4. Comparison of model complexity and inference speed on Cityscapes [8] (2048 × 1024 resolution).  
+
+<table><tr><td>Model Variant</td><td>Params (M)</td><td>GFLOPs</td><td>FPS</td><td>Acc</td><td>mIoU</td></tr><tr><td>SegMAN-S Encoder</td><td>25.5</td><td>21.4</td><td>139</td><td>84.0</td><td>51.3</td></tr><tr><td colspan="6">Replace LASS</td></tr><tr><td>MaxViT [47]</td><td>24.6</td><td>29.8</td><td>96</td><td>83.5 (-0.5)</td><td>47.2 (-4.1)</td></tr><tr><td>ACMix [37]</td><td>24.9</td><td>19.3</td><td>104</td><td>83.1 (-0.9)</td><td>48.6 (-2.7)</td></tr><tr><td>PVT [49]</td><td>29.5</td><td>22.0</td><td>169</td><td>82.8 (-1.2)</td><td>49.1 (-2.2)</td></tr><tr><td>BiFormer [66]</td><td>25.2</td><td>30.5</td><td>97</td><td>82.9 (-1.1)</td><td>48.8 (-2.5)</td></tr><tr><td colspan="6">LASS Design</td></tr><tr><td>Parallel Natten and SS2D</td><td>25.5</td><td>21.4</td><td>129</td><td>83.8 (-0.2)</td><td>48.9 (-2.4)</td></tr><tr><td>w/o SS2D residual</td><td>26.1</td><td>21.4</td><td>132</td><td>83.7 (-0.3)</td><td>50.2 (-1.1)</td></tr><tr><td>Stage 4 Attn → SS2D</td><td>30.0</td><td>21.8</td><td>121</td><td>83.7 (-0.3)</td><td>50.6 (-0.7)</td></tr><tr><td colspan="6">SS2D within LASS</td></tr><tr><td>Replace SS2D w SRA [49]</td><td>26.0</td><td>22.3</td><td>142</td><td>83.7 (-0.3)</td><td>50.6 (-0.7)</td></tr><tr><td>Replace SS2D w L-Attn [22]</td><td>27.6</td><td>21.8</td><td>150</td><td>83.6 (-0.4)</td><td>49.5 (-1.8)</td></tr><tr><td>Remove SS2D</td><td>25.0</td><td>20.9</td><td>162</td><td>83.6 (-0.4)</td><td>47.4 (-3.9)</td></tr><tr><td colspan="6">Natten within LASS</td></tr><tr><td>Replace Natten w Conv</td><td>25.8</td><td>20.7</td><td>160</td><td>83.1 (-0.9)</td><td>49.5 (-1.8)</td></tr><tr><td>Replace Natten w Swin [29]</td><td>30.0</td><td>21.6</td><td>147</td><td>83.5 (-0.5)</td><td>50.3 (-1.0)</td></tr><tr><td>Remove Natten [19]</td><td>24.9</td><td>25.9</td><td>100</td><td>83.6 (-0.4)</td><td>49.8 (-1.5)</td></tr></table>
+
+Table 5. Ablation studies and comparison of token mixers for SegMAN encoder. FLOPs and FPS are calculated or measured at  $512 \times 512$  resolution using the encoder.
+
+Transformer [29], ConvNeXt [30], InternImage [51] ViM [67], VMamba [28], SparX-Mamba [32]). SegMAN Encoder variants consistently achieve superior classification accuracy in comparison to architectures of similar computational complexity. Specifically, SegMAN-S Encoder significantly surpasses ConvNeXt-T and MSCAN-B by  $1.9\%$  and  $1.0\%$  in Top-1 accuracy, respectively. SegMAN-B Encoder demonstrates similar improvements over Swins and MSCAN-L by  $1.9\%$  and  $1.2\%$ , respectively. The promising performance across model variants demonstrates the benefits of integrating local attention with state space models to effectively capture both fine-grained details and global contextual information. A more detailed comparison can be found in the supplementary material.
+
+# 4.4. Ablation studies on SegMAN encoder
+
+We systematically evaluate SegMAN Encoder's architectural design on ImageNet-1K [9] and ADE20K [65]. Our LASS token mixer integrates Neighborhood Attention (Natten [19]) and 2D-Selective-Scan (SS2D), with a residual connection added to SS2D to fuse global and local contexts. In Stage 4 blocks, we replace SS2D with global attention for enhanced high-level feature modeling.
+
+To validate these architectural decisions, we conduct comprehensive ablation studies examining: (1) comparative performance against alternative token mixers, (2) design choices of LASS, and (3) the impact of individual components within the token mixer. In all experiments, including token mixer replacements, we maintain consistent model complexity by adjusting channel dimensions to achieve comparable parameter counts and computational costs. Table 5 presents our findings.
+
+Alternative token mixers. We evaluate the effectiveness of LASS by replacing it with recent token mixers
+
+<table><tr><td>Model</td><td>Params (M)</td><td>GFLOPs</td><td>mIoU (%)</td></tr><tr><td>UPerNet [54]</td><td>27.1</td><td>38.7</td><td>50.4</td></tr><tr><td>MLP Decoder [56]</td><td>25.0</td><td>32.7</td><td>50.3</td></tr><tr><td>Ham Decoder [18]</td><td>25.3</td><td>29.5</td><td>50.4</td></tr><tr><td>VWFormer Decoder [57]</td><td>28.2</td><td>40.7</td><td>50.5</td></tr><tr><td>CGRHead [36]</td><td>44.2</td><td>24.5</td><td>50.3</td></tr><tr><td>EDAFormer Decoder [60]</td><td>28.5</td><td>26.2</td><td>50.2</td></tr><tr><td>SegMAN-S Decoder</td><td>29.4</td><td>25.3</td><td>51.3</td></tr></table>
+
+Table 6. Comparison of complexity and accuracy of segmentation decoders. FLOPs are calculated at  $512 \times 512$  resolution.
+
+(MaxViT [47], ACMix [37], PVT [49], and BiFormer [66]). Note that we implement MaxViT's Block-SA and Grid-SA modules in series to serve as a token mixer. As shown in Table 5, our LASS achieves consistent and substantial improvements across both classification and segmentation tasks while maintaining competitive computational costs. The performance gains stem from the complementary nature between Natten, which models fine-grained local features, and SS2D, which efficiently models global contexts.
+
+Alternative LASS mixer designs. We explore alternative methods to integrate SS2D and Natten together in LASS. Results indicate that parallel arrangement of Natten and SS2D and removing the residual connections for SS2D lead to substantial performance drops in both classification accuracy and segmentation mIoU.
+
+SS2D and Natten. We analyze the two key components of LASS through systematic ablations. When SS2D is replaced with alternative global attention mechanisms, performance consistently decreases: SRA [49]  $(-0.3\%)$  accuracy,  $-0.7\%$  mIoU) and Linear Attention [22]  $(-0.4\%)$  accuracy,  $-1.8\%$  mIoU). Likewise, replacing Natten with either convolutions  $(-0.9\%)$  accuracy,  $-1.8\%$  mIoU) or Shifted-Window Attention [29]  $(-0.5\%)$  accuracy,  $-1.0\%$  mIoU) leads to significant performance degradation, demonstrating the effectiveness of our component choices.
+
+# 4.5. Ablation studies on SegMAN decoder
+
+We investigate both the overall effectiveness of our proposed decoder and the performance of individual components of the MMSCopE module using the ADE20K dataset [65]. For a fair comparison, we ensure that different model variants have comparable computational complexity by adjusting the number of channels. Here, all ablations are conducted using the SegMAN-S decoder.
+
+Decoder comparisons and encoder generalization. Table 6 evaluates decoder efficacy under controlled conditions, where all models employ our SegMAN-S Encoder. We benchmark against decoders from representative methods [18, 36, 56, 57, 60] with matched model sizes and computational budgets—UPerNet's channel dimensions were adjusted to ensure equivalent GFLOPs. Our decoder achieves superior mIoU.
+
+Notably, integrating our SegMAN Encoder with third
+
+<table><tr><td>Model Variant</td><td>Params (M)</td><td>GFLOPs</td><td>FPS</td><td>mIoU</td></tr><tr><td>SegMAN-S</td><td>29.4</td><td>25.3</td><td>128</td><td>51.3</td></tr><tr><td colspan="5">Scan Strategy</td></tr><tr><td>Independent scans</td><td>28.7</td><td>25.9</td><td>65 (-63)</td><td>51.5 (+0.2)</td></tr><tr><td colspan="5">Multi-scale feature</td></tr><tr><td>w/o 16×16</td><td>28.9</td><td>24.8</td><td>130 (+2)</td><td>51.0 (-0.3)</td></tr><tr><td>w/o 32×32</td><td>28.5</td><td>24.5</td><td>132 (+4)</td><td>50.8 (-0.5)</td></tr><tr><td>w/o 64×64</td><td>28.3</td><td>24.3</td><td>131 (+3)</td><td>50.6 (-0.7)</td></tr><tr><td>64×64 only</td><td>26.2</td><td>23.6</td><td>137 (+9)</td><td>50.9 (-0.4)</td></tr><tr><td colspan="5">Compression</td></tr><tr><td>w/o compression</td><td>30.3</td><td>26.7</td><td>98 (-30)</td><td>51.0 (-0.3)</td></tr><tr><td>Channels 1C:2C:4C</td><td>31.2</td><td>25.3</td><td>124 (-4)</td><td>50.8 (-0.5)</td></tr><tr><td colspan="5">Architecture Components</td></tr><tr><td>w/o FFN</td><td>28.4</td><td>24.8</td><td>129 (+1)</td><td>51.2 (-0.1)</td></tr></table>
+
+Table 7. Ablation studies on the MMSCopE module in SegMAN decoder. FLOPs and FPS are measured at  $512 \times 512$  resolution.
+
+party decoders yields significant performance gains:  $+2.1\%$  for VWFormer,  $+1.9\%$  for SegNeXt, and  $+1.2\%$  for EDAFormer over their original encoder-decoder configurations (Table 2). This highlights our encoder's generalization and plug-and-play capability.
+
+Ablation studies on the MMSCopE module. Table 7 presents the impact of various components within the MMSCopE module: (1) Switching to independently scanning each feature map instead of our proposed simultaneous scanning strategy significantly reduces FPS from 128 to 65; (2) The inclusion of multi-scale features yields consistent performance gains. Specifically, removing the feature map at  $16 \times 16$ ,  $32 \times 32$ , or  $64 \times 64$  resolution inside the MMSCopE module results in sub-optimal mIoU, with the removal of the  $64 \times 64$  feature map resulting in the largest performance drop of  $0.7\%$ . Additionally, using the  $64 \times 64$  feature map alone without pixel unshuffling and compression leads to a  $0.4\%$  decline. (3) Channel compression before SS2D proves beneficial, as its removal reduces mIoU by  $0.3\%$ . Alternative channel scaling configuration (1C, 2C, 4C) for the three feature scales yields suboptimal results; (4) The FFN following SS2D provides a marginal benefit of  $0.1\%$ .
+
+# 5. Conclusion
+
+This work presents a new segmentation network with two novel components: SegMAN Encoder, a hierarchical encoder that synergistically integrates state space models with local attention, and MMSCopE, an efficient decoder module that leverages state space models for multi-scale context extraction. SegMAN Encoder enables simultaneous modeling of fine-grained spatial details and global contextual information while MMSCopE adaptively scales with the input resolution while processing multi-scale features. We conduct comprehensive evaluations across multiple benchmarks, which demonstrates that our approach achieves state-of-the-art performance in semantic segmentation.
+
+# Acknowledgement
+
+This work was supported by Hong Kong Research Grants Council under the NSFC/RGC Collaborative Research Scheme (Grant CRS_HKU703/24).
+
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+# Segment Any Motion in Videos
+
+Nan Huang $^{1,2}$  Wenzhao Zheng $^{1}$  Chenfeng Xu $^{1}$
+
+Kurt Keutzer<sup>1</sup> Shanghang Zhang<sup>2</sup> Angjoo Kanazawa<sup>1</sup> Qianqian Wang<sup>1</sup>
+
+$^{1}$ UC Berkeley  $^{2}$ Peking University
+
+# Abstract
+
+Moving object segmentation is a crucial task for achieving a high-level understanding of visual scenes and has numerous downstream applications. Humans can effortlessly segment moving objects in videos. Previous work has largely relied on optical flow to provide motion cues; however, this approach often results in imperfect predictions due to challenges such as partial motion, complex deformations, motion blur and background distractions. We propose a novel approach for moving object segmentation that combines long-range trajectory motion cues with DINO-based semantic features and leverages SAM2 for pixel-level mask densification through an iterative prompting strategy. Our model employs Spatio-Temporal Trajectory Attention and Motion-Semantic Decoupled Embedding to prioritize motion while integrating semantic support. Extensive testing on diverse datasets demonstrates state-of-the-art performance, excelling in challenging scenarios and fine-grained segmentation of multiple objects. Our code is available at https://motion-seg.github.io/.
+
+# 1. Introduction
+
+Segmenting moving objects in videos is crucial for a range of applications, including action recognition, autonomous driving [10, 22, 65], and 4D reconstruction [58]. Many prior works address this problem under terms such as Video Object Segmentation (VOS) or motion segmentation. In this paper, we define our task as moving object segmentation (MOS) – segmenting objects that exhibit observable motion within the video. This definition differs from Video Object Segmentation, which includes objects that have the potential to move even if they remain static in the video, and from motion segmentation, which may also capture background motion, such as flowing water. This task is challenging as it implicitly requires distinguishing between camera motion and object motion, robustly tracking objects despite deformations, occlusions, rapid or transient movement, and segmenting them out with precise, clean masks.
+
+Recently, promptable visual segmentation has made sig-
+
+![](images/07a9fb0c4a3f2bb43614fb8a96b9368c7d223a5dab597264f772a8b7094c63c2.jpg)  
+Images Inputs
+
+![](images/7f4b5e5d77431c72fc717d29d86ea4d6bfef2736025714a0e333d8ee118ce27b.jpg)  
+Long-range Tracks Inputs
+
+![](images/17999daec2d298cfd1cf83cfbfafe2ab68f057c34adf14c8b242985204e42ca2.jpg)  
+Filtered Dynamic Tracks
+
+![](images/fb1d6e5661c408ab80fe66d3b08236f93ef6e30d8fc79449d03efc06992b9b6f.jpg)  
+Dynamic Masks  
+Figure 1. Our method is capable of handling challenging scenarios, including articulated structures, shadow reflections, dynamic background motion, and drastic camera movements, while producing per object level fine-grained moving object masks.
+
+nificant progress. Taking points, masks, or bounding boxes as prompts, SAM2 [51] segments and tracks the associated objects in videos effectively. However, SAM2 cannot natively handle MOS, as it has no mechanism to detect which objects are moving.
+
+We propose an innovative combination of long-range tracks with SAM2 for moving object segmentation to exploit the capabilities of SAM2. First, point tracking captures valuable long-range pixel motion information which is robust to deformation and occlusion, as shown in Fig. 2. At the same time, we incorporate DINO feature [12, 45], to add semantic context as a complementary source of information to support motion-based segmentation. We depart from traditional MOS approaches by training a model on extensive datasets that effectively combines motion and semantic information at a high level. Given a set of long-range 2D tracks, our model is designed to identify those tracks that correspond to moving objects. Once these dynamic tracks are identified, we apply a sparse-to-dense mask densification strategy, which uses an Iterative Prompting method in conjunction with SAM2 [51] to transform the sparse, point-level mask into a pixel-level segmentation. Since the primary objective is moving object segmentation, we em
+
+phasize motion cues while using semantic information as secondary support. To effectively balance these two types of information, we propose two specialized modules. (1) Spatio-Temporal Trajectory Attention. Given the long-term nature of input tracks, our model incorporates spatial attention to capture relationships between different trajectories and temporal attention to monitor changes within individual trajectories over time. (2) Motion-Semantic Decoupled Embedding. We implement special attention mechanisms to prioritize motion patterns and process semantic features in supplementary pathways.
+
+We trained our model on extensive datasets, including both synthetic [19, 28] and real-world data [36]. Due to the self-supervised nature of DINO features [45], our model demonstrates strong generalization capabilities, even when primarily trained on synthetic data. We evaluated our approach on benchmarks [34, 43, 47, 48] that were not part of the training data, and the results show that our method significantly outperforms baseline models in diverse tasks.
+
+While previous MOS methods leverage optical flow [6, 8, 9] to capture motion information, either by identifying different motion groups [6, 46, 53, 59] or by using learning-based models [8, 9, 18, 40, 49] to derive pixel masks from optical flow. However, optical flow is limited to short-range motion and can lose track over extended durations. Other methods [3, 7, 14, 42] rely on point trajectories as motion cues, but traditionally utilize spectral clustering on affinity matrices which struggle with complex motions. Though some methods also attempt to take advantage of appearance cues [24, 61] to help understand motion better, they typically handle different modalities in diverse separate stages, limiting the effective integration of their complementary information. Addressing these limitations, our unified framework achieves threefold integration: long-range trajectory, DINO feature, and SAM2. This design explains the model's exceptional capability in handling challenging cases like articulated motion and reflective surfaces as shown in the Fig. 1, and the superior performance in fine-grained segmentation of multiple objects.
+
+In summary, we make the following contributions:
+
+- We introduce an innovative combination of long-range tracks with SAM2, which enables efficient mask densification and tracking across frames.  
+- To obtain motion labels for trajectories, we propose a method that differs from traditional affinity matrix-based approaches and introduce the Motion-Semantic Decoupled Embedding, which enables a more effective integration of motion and semantic information, enhancing track-level segmentation by balancing these cues.  
+- Extensive results on multiple benchmarks demonstrate the effectiveness of our method, particularly in fine-grained moving object segmentation.
+
+![](images/ab45f340d5aba1c22638bfe22ecbb3971cec194d68e95e81ce73d22508750aaa.jpg)  
+Figure 2. The effectiveness of long-range tracks. Over longer periods of time, if a moving object experiences factors such as occlusion or changes in lighting, it can negatively affect the tracking performance of optical-flow-based methods for that object.
+
+# 2. Related Work
+
+Flow-based Moving Object Segmentation. Traditionally, optical flow based methods [6, 46, 53, 59] segment moving objects by grouping motion cues to create a moving object mask. These methods typically employ iterative optimization or statistical inference techniques to estimate motion models and identify motion regions simultaneously. Recently, numerous deep learning-based approaches [8, 9, 18, 37, 40, 49, 62] have used CNN encoders or transformer to extract motion cues from optical flow, followed by decoders to produce the final segmentation. The main distinctions among these methods lie in model architecture; for instance, methods that encode semantic information often utilize multiple CNN encoders to process different data modalities separately. In general, optical-flow-based methods struggle to distinguish independent object motion from apparent motion caused by depth differences. Furthermore, strong brightness changes also adversely affect these methods. Additionally, optical-flow-based methods are limited to short temporal sequences; they perform poorly if objects move slowly or are occluded.
+
+Trajectory-based Moving Object Segmentation. Trajectory-based methods can be typically classified into two categories: two-frame and multi-frame methods. Two-frame methods [3, 14, 25] generally estimate motion parameters by solving an iterative energy minimization problem, which are recently powered with various convolutional neural network (CNN) models [54, 74]. Multi-frame methods, in contrast, often utilize spectral clustering based on affinity matrices. These matrices are derived through techniques such as geometric model fitting [2, 23, 32, 63], subspace fitting [17, 50, 55, 57], or pairwise motion affinities that integrate motion and appearance information [7, 24, 30, 42]. Recent work has
+
+![](images/793c8605cb0f9ea599925f309d49bb867c5c5df877a34c5ad62678ba59658385.jpg)
+
+![](images/0da4e64087b6fcecac7b640e67dd85abdd8f2fe3b1e7db97b2a135266d5da7f9.jpg)  
+Figure 3. Overview of Our Pipeline. We take 2D tracks and depth maps generated by off-the-shelf models [15, 66] as input, which are then processed by a motion encoder to capture motion patterns, producing featured tracks. Next, we use tracks decoder that integrates DINO feature [45] to decode the featured tracks by decoupling motion and semantic information and ultimately obtain the dynamic trajectories(a). Finally, using SAM2 [51], we group dynamic tracks belonging to the same object and generate fine-grained moving object masks(b).
+
+focused on the search for more effective motion models. For instance, [1] uses the trifocal tensor to analyze point trajectories, arguing that it provides more reliable matches over three images than fundamental matrices can over two. However, the trifocal tensor also poses challenges: it is difficult to optimize and prone to failure when the three camera positions are nearly collinear[44]. Other studies [27, 64] have proposed geometric model fusion techniques to combine different models. Some recent work has explored integrating multiple motion cues [21, 41]. For example, [24] investigates combining point trajectories and optical flow, using well-crafted geometric motion models to fuse the two affinity matrices through co-regularized multi-view spectral clustering. However, these approaches still face inherent issues due to their reliance on affinity matrices. They tend to capture only local similarities, leading to poor global consistency, resulting in inconsistent segmentation. Furthermore, affinity matrices is difficult to capture dynamic changes in motion features like speed and direction over time. In contrast, we address the challenge of capturing motion similarities across diverse motion types.
+
+Unsupervised Video Object Segmentation. Unsupervised Video Object Segmentation (VOS) aims to automatically identify and track salient objects in raw video footage, while semi-supervised VOS relies on first-frame ground truth annotations to segment objects in subsequent frames [47, 48]. In this work, we focus on Unsupervised VOS, referred to here simply as "VOS". Recently, many approaches [68, 71] have combined motion and appearance information. For instance, MATNet [73] introduces a motion-attentive transition model for unsupervised VOS, leveraging
+
+ing motion cues to guide segmentation with a primary focus on appearance. RTNet [52] presents a method based on reciprocal transformations, using the consistency of object appearance and motion between consecutive frames to achieve segmentation. FSNet [26] employs a full-duplex strategy with a dual-path network to jointly model both appearance and motion. Overall, VOS generally targets salient objects in videos, regardless of whether the object is moving. Although many VOS methods incorporate motion information, it is often not their primary focus.
+
+# 3. Method
+
+Our objective is, given a video, to identify moving objects and generate pixel-level dynamic masks. Fig. 3 provides an overview of our pipeline. The central insight is that long-range tracks not only capture motion patterns that facilitate video understanding but also offer long-range prompts essential for promptable visual segmentation. Thus, we use long-range point tracks as motion cues, serving as the primary input in Sec. 3.1, where we apply spatial-temporal attention to capture context-aware feature. In Sec. 3.2, we further incorporate and decouple the use of semantic information with motion cues to decode features, helping the model predict the final motion labels. After identifying dynamic tracks, we leverage these long-range tracks to prompt SAM2 [51] iteratively, as described in Sec. 3.3.
+
+# 3.1. Motion Pattern Encoding
+
+Point trajectories carry valuable information for understanding motion, and related MOS methods can be typi
+
+cally classified into two categories: two-frame and multiframe methods. However, as discussed in Sec. 2, two-frame methods [3, 14, 25] often suffer from significant temporal inconsistencies and exhibit degraded performance when input flows are noisy. Multi-frame methods, in contrast, often utilize spectral clustering based on affinity matrices. Nevertheless, they remain highly sensitive to noise and struggle to handle global, dynamic, and complex motion patterns effectively.
+
+To address these limitations, and inspired by ParticleSFM [72], we propose a method that leverages long-range point tracks [15], processed through a specialized trajectory processing model, to predict per-trajectory motion labels. As illustrated in Fig. 3, our proposed network adopts an encoder-decoder architecture. The encoder directly processes long-range trajectory data and applies a Spatio-Temporal Trajectory Attention mechanism across trajectories. This mechanism integrates both spatial and temporal cues, capturing both local and global information across time and space, in order to embed the motion pattern of each trajectory.
+
+Given that the accuracy and quality of long-range trajectories significantly impact model performance, we utilize BootsTAP [15] to generate the tracks, which provides a confidence score for each track at each time step, enabling us to mask out low-confidence points. Furthermore, due to the movement of dynamic objects and camera motion, the visibility of long-range tracks can vary over time, as they may be occluded or move out of the frame. This variability in visibility and confidence makes each trajectory data highly irregular, motivating our use of a transformer model, inspired by sequence modeling approaches in natural language processing [56, 72], to handle the data effectively.
+
+Our input data comprises long-range trajectories, with each trajectory consisting of normalized pixel coordinates  $(u_{i},v_{i})$ , visibility  $\rho_{i}$  and confidence scores  $c_{i}$ , where  $i\in$  (0, time). Masks  $\mathcal{M}_i$  is applied to indicate points where the pixel coordinates are either invisible or low-confidence. Additionally, we integrate monocular depth maps  $d_{i}$  estimated by Depth-Anything [66], which, despite some noise, provide valuable insights into the underlying 3D scene structure, enhancing understanding of spatial layout and occlusions. To further enrich the input data and strengthen temporal motion cues, we compute frame-to-frame differences in both trajectory coordinates  $(\Delta u_{i},\Delta v_{i})$  and depth  $\Delta d_{i}$  for adjacent frames.
+
+Since adjacent sampling points in coordinates can lead to oversmoothing of spatially close features, we draw inspiration from NeRF [39] to address this issue. Specifically, we apply frequency transformations for positional encoding to better capture fine-grained spatial details.
+
+The final augmented trajectories pass through two MLPs to generate intermediate features, which are then fed into
+
+the transformer encoder. Given the long-range nature of the input data, we propose a Spatio-Temporal Trajectory Attention for our encoder  $\mathcal{E}$ , interleaves attention layers that operate alternately across track and temporal dimensions [4, 29]. This design allows the model to capture both the temporal dynamics within each trajectory and the spatial relationships across different trajectories. Finally, to obtain a feature representation for each entire trajectory rather than individual points, we perform max-pooling along the temporal dimension, following [72]. This process yields a single feature vector for each trajectory, naturally forming a high-dimensional featured track that implicitly captures the unique motion pattern of each trajectory.
+
+# 3.2. Per-trajectory Motion Prediction
+
+Though we encoded motion pattern in Sec. 3.1, it is still challenging to distinguish moving objects based solely on motion cues, because learning to differentiate between object motion and camera motion from highly abstracted trajectories is difficult for the model. Providing the model with texture, appearance, and semantic information can simplify this task by helping it understand which objects are likely to move or be moved. Some approaches directly apply semantic segmentation models [5, 20, 67, 70] where potentially moving pixels are identified based on semantic labels. While these methods can be effective in specific scenarios, they are intrinsically limited for general moving object segmentation, as they depend entirely on predefined semantic classes. Recently, many MOS [61, 69] and VOS [11, 33, 35] methods combine appearance information and motion cues, but they do so in two separate stages, often using RGB images to refine masks. However, relying on raw RGB data may fail to capture high-level information, and applying the two modalities in separate stages limits the effective integration of their complementary information.
+
+To address these limitations, we incorporate DINO features predicted by DINO v2 [45], a self-supervised model, which helps generalize the inclusion of appearance information. However, we observed that simply introducing DINO features as input makes the model overly reliant on semantics as shown in Fig. 8 and discussed in Sec. 4.5, reducing its ability to differentiate between moving and static objects within the same semantic category. To overcome this issue, we propose a Motion-Semantic Decoupled Embedding, enabling the transformer decoder  $\mathcal{D}$  to prioritize motion information while still considering semantic cues.
+
+We obtain the final embedded featured tracks  $\mathcal{P}$  through the process described in Sec. 3.1:
+
+$$
+\mathcal {P} = \mathcal {E} ((\gamma (u), \gamma (v), \gamma (\Delta u), \gamma (\Delta v), d, \Delta d, \rho , c), \mathcal {M}). \tag {1}
+$$
+
+We then design a transformer-based decoder, where the encoder layer performs attention only on the embedded featured tracks, which contain motion information exclusively.
+
+After computing the attention-weighted feature, we concatenate the DINO feature and pass this concatenated feature through a feed-forward layer. In the decoder layer, self-attention is still applied only to the motion features; however, multi-head attention is used to attend to a memory that includes semantic information. Finally, we apply a sigmoid activation function to produce the final output, yielding the predicted label for each trajectory.
+
+We then compute the loss between these predicted labels and per-track ground truth labels using a weighted binary cross-entropy loss [72]. We assign ground truth labels to each trajectory by checking if the sampled point coordinates lie within the ground truth dynamic masks. If a point falls inside the mask, it is labeled as dynamic.
+
+# 3.3. SAM2 Iterative Prompting
+
+As depicted in Fig. 3, after obtaining the predicted label of each trajectory and filter dynamic trajectories, we use these trajectories as point prompts for SAM2[51] with an iterative, two-stage prompting strategy. The first stage focuses on grouping trajectories belonging to the same object and storing the trajectories of each distinct object in memory. In the second stage, this memory is used as a prompt for SAM2 [51] to generate dynamic masks.
+
+The motivation behind this approach is twofold. First, it is necessary because SAM2 requires object IDs as input. However, if we assign the same object ID to all dynamic objects (e.g., assigning 1 to represent all dynamic objects), SAM2 would struggle to simultaneously segment multiple objects that share the same ID. Second, this method offers the benefit of achieving finer-grained segmentation.
+
+In the first stage, we select the time frame with the maximum number of visible points and locate the densest point among all visible points in that frame. This point serves as the initial prompt for SAM2 [51], which then generates an initial mask for that frame. After generating this mask, we apply dilation to expand its boundaries, excluding all points within the expanded mask area to remove edge points and assume that these points belong to the same object. We then proceed to the next frame with the highest number of visible points and repeat this process until the remaining visible points across all frames are too few to process. The trajectories identified as belonging to the same object are stored in memory, with unique object IDs assigned to each. We only save the points within the undilated mask for each object.
+
+In the second stage, we use this memory to refine prompt selection by locating the densest point within the stored trajectories and the two points furthest from this point. Leveraging the long-range nature of trajectories, we prompt SAM2 at regular intervals to prevent it from losing track of the object over extended distances. Since SAM2 may generate partial object masks (e.g., parts of a person's clothing), we perform post-processing on all masks to merge
+
+those that overlap internally or appear within the same mask boundaries. This results in a complete mask for each distinct object.
+
+# 4. Experiments
+
+# 4.1. Implementation Details
+
+Training Dataset. We train our model using three datasets: Kubric[19], Dynamic Replica[28], and HOI4D [36], sampling them at a ratio of  $35\%$ ,  $35\%$ , and  $30\%$  respectively. Kubric [19] is a synthetic dataset composed of sequences of 24 frames showing 3D rigid objects falling under gravity and bouncing. We generate dynamic masks for each sequence based on the motion labels of individual objects. Dynamic Replica [28] is another synthetic dataset, created for 3D reconstruction, that includes long-term tracking annotations and object masks, featuring articulated models of humans and animals. We calculate dynamic masks by analyzing the 3D tracks to determine whether each object is in motion, providing accurate motion segmentation for this dataset. HOI4D [36] is a real-world, egocentric dataset that contains common objects involved in human-object interactions. This dataset provides official motion segmentation masks, making it ideal for real-world training of our model.
+
+Data Sampling. During training, we randomly sample a variable number of tracking points, enhancing the model's robustness to different track counts. For the Dynamic Replica dataset [28], which contains 300 frames, we speed up training by sampling  $1/4$  of the frames at regular intervals randomly. This approach preserves the large camera motion characteristics of the dataset. We find that including the Dynamic Replica dataset is essential for helping the model understand camera motion effectively.
+
+# 4.2. Benchmark and metrics
+
+We evaluate our model using several established datasets for moving object video segmentation. DAVIS17-Moving[13] is a subset of the DAVIS2017 dataset[48], designed specifically for moving object detection and segmentation. In DAVIS17-Moving, all moving instances within each video sequence are labeled, while static objects are excluded. Following the same criteria, we created DAVIS16-Moving as a subset of the DAVIS2016 dataset [47]. Additionally, we report performance on other popular video object segmentation benchmarks, including DAVIS2016[47], SegTrackv2[34], and FBMS-59 [43].
+
+For evaluation, we benchmark our moving object video segmentation performance using region similarity (J) and contour similarity (F) metrics, as outlined in [35, 38, 61].
+
+# 4.3. Moving Object Segmentation
+
+We selected methods that specifically target moving object segmentation as baselines [35, 38, 60, 61, 69]. For
+
+![](images/669c46a5af54aa15521a07f6ab3ca6668d088c00bc66eb7ea926e617422f1044.jpg)  
+Figure 4. Qualitative comparison on DAVIS17-moving benchmarks. For each sequence we show moving object mask results. Our method successfully handles water reflections (left), camouflage appearances (middle), and drastic camera motion (right).
+
+![](images/83a3f772fb61d90f31d3879da5450b9c9ba684ef9b2460f28b6e266ff8fc4eac.jpg)  
+Figure 5. Qualitative comparison on FBMS-59 benchmarks. The masks produced by us are geometrically more complete and detailed.
+
+OCLR [60], we report results for two versions: OCLR-flow, which uses only flow input, and a second version OCLR-TTA that incorporates test-time adaptation on top of OCLR-flow. For RCF [35], the first stage, RCF-stage 1, focuses on motion information, while the second stage, RCF-All, further optimizes the results from the first stage. We report results for both stages. For all baselines, we apply a fully connected conditional random field (CRF) [31] to refine the masks and achieve the best possible results.
+
+Notably, for multi-object scenarios, we follow the common practice [16, 60, 61, 69] of grouping all foreground objects together for evaluation purposes, which we refer to as MOS. Although our approach is capable of generating highly accurate, fine-grained per-object masks, as detailed in Sec. 4.4, we term this second evaluation method
+
+as fine-grained MOS. Table 1 compares the performance of our model with several baseline methods on the MOS task. Our method achieves state-of-the-art F-scores across all datasets, and our region similarity (J) scores are either the best or second-best across multiple datasets, further validating the effectiveness of our approach. Fig. 4 shows our visual results on the DAVIS16-Moving dataset, where our method accurately identifies object boundaries without incorrectly labeling moving backgrounds. Moreover, our masks exhibit strong geometric structure, particularly in challenging scenarios with significant camera motion. Fig. 5 and Fig. 6 present qualitative results on the FBMS-59 and SegTrack v2 benchmarks, respectively. Our method performs exceptionally well in maintaining mask geometry, and even in cases where the RGB images are blurred or of
+
+![](images/c722510269c7038ca8fb740419f559bc1f024475532c9650e2cd62ceda4c85e3.jpg)  
+Figure 6. Qualitative comparison on SegTrack v2 benchmarks. Our method succeeds even under motion blur conditions.
+
+Table 1. Quantitative comparison on MOS task which grouping all foreground objects together for evaluation.  
+
+<table><tr><td rowspan="2">Methods</td><td colspan="2">Model Settings</td><td colspan="3">DAVIS2016-Moving</td><td>SegTrackv2</td><td colspan="2">FBMS-59</td><td colspan="3">DAVIS2016</td></tr><tr><td>Motion</td><td>Appearance</td><td>J&amp;F↑</td><td>J↑</td><td>F↑</td><td>J↑</td><td>J↑</td><td>F↑</td><td>J&amp;F↑</td><td>J↑</td><td>F↑</td></tr><tr><td>CIS [69]</td><td>Optical Flow</td><td>RGB</td><td>66.2</td><td>67.6</td><td>64.8</td><td>62.0</td><td>63.6</td><td>-</td><td>68.6</td><td>70.3</td><td>66.8</td></tr><tr><td>EM [38]</td><td>Optical Flow</td><td>X</td><td>75.2</td><td>76.2</td><td>74.3</td><td>55.5</td><td>57.9</td><td>56.0</td><td>70.0</td><td>69.3</td><td>70.7</td></tr><tr><td>RCF-Stage1 [35]</td><td>Optical Flow</td><td>X</td><td>77.3</td><td>78.6</td><td>76.0</td><td>76.7</td><td>69.9</td><td>-</td><td>78.5</td><td>80.2</td><td>76.9</td></tr><tr><td>RCF-All [35]</td><td>Optical Flow</td><td>DINO</td><td>79.6</td><td>81.0</td><td>78.3</td><td>79.6</td><td>72.4</td><td>-</td><td>80.7</td><td>82.1</td><td>79.2</td></tr><tr><td>OCLR-flow [60]</td><td>Optical Flow</td><td>X</td><td>70.0</td><td>70.0</td><td>70.0</td><td>67.6</td><td>65.5</td><td>64.9</td><td>71.2</td><td>72.0</td><td>70.4</td></tr><tr><td>OCLR-TTA [60]</td><td>Optical Flow</td><td>RGB</td><td>78.5</td><td>80.2</td><td>76.9</td><td>72.3</td><td>69.9</td><td>68.3</td><td>78.8</td><td>80.8</td><td>76.8</td></tr><tr><td>ABR [61]</td><td>Optical Flow</td><td>DINO</td><td>72.0</td><td>70.2</td><td>73.7</td><td>76.6</td><td>81.9</td><td>79.6</td><td>72.5</td><td>71.8</td><td>73.2</td></tr><tr><td>Ours</td><td>Trajectory</td><td>DINO</td><td>89.5</td><td>89.2</td><td>89.7</td><td>76.3</td><td>78.3</td><td>82.8</td><td>90.9</td><td>90.6</td><td>91.0</td></tr></table>
+
+![](images/1cf10bdac16f86d73ecdf37c11ec154568dfacfe1c6f67287da32842ae073bc7.jpg)  
+Figure 7. Qualitative comparison on Fine-grained MOS task which will produce per-object level masks.
+
+low quality, our reliance on long-range trajectories enables accurate identification of moving objects.
+
+# 4.4. Fine-grained Moving Object Segmentation
+
+Building on the initial MOS task, this task not only identifies moving objects but also classifies them within their motion context to generate fine-grained, per-object masks. We evaluate our approach for multi-moving object segmentation specifically on the DAVIS2017-Moving dataset. For a fair comparison, we only include baselines that claim the
+
+Table 2. Quantitative comparison on DAVIS17-Moving dataset for MOS and Fine-grained MOS tasks.  
+
+<table><tr><td rowspan="2">Methods</td><td colspan="2">MOS</td><td colspan="3">Fine-grained MOS</td></tr><tr><td>J↑</td><td>F↑</td><td>J&amp;F↑</td><td>J↑</td><td>F↑</td></tr><tr><td>OCLR-flow [60]</td><td>69.9</td><td>70.0</td><td>44.4</td><td>42.1</td><td>46.8</td></tr><tr><td>OCLR-TTA [60]</td><td>76.0</td><td>75.3</td><td>49.1</td><td>48.4</td><td>49.9</td></tr><tr><td>ABR [61]</td><td>74.6</td><td>75.2</td><td>51.1</td><td>50.9</td><td>51.2</td></tr><tr><td>Ours</td><td>90.0</td><td>89.0</td><td>80.5</td><td>77.4</td><td>83.6</td></tr></table>
+
+ability to perform this task. Table 2 shows that our method significantly outperforms the baselines, demonstrating its superior capability in producing accurate per-object masks. Additionally, Fig. 7 illustrates that, first, our method accurately identifies each object, effectively distinguishing different objects with similar motion patterns. Second, it ensures the completeness of each object mask, handling challenging cases such as articulated human structures and occluded objects while maintaining mask integrity.
+
+# 4.5. Ablation Study
+
+We investigate the effectiveness of our method and its various components on the DAVIS17-Moving and DAVIS16-Moving datasets. The former is used for fine-grained MOS, while the latter focuses on MOS. All models are trained for a full number of epochs.
+
+We conducted several experiments to assess the importance of each component. The w/o DINO configuration ex
+
+![](images/2bcd9e4108526de0d12303cd4b8e9aed5ff3981ff0e0931c1362eb703ac4fc3f.jpg)  
+Figure 8. Visual comparison for the ablation study on two critical and challenging cases. The top sequence shows scenarios involving drastic camera motion and complex motion patterns, while the bottom sequence with both static and dynamic objects of the same category. The experimental setup is detailed in Sec. 4.5.
+
+Table 3. Quantitative comparison for the ablation study on the DAVIS17-Moving and DAVIS16-Moving benchmarks, which evaluate fine-grained MOS and MOS tasks, respectively. The experimental setup is detailed in Sec. 4.5.  
+
+<table><tr><td rowspan="2">Methods</td><td colspan="3">DAVIS17-Moving</td><td colspan="3">DAVIS16-Moving</td></tr><tr><td>J&amp;F↑</td><td>J↑</td><td>F↑</td><td>J&amp;F↑</td><td>J↑</td><td>F↑</td></tr><tr><td>w/o Depth</td><td>69.2</td><td>65.6</td><td>72.8</td><td>82.5</td><td>78.6</td><td>86.4</td></tr><tr><td>w/o Tracks</td><td>19.6</td><td>14.5</td><td>24.7</td><td>20.9</td><td>9.8</td><td>31.9</td></tr><tr><td>w/o DINO</td><td>65.0</td><td>62.1</td><td>67.9</td><td>75.5</td><td>71.4</td><td>79.5</td></tr><tr><td>w/o MOE</td><td>72.0</td><td>68.7</td><td>75.4</td><td>81.8</td><td>81.0</td><td>82.7</td></tr><tr><td>w/o MSDE</td><td>63.0</td><td>59.3</td><td>66.7</td><td>78.2</td><td>77.3</td><td>79.1</td></tr><tr><td>w/o PE</td><td>66.4</td><td>64.7</td><td>68.2</td><td>82.0</td><td>81.5</td><td>82.5</td></tr><tr><td>w/o ST-ATT</td><td>65.5</td><td>61.9</td><td>69.1</td><td>78.3</td><td>74.3</td><td>82.4</td></tr><tr><td>Ours-full</td><td>80.5</td><td>77.4</td><td>83.6</td><td>89.1</td><td>89.0</td><td>89.2</td></tr></table>
+
+cludes DINO features entirely during training, while w/o MOE (Motion-only Encoding) concatenates DINO features with motion cues before the motion encoder, allowing both the encoder and decoder layers to incorporate DINO information throughout. w/o MSDE (Motion-Semitic Decoupled Embedding) excludes DINO features from the motion encoder but concatenates them with the embedded featured tracks from the encoder output, introducing semantic information through self-attention in the tracks decoder. We also test configurations w/o depth and w/o tracks, removing specific inputs to observe their impact on performance. Additionally, w/o PE (Positional Embedding) omits NeRF-like positional embedding in the motion encoder, and w/o ST-ATT (Spatial-temporal Attention) replaces spatial-temporal attention with conventional attention.
+
+Table 3 presents the quantitative results. We find that excluding depth as input or positional encoding impacts performance less than other components, but it still falls significantly short of the best results. When tracks are removed and only DINO features and depth maps are used, performance drops drastically, indicating that the model struggles to learn effectively without trajectory-based information. We further analyze the key components in two chal
+
+lenging scenarios presented below.
+
+Drastic Camera Motion. We observed that in highly challenging scenes, such as those with drastic camera movement or rapid object motion, relying solely on motion information is insufficient. As shown in the upper part of Fig. 8, the colored points represent dynamic points predicted by the model, while the hollow points indicate invisible points at that moment. In this example, without DINO feature information, the model incorrectly classifies the stationary road surface as dynamic, despite the fact that the road lacks the ability to move. This information can be effectively supplemented by incorporating DINO features. Additionally, we found that adding spatial-temporal attention within the motion encoder is particularly beneficial in these difficult scenarios, as it provides the model with richer motion information to capture the long-range motion patterns of tracks, as illustrated in Fig. 8.
+
+Distinguishing Moving and Static Objects of the Same Category. Results show that excluding DINO features entirely results in a performance drop, and the manner in which these features are integrated significantly affects the model's output. Simply incorporating DINO as an input during the motion encoding stage causes the model to rely heavily on semantic information, often leading it to assume that objects of the same type share the same motion state. In contrast, our Motion-Semantic Decoupled Embedding architecture effectively reduces this over-reliance on semantics, allowing the model to differentiate between moving and static objects within the same category, as illustrated in the lower part of Fig. 8.
+
+# 5. Conclusion
+
+In this work, we present a novel approach that leverages long-range tracks which departs from traditional affinity matrix-based methods. Trained on extensive datasets, our model accurately identifies dynamic tracks, which, when combined with SAM2, produce precise moving object masks. Our carefully designed model architecture is tailored to handle long-range motion information while effectively balancing motion and appearance cues. Experiments show that our method achieves state-of-the-art results across multiple benchmarks, with particularly strong performance in per-object-level segmentation.
+
+# 6. Limitation
+
+Our current model utilizes off-the-shelf long-range tracking estimators, whose accuracy can greatly influence overall performance, as shown in Tab. 3. However, we believe that our novel approach to moving object segmentation can, in turn, enhance the performance and understanding of these estimators, creating a mutually beneficial improvement.
+
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+the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 879-888, 2019. 4, 5, 6, 7  
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CVPR/2025/Segment Any-Quality Images with Generative Latent Space Enhancement/full.md

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+# Segment Any-Quality Images with Generative Latent Space Enhancement
+
+Guangqian Guo $^{1,2}$  Yong Guo $^{2}$  Xuehui Yu $^{3}$  Wenbo Li $^{4}$  Yaoxing Wang $^{1}$  Shan Gao $^{1}$ $^{1}$ Northwestern Polytechnical University  $^{2}$ Huawei  $^{3}$ Tencent  $^{4}$ Huawei Noah's Ark Lab  
+ $\{\mathrm{guogq}21, \mathrm{wangyx}24\} @$ mail.nwpu.edu.cn  $\{\mathrm{guoyongcs, fenglinglwb}\} @$ gmail.com  
+xuehuiyu@tencent.com gaoshan@nwpu.edu.cn
+
+# Abstract
+
+Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world scenarios. To address this, we propose GleSAM, which utilizes Generative Latent space Enhancement to boost robustness on low-quality images, thus enabling generalization across various image qualities. Specifically, we adapt the concept of latent diffusion to SAM-based segmentation frameworks and perform the generative diffusion process in the latent space of SAM to reconstruct high-quality representation, thereby improving segmentation. Additionally, we introduce two techniques to improve compatibility between the pre-trained diffusion model and the segmentation framework. Our method can be applied to pre-trained SAM and SAM2 with only minimal additional learnable parameters, allowing for efficient optimization. We also construct the LQSeg dataset with a greater diversity of degradation types and levels for training and evaluating the model. Extensive experiments demonstrate that GleSAM significantly improves segmentation robustness on complex degradations while maintaining generalization to clear images. Furthermore, GleSAM also performs well on unseen degradations, underscoring the versatility of our approach and dataset.
+
+# 1. Introduction
+
+Accurate object detection and segmentation [3, 12, 15, 16, 18, 26, 51, 55] in diverse scenarios is a fundamental task for various high-level visual applications, such as robotics and autonomous driving. The recently developed Segment Anything Models (SAMs), including SAM [27] and SAM2 [44], serving as a foundational model, have gained significant influence within the community [4, 5, 10, 16, 36, 37] due to their outstanding zero-shot segmentation abilities.
+
+Images w/ various quality
+
+![](images/49f2c4db074d74d67e34459a85c177f98ffb9c08334230d23f821ac0bd067b01.jpg)  
+Clear
+
+![](images/461cea2f23efb6afb3326b3c8c9d62cf0b204b666015c8ab26a80b6927a6f933.jpg)
+
+![](images/e9ed58cab824c0e18a60c394a32fda2ce29ca15d7ccf4993db7e900bcfc5e95e.jpg)  
++ Gaussian N.
+
+![](images/7e3040ff0174a0f7e975f3b7a859b5c7927eaa64c4d85f4c9a717ab9e01a3836.jpg)  
++Re-sam.N.  
++ Noise degree  
+Figure 1. The comparison of qualitative results on low-quality images with varying degradation levels from an unseen dataset. To generate images with different degradation levels, we progressively added Gaussian Noise, Re-sampling Noise, and more severe Gaussian noise to an image. Results indicate that the baseline SAM [27] shows limited robustness to degradation. Although RobustSAM [8] retains some resilience against simpler degradations, it struggles with more complex and unfamiliar degradations. In contrast, our method consistently demonstrates strong robustness across images of varying quality.
+
+Qualitative Comparison
+
+![](images/481518b376a084d4bb4abec12805a743984c20be8370bc25d0aee95030f3f499.jpg)
+
+![](images/d8e7fdb1725d0301d1f483cebac8e4392d8f4dbf7ec711cbe3918604439820fc.jpg)
+
+![](images/b1dcd6f329a53a9f575d3e4f9ebe4bd81371c1b788b1f744085f01caf98b8b91.jpg)
+
+![](images/5ea1ae54c609584cdabd1937716b2cbaae64f10948b611e01d3ae1a76ef2ab6e.jpg)  
+SAM
+
+![](images/02514ad182f5a777b20db3784277a65f9e6a6e1d0bbf10a5a930b08064a630f6.jpg)
+
+![](images/756bd690003e290240abcc807ff9bd37ab3261aa84daead529eaa111e0e52a5e.jpg)
+
+![](images/6a6af336cd5d389e2ef6ce89830339dc7283cfec0ee907f606e8e10035c49f35.jpg)
+
+![](images/a88fad9c423a596276dbac1ad85145c9460a24e164dcdd4986f655fb327c31a9.jpg)  
+RobustSAM
+
+![](images/6dd471993be710accc10c6fc1edd701fd625ad7fc422ba8e4d83ac2ff03e46a7.jpg)
+
+![](images/9ce9986e329d3f91904a88060855b5395511fa0d3e97a73ffe61f8dc41957b3e.jpg)
+
+![](images/06631936e528d876672503e883e2731b7eda4d666e017949ff93375a30798601.jpg)
+
+![](images/509235a06cc95ee61b7aa01bb076118dac9539c7aaa3077070815df0ed31572d.jpg)  
+GleSAM (Ours)
+
+Despite their success, SAMs perform poorly on common low-quality images, such as those degraded by noise, blur, and compression artifacts [22, 42, 47, 56], which are often encountered in real-world scenarios [19, 34, 35]. Previous methods [8, 11] have employed distillation-based consistent learning to enhance degradation-robust features.
+
+Nonetheless, they still face challenges in handling severely degraded low-quality images, as illustrated in Fig. 1. As degradations become more complex (e.g. combining various types of degradation or increasing the level of degradation), the existing SAMs [8, 27] struggle to accurately segment edges and complete target areas, leading to incorrect segmentation. We analyze that it is caused by the limited feature representation for degraded images. The visualizations in Fig. 2 reveal that SAM's latent features from severely degraded images contain excessive noise, compromising the original representations and subsequently impacting the predictions of the decoder. Furthermore, the large gap between low-quality and high-quality features complicates consistency learning [38] in previous works [8], hindering performance improvement. Thus, achieving high-quality latent feature representations and robust segmentation across varying image quality, especially for degraded images, remains challenging.
+
+The recently developed generative Diffusion Models (DM) [20, 50], especially the large-scale pre-trained Latent Diffusion Models (LDM) [45] have demonstrated powerful content generation capabilities. Having been trained on internet-scale data [46], LDM that proceed diffusion and denoising in latent space, possess powerful representation prior, which can be well explored to enhance the latent representation of segmentation models. This inspires us to take full advantage of the generative ability of pre-trained diffusion models and incorporate it into the latent space of SAMs to enhance low-quality features, thus promoting accurate segmentation in low-quality images.
+
+To this end, we propose GleSAM, which reconstructs high-quality features (Fig. 2 (d)) in SAM's latent space through generative diffusion, enabling accurate segmentation across any-quality images. Starting with low-quality features, high-quality representations are generated through single-step denoising. To integrate LDM generative knowledge, we incorporate a pre-trained U-Net from LDM with learnable LoRA layers [21] to align with segmentation-specific features. Furthermore, to improve compatibility between the pre-trained diffusion model and the segmentation framework, we introduce two effective techniques: Feature Distribution Alignment (FDA) and Channel Replicate and Expansion (CRE). These techniques bridge feature distribution and structural alignment gaps between models. Built upon SAM and SAM2, GleSAM leverages the generalization of pre-trained segmentation and diffusion models, with a few learnable parameters added, and can be efficiently trained within 30 hours on four GPUs.
+
+In terms of data, we constructed LQSeg based on existing datasets [9, 17, 31, 32, 49] to train and assess segmentation models on low-quality images. LQSeg incorporates a greater diversity of degradation types than previous methods [8], combining basic degradation models (e.g.,
+
+![](images/2b9b1ca303cad7de372bff2d460425e9e50f0438deea06818f5d986e08316abc.jpg)
+
+![](images/b062ae780362b86fa98a8d5a9f209228110ce58506097f6d86da7e3de8b70343.jpg)  
+(a) LQ-Img
+
+![](images/fbf39ec8b1eb98d7da7db4dd0b9347e27387082ec9e54b9f6c2c1f684380fe18.jpg)
+
+![](images/09d9094775c7cd7c74313bda9670654047ef50614d319cd5e15d548937f0bbf4.jpg)  
+(b) LQ-Feat
+
+![](images/acca9c60fa52591e1da4e4befd04f3dec0649e835d1b5004d892319dce191ed6.jpg)
+
+![](images/5ff0fd44433a47b5afe7eb73377bd482b0304525e283909d73232dbae51a4299.jpg)  
+(c) HQ-Feat  
+Figure 2. The visualization of latent features: (a) low-quality (LQ) images, (b) the SAM's latent features extracted from LQ images, which contain excessive noise and compromise the original representations, (c) the high-quality (HQ) features of the corresponding clear images, which are more salient than LQ ones, and (d) enhanced representation by our GleSAM.
+
+![](images/a48059ccce8f174be1092efa234cc6a18201cd4515b8ed8e79aab810434df659.jpg)
+
+![](images/a3b764976eb83a4aff2ee0d30c53f4045441732af9e434828fa16e4dc7539d53.jpg)  
+(d) Ours-Feat
+
+noise and blur) to simulate complex and real-world noise [54, 63]. We also introduce three degradation levels for a more comprehensive evaluation. We hope LQSeg will inspire the development of more robust segmentation models and contribute to future research. Overall, our contributions are summarized as:
+
+- We propose GleSAM, a SAM-based framework incorporating generative latent space enhancement, to generalize across images of any quality. GleSAM exhibits significantly improved robustness, particularly for low-quality images with varying degradation levels.  
+- Two effective techniques: FDA and CRE, are introduced to bridge feature distribution and structural gaps between the pre-trained latent diffusion model and SAM.  
+- We also construct the LQSeg dataset which includes a wide range of degradation types and levels, to effectively train and evaluate the model.  
+- Extensive experiments show that our method performs excellently on low-quality images with varying degrees of degradation while maintaining generalization to clear images. Additionally, our method achieves strong performance on unseen degradations, highlighting the adaptability of both our framework and dataset.
+
+# 2. Related Work
+
+# 2.1. Segmentation on Low-Quality Images
+
+Executing robust segmentation across various scenarios is a critical issue. Numerous studies [8, 24, 43, 56] have highlighted significant performance degradation in conventional segmentation models and foundational SAMs when confronted with low-quality images with degradation. Many related studies [11, 14, 24, 29, 43] have been proposed to enhance the robustness of segmentation models against low-
+
+quality data. These methods primarily consider a single type of degradation. Recently, RobustSAM [8] is introduced to enhance the robustness of the SAM against multiple image degradations through anti-degradation feature learning. However, its performance also struggles when dealing with complex degradations. The real-world image noise is often too complex to be modeled by a single degradation [19, 34, 35]. Therefore, robustly segmenting images of any quality remains challenging.
+
+# 2.2. Diffusion Models for Perception Tasks
+
+Recently, diffusion models [20, 39, 45, 50, 62] have garnered significant attention in research, due to their powerful generation capabilities. Numerous studies [1, 2, 6, 23, 33, 40, 53, 60, 64] explore how to extend their applications to a broader range of tasks, such as detection, segmentation, and image reconstruction, etc. For diffusion-based perception tasks, one category of methods [1, 7, 58, 59] reformulate the perception tasks as progressive denoising from random noise. such as DiffusionDet [6] and DiffusionInst [6]. Another route employs the denoising autoencoder pre-trained on the text-to-image generation as a backbone for downstream perception tasks [13, 23, 28, 40, 64]. For example, VPD [64] passes the image through a pre-trained diffusion model and extracts intermediate features for task prediction. Diverging from these existing works, we preserve the original segmentation structure and fine-tune a generative diffusion to enhance the segmentation model's latent representations for accurate segmentation of any quality images.
+
+# 2.3. Segment Anything Model and Variants
+
+Segment Anything Models (SAMs) [27, 44] have gained significant influence within the community due to their outstanding zero-shot segmentation capabilities. SAM [27] can interactively segment any object in an image using visual prompts such as points and bounding boxes. Most recently, the updated SAM2 [44] has been released, showing improved segmentation accuracy and inference efficiency. Their generalization abilities have led to breakthroughs and new paradigms in various downstream tasks [25, 30, 36, 48, 52, 57, 61, 65]. Although SAM is powerful, its performance decreases when facing complex scenarios, such as degraded images [22, 42, 56] and adverse weather conditions [47], which significantly hinders the real-world applications of SAM. Enhancing SAM's capability in such challenging scenarios is a worthwhile research topic.
+
+# 3. Generative Latent Space Enhancement for Any-Quality Image Segmentation
+
+In the following, we explore how to improve SAM's robustness for low-quality images through generative latent space enhancement, thus enabling it to generalize across varying image qualities. The overall framework of the proposed
+
+GleSAM is shown in Fig. 3. To begin, in Sec. 3.1, we propose incorporating diffusion models' generative capabilities into SAM's latent space to effectively and efficiently enhance low-quality feature representations. Next, to improve the compatibility of feature distribution and architecture between the pre-trained diffusion model and SAM, we introduce two techniques: Feature Distribution Alignment and Channel Replicate and Expansion, which are detailed in Sec. 3.2 and Sec. 3.3, respectively. Finally, the overall training method is outlined in Sec. 3.4.
+
+# 3.1. Latent Denoising Diffusion in Segmentation
+
+Recall that diffusion models [20, 45, 50] are a class of probabilistic generative models that progressively add noise to the latent space, and then they learn to reverse this process by predicting and removing the noise. Formally, in LDMs, the forward noise process iteratively adds Gaussian noise with variance  $\beta_{t} \in (0,1)$  to the variable  $z$ . The sample at each time point is defined as:
+
+$$
+z _ {t} = \sqrt {\overline {{\alpha}} _ {t}} z + \sqrt {1 - \overline {{\alpha}} _ {t}} \epsilon , \qquad (1)
+$$
+
+where  $\alpha_{t} = 1 - \beta_{t},\overline{\alpha}_{t} = \prod_{s = 1}^{t}\alpha_{s}$ , and  $\epsilon \in \mathcal{N}(0,\mathrm{I})$ . While the inverse diffusion process is modeled by applying a neural network  $\epsilon_{\theta}(z_t,t)$  to predict the noise  $\hat{\epsilon}$  and recover the original input  $\hat{z}$ . LDMs model the above process in a latent space using a pre-trained Variational AutoEncoder (VAE) and then up-sample the latent output to the original resolution using the VAE decoder, enabling more efficient computations in the training and inference phases.
+
+A similar idea motivates us to introduce the generative latent space denoising process into the SAMs' framework to reconstruct low-quality segmentation features. Let's denote  $\mathcal{E}_{\theta}$  and  $\mathcal{D}_{\theta}$  the segmentation encoder and decoder of SAMs, respectively. As shown in Fig. 3, the input LQ image is first compressed by  $\mathcal{E}_{\theta}$  and generates LQ feature  $z_{L}$ . We consider  $z_{L}$  to be a noisy version of  $z_{H}$ , containing sufficient information to reconstruct a high-quality feature. Instead of the complex multi-step denoising from random noise, we start directly from  $z_{L}$  and forward with a single denoising step. Specifically, based on Eq. 1, the clean latent variable  $z$  can be directly predicted from the model's predicted noise  $\hat{\epsilon}$ , as:
+
+$$
+\hat {z} = \frac {z _ {t} - \sqrt {1 - \overline {{\alpha}} _ {t}} \hat {\epsilon}}{\sqrt {\overline {{\alpha}} _ {t}}}, \tag {2}
+$$
+
+where  $\hat{\epsilon}$  is the prediction of the network  $\epsilon_{\theta}$  with given  $z_{t}$  and t:  $\hat{\epsilon} = \epsilon_{\theta}(z_t;t)$ . We re-parameterize the above generative denoising process to adapt low-quality latent space enhancement in segmentation, as:
+
+$$
+\hat {z} _ {H} = \operatorname {G L E} \left(z _ {L}\right) = \frac {z _ {L} - \sqrt {1 - \bar {\alpha} _ {T}} \epsilon_ {\theta} \left(z _ {L} ; T\right)}{\sqrt {\bar {\alpha} _ {T}}}, \tag {3}
+$$
+
+where we consider low-quality feature  $x_{L}$  as the noised feature and perform one-step denoising at the  $T$ -th diffusion
+
+![](images/6693a538551966b2a531ee42df6c36d781d37e091add082ad8662a033e631f67.jpg)  
+Figure 3. Given an input image, GleSAM performs accurate segmentation through image encoding, generative latent space enhancement, and mask decoding. During training, HQ-LQ image pairs are fed into the frozen image encoder to extract the corresponding HQ and LQ latent features. We then reconstruct high-quality representations in the SAM's latent space by efficiently fine-tuning a generative denoising U-Net with LoRA. Subsequently, the decoder is fine-tuned with segmentation loss to align the enhanced latent representations. Built upon SAMs, GleSAM inherits prompt-based segmentation and performs well on images of any quality.
+
+timestep. The denoised output  $\hat{z}_H$  is expected to be closer to the features extracted from clear images  $z_{H}$ . This single-step process significantly reduces computational overhead, making it more efficient when applied to segmentation models. After that, with  $\hat{z}_H$  as input, the mask decoder can predict more precise masks, as:  $m_p = \mathcal{D}_{\theta}(\hat{z}_H)$
+
+# 3.2. Feature Distribution Alignment
+
+We employ the pre-trained U-Net in LDM as the denoising backbone. However, a significant challenge arises due to the substantial difference between the latent spaces in the original LDM (encoded by VAE) and segmentation models, leading to several technical issues for our application.
+
+Firstly, there is a distribution gap between the two spaces and directly feeding segmentation features into U-Net may prevent it from fully exerting its denoising capabilities, as shown in the right part of Fig. 3. To address this gap, we introduce a Feature Distribution Alignment (FDA) technique. Specifically, we add an adaptation weight  $\gamma$  to scale the segmentation features, adjusting their variance to align more closely with the VAE's latent space. This adjustment ensures that the features are compatible with U-Net's optimal input space, improving the robustness and accuracy of the semantic interpretation and enhancing the denoising capability. The LQ feature denoising process in Eq. 3 can be updated as:
+
+$$
+\hat {z} _ {H} = \mathrm {G L E} (z _ {L}) = \frac {\gamma z _ {L} - \sqrt {1 - \overline {{\alpha}} _ {T}} \epsilon_ {\theta} (\gamma z _ {L} ; T)}{\gamma \sqrt {\overline {{\alpha}} _ {T}}}, \quad (4)
+$$
+
+where we divide by  $\gamma$  to restore its original distribution. We experimentally verified in Sec. 5.4 that this simple operation effectively improves U-Net's denoising performance when applied to segmentation features.
+
+# 3.3. Channel Expansion for Head-tail Layers
+
+Another technical issue arises from the channel mismatch of the head and tail layers between the pre-trained U-Net and the segmentation features. The U-Net in LDMs is designed for 4-channel input and output ( $h \times w \times 4$ ), which does not match the dimension of SAM's latent space ( $h \times w \times 256$ ). We explore various methods to solve this problem (in Sec. 5.4) and empirically find that fine-tuning new head and tail layers or an encoder-decoder for segmentation features is ineffective. This is likely due to difficulties in aligning with the pre-trained model's parameters while preserving its generalization ability. To address this, we propose a Channel Replication and Expansion (CRE) method that replicates and concatenates the pre-trained weights of head and tail layers to match the required channel dimension. During training, the parameters of the head and tail layers remain frozen, while learnable LoRA layers are added to adapt to segmentation features. This approach effectively preserves the pre-trained generalization capacity while minimizing the number of learnable parameters.
+
+# 3.4. Training Method
+
+We employ a two-step fine-tuning process. In the first step, we fine-tune the denoising U-Net to reconstruct high
+
+quality features. In the second step, we fine-tune the decoder with the restored features to further align the feature space for more accurate segmentation.
+
+U-Net finetuning. To adapt the pre-trained U-Net to the segmentation framework while preserving its inherent generalization ability, we employ the LoRA [21] scheme to fine-tune all the attention layers in the U-Net. During this step, we freeze the pre-trained image encoder and U-Net layers and only fine-tune the added LoRA layers. The estimated feature is compared with the corresponding HQ feature  $z_{H}$  by a reconstruction loss, as:
+
+$$
+\mathcal {L} _ {\mathrm {R e c}} = \mathcal {L} _ {\mathrm {M S E}} (\mathrm {G L E} \left(z _ {L}\right), z _ {H}). \tag {5}
+$$
+
+This step significantly enhances performance without fine-tuning SAM's parameters.
+
+Decoder finetuning. Next, we use the reconstructed high-quality features to fine-tune the mask decoder for more precise segmentation. Our experiments demonstrate that finetuning either the entire decoder or only the output tokens with these features further improves segmentation accuracy while maintaining generalization on clear images. Focal Loss and Dice Loss are employed as segmentation loss functions, as:
+
+$$
+\mathcal {L} _ {\text {S e g}} = \mathcal {L} _ {\text {D i c e}} \left(m _ {p}, m _ {g}\right) + \mathcal {L} _ {\text {F o c a l}} \left(m _ {p}, m _ {g}\right), \tag {6}
+$$
+
+where  $m_p$  and  $m_g$  indicate predicted and ground-truth masks respectively.
+
+# 4. Low-Quality Image Segmentation Dataset
+
+We construct a comprehensive low-quality image segmentation dataset dubbed  $LQSeg$  that encompasses more complex and multi-level degradations, rather than relying on a single type of degradation for each image. The dataset is composed of images from several existing datasets with our synthesized degradations. In this section, we first model a multi-level degradation process of low-quality images and then detail the dataset composition.
+
+# 4.1. Multi-level Degradation Modeling
+
+To model a more practical and complex degradation process, inspired by the previous work in image reconstruction [54, 63], we utilize a mixed degradation method. Specifically, the degraded process is modeled as the random combination of the four common degradation models, including Blur, Random Resize, Noise, and JPEG Compression. Each degradation model encompasses various types, such as Gaussian and Poisson noise for Noise, ensuring the diversity of the degradation process.
+
+To enrich the granularity of degradation, we employ multi-level degradation by adjusting the downsampling rates. We employed three different resize rates, i.e., [1, 2, 4], which correspond to three degradation levels from slight
+
+to severe: LQ-1, LQ-2, and LQ-3. More implementation details are illustrated in Supplementary Material.
+
+# 4.2. Dataset Composition
+
+Based on the above multi-level degradation model, we construct LQSeg to train our model and evaluate the segmentation performance on different levels of low-quality images. The images in LQSeg are sourced from several well-known existing datasets in the community with our synthesized degradation. In detail, for the training set, we utilize the entire training sets of LVIS [17], ThinObject-5K [31], and MSRA10K [9] as the source data and procedurally synthesize corresponding low-quality images. The evaluation set is sourced from four subsets, i.e., 1) the whole test sets of ThinObject-5K and LVIS (seen sets), and 2) ECSSD [49] and COCO-val [32] (unseen sets). For each source image, We systematically generate three levels of degraded images to thoroughly assess the model's robustness.
+
+# 5. Experiment
+
+We conduct extensive experiments to verify our method across images of varying quality. All proposed techniques can be applied to SAM and SAM2, referred to as GleSAM and GleSAM2. In practice, our models perform well on low-quality (Tab. 1, 2) and clear images (Tab. 5) and they generalize effectively to unseen degradations (Tab. 3).
+
+# 5.1. Experimental Setup
+
+Implement Details. Our model is trained using the AdamW optimizer with the learning rate of  $1 \times 10^{-4}$  and batch size of 4. The pre-trained U-Net in Stable Diffusion (SD) 2.1-base [45] is adopted as the denoising backbone. We use 3 clicks as SAM's prompts by default. Our approach can be efficiently trained on  $4 \times$  A100 GPUs within approximately 30 hours, during which we fine-tune the U-Net for 100K iterations and the decoder for only 20K iterations.
+
+Evaluation Metrics. We employ three metrics to assess our model's performance, including Intersection over Union (IoU), Dice Coefficient (Dice), and Pixel Accuracy (PA).
+
+# 5.2. Performance Comparisons
+
+In this experiment, we evaluate the performance on the test set of our LQSeg, including seen-set (Tab. 1) and unseen-set (Tab. 2) evaluations. We compare our method with a set of comparison baselines to quantify the performance gains. For SAM-based comparisons, besides the original SAM [27], we also compare with the RobustSAM [8], which has improved robustness on the degraded dataset. Additionally, we compare with two-stage methods, i.e., reconstructing images first with image reconstruction (IR) networks and passing the restored clear images to the SAM and SAM2. We use two state-of-the-art IR networks for comparison: PromptIR [41], and diffusion-based DiffBIR [33].
+
+<table><tr><td rowspan="3">Method</td><td colspan="6">ThinObject-5K</td><td colspan="6">LVIS</td></tr><tr><td colspan="2">LQ-3</td><td colspan="2">LQ-2</td><td colspan="2">LQ-1</td><td colspan="2">LQ-3</td><td colspan="2">LQ-2</td><td colspan="2">LQ-1</td></tr><tr><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td></tr><tr><td>SAM</td><td>0.6285</td><td>0.7286</td><td>0.7054</td><td>0.7939</td><td>0.7527</td><td>0.8343</td><td>0.4041</td><td>0.5005</td><td>0.4886</td><td>0.5838</td><td>0.5325</td><td>0.6282</td></tr><tr><td>RobustSAM</td><td>0.7015</td><td>0.7965</td><td>0.7648</td><td>0.8463</td><td>0.7922</td><td>0.8658</td><td>0.4517</td><td>0.5670</td><td>0.4962</td><td>0.6079</td><td>0.5262</td><td>0.6356</td></tr><tr><td>PromptIR-SAM</td><td>0.6380</td><td>0.7374</td><td>0.7146</td><td>0.8018</td><td>0.7434</td><td>0.8248</td><td>0.4020</td><td>0.4978</td><td>0.4705</td><td>0.5677</td><td>0.5222</td><td>0.6187</td></tr><tr><td>DiffBIR-SAM</td><td>0.7055</td><td>0.7917</td><td>0.7772</td><td>0.8531</td><td>0.7927</td><td>0.8652</td><td>0.5316</td><td>0.6307</td><td>0.5812</td><td>0.6913</td><td>0.6021</td><td>0.7090</td></tr><tr><td>GleSAM (Ours)</td><td>0.7594</td><td>0.8413</td><td>0.8033</td><td>0.8738</td><td>0.8277</td><td>0.8920</td><td>0.5535</td><td>0.6756</td><td>0.5916</td><td>0.7066</td><td>0.6131</td><td>0.7244</td></tr><tr><td>SAM2</td><td>0.7174</td><td>0.8000</td><td>0.7636</td><td>0.8373</td><td>0.7839</td><td>0.8536</td><td>0.5118</td><td>0.6174</td><td>0.5634</td><td>0.6633</td><td>0.6024</td><td>0.7004</td></tr><tr><td>PromptIR-SAM2</td><td>0.7119</td><td>0.7945</td><td>0.7753</td><td>0.8477</td><td>0.7801</td><td>0.8487</td><td>0.5017</td><td>0.6084</td><td>0.5529</td><td>0.6546</td><td>0.5875</td><td>0.6865</td></tr><tr><td>DiffBIR-SAM2</td><td>0.7348</td><td>0.8117</td><td>0.7832</td><td>0.8505</td><td>0.7974</td><td>0.8604</td><td>0.5651</td><td>0.6664</td><td>0.6004</td><td>0.7032</td><td>0.6278</td><td>0.7380</td></tr><tr><td>GleSAM2 (Ours)</td><td>0.7947</td><td>0.8653</td><td>0.8300</td><td>0.8896</td><td>0.8527</td><td>0.9072</td><td>0.5738</td><td>0.6887</td><td>0.6082</td><td>0.7161</td><td>0.6361</td><td>0.7402</td></tr></table>
+
+Table 1. Performance comparison on the test set of Thinobject-5K [31] and LVIS [17] datasets (seen datasets) with different levels of degradation. From LQ-1 to LQ-3, the degree of degradation increases progressively. We report IoU and Dice for comparison. Our GleSAM and GleSAM2 consistently outperform other competitors, especially on the most challenging LQ-3 version. The words with boldface indicate the best results and those underlined indicate the second-best results.  
+
+<table><tr><td rowspan="3">Method</td><td colspan="6">ECSSD</td><td colspan="6">COCO</td></tr><tr><td colspan="2">LQ-3</td><td colspan="2">LQ-2</td><td colspan="2">LQ-1</td><td colspan="2">LQ-3</td><td colspan="2">LQ-2</td><td colspan="2">LQ-1</td></tr><tr><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td></tr><tr><td>SAM</td><td>0.5219</td><td>0.6383</td><td>0.6156</td><td>0.7202</td><td>0.6778</td><td>0.7735</td><td>0.4048</td><td>0.5014</td><td>0.4848</td><td>0.5810</td><td>0.5392</td><td>0.6350</td></tr><tr><td>RobustSAM</td><td>0.6535</td><td>0.7659</td><td>0.7271</td><td>0.8240</td><td>0.7758</td><td>0.8606</td><td>0.4541</td><td>0.5711</td><td>0.4956</td><td>0.6087</td><td>0.5304</td><td>0.6401</td></tr><tr><td>PromptIR-SAM</td><td>0.5277</td><td>0.6442</td><td>0.6109</td><td>0.7188</td><td>0.6752</td><td>0.7726</td><td>0.4006</td><td>0.4969</td><td>0.4784</td><td>0.5753</td><td>0.5257</td><td>0.6219</td></tr><tr><td>DiffBIR-SAM</td><td>0.6675</td><td>0.7652</td><td>0.7408</td><td>0.8244</td><td>0.7737</td><td>0.8501</td><td>0.5260</td><td>0.6263</td><td>0.5742</td><td>0.6918</td><td>0.6112</td><td>0.7080</td></tr><tr><td>GleSAM (Ours)</td><td>0.7332</td><td>0.8282</td><td>0.7944</td><td>0.8719</td><td>0.8257</td><td>0.8931</td><td>0.5495</td><td>0.6705</td><td>0.5900</td><td>0.7055</td><td>0.6158</td><td>0.7257</td></tr><tr><td>SAM2</td><td>0.6534</td><td>0.7540</td><td>0.7292</td><td>0.8157</td><td>0.7625</td><td>0.8417</td><td>0.5176</td><td>0.6230</td><td>0.5681</td><td>0.6692</td><td>0.6018</td><td>0.7000</td></tr><tr><td>PromptIR-SAM2</td><td>0.6490</td><td>0.7526</td><td>0.7175</td><td>0.8082</td><td>0.7519</td><td>0.8351</td><td>0.5088</td><td>0.6153</td><td>0.5579</td><td>0.6600</td><td>0.5905</td><td>0.6914</td></tr><tr><td>DiffBIR-SAM2</td><td>0.7158</td><td>0.8026</td><td>0.7853</td><td>0.8588</td><td>0.8070</td><td>0.8745</td><td>0.5680</td><td>0.6711</td><td>0.6109</td><td>0.7190</td><td>0.6245</td><td>0.7348</td></tr><tr><td>GleSAM2 (Ours)</td><td>0.7335</td><td>0.8258</td><td>0.7976</td><td>0.8726</td><td>0.8175</td><td>0.8853</td><td>0.5724</td><td>0.6867</td><td>0.6165</td><td>0.7246</td><td>0.6337</td><td>0.7371</td></tr></table>
+
+Table 2. Zero-shot performance comparison on the ECSSD [49] and COCO [32] datasets (unseen datasets) with different levels of degradation. These results indicate that GleSAMs possesses significant robustness in zero-shot segmentation across different levels of degradations.
+
+Performance Comparison on Seen Datasets. In Tab. 1, we evaluate the performance of our GleSAM and GleSAM2 on two seen datasets: ThinObject-5K [31] and LVIS [17]. Each dataset contains three levels of degradation. Our method demonstrates superior performance across all degradation levels, effectively handling low-quality images. As the degradation level increases (from LQ-1 to LQ-3), GleSAMs show increasingly significant performance improvements compared to the baseline, highlighting its robustness against challenging degradations.
+
+Performance Comparison on Un-seen Datasets. In Tab. 2, we evaluate the zero-shot segmentation performance of GleSAMs on two unseen datasets: ECSSD [49] and COCO [32], all of which are synthesized with different levels of degradations. GleSAM consistently outperforms other methods, particularly on the most challenging LQ-3 version, underscoring its strong zero-shot generalization capabilities and potential for real-world applications. To further assess segmentation quality, we plot the density distribution maps of image quality and segmentation IoU in Fig. 4. Compared to the baselines, our method achieves overall
+
+improvement and more stable performance on low-quality images, especially for those of inferior quality.
+
+Validation with Other Degradations. To validate the model's generalization on other unseen degradations, we evaluated GleSAM and GleSAM2 on RobustSeg-style degradations [8], with the results presented in Tab. 3. These degradations were not used during training. Our method consistently outperforms SAM and SAM2 and even surpasses RobustSAM which is specifically trained on RobustSeg. This demonstrates the strong generalization of our method across diverse degradations.
+
+# 5.3. Ablation Study
+
+We conduct ablation experiments in Tab. 4, 5 to further understand the impact of each component of our framework.
+
+Effect of Each Component. In Tab. 4, we validated the effectiveness of each proposed module in GleSAM. "Gle" indicates our framework with generative latent space enhancement. The results show that each introduced method significantly improves the performance. We also make a qualitative visualization in Fig. 5, which shows that the
+
+![](images/0c996d3d21f3661365ab448bc827b0ec838ee9a368c3c8e19c9097f74ba29799.jpg)  
+Figure 4. Density distribution maps about IoU and image quality across different methods, including SAM, GleSAM, SAM2, and GleSAM2. The image quality is calculated using the Laplacian operator in OpenCV. The red dashed box highlights the area where our method demonstrates improved segmentation performance compared to SAM, particularly in lower-quality images.
+
+![](images/465ce12e70e8a50e0668cb3421a74c91c0224b0ced1a28ba5fb880433f7f62cb.jpg)
+
+![](images/2c547d0664123531e0f3eeefde53684a458885be159343ef2de948aedb418a47.jpg)
+
+![](images/74f8d94249e386f2186780ac9c9141a202b06049b107495177876069ff2b98c3.jpg)
+
+<table><tr><td rowspan="2">Method</td><td colspan="2">ECSSD</td><td colspan="2">COCO</td></tr><tr><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td></tr><tr><td>SAM [27]</td><td>0.7833</td><td>0.8573</td><td>0.6512</td><td>0.7457</td></tr><tr><td>RobustSAM [8]</td><td>0.8427</td><td>0.9044</td><td>0.6543</td><td>0.7518</td></tr><tr><td>GleSAM (Ours)</td><td>0.8568</td><td>0.9119</td><td>0.6678</td><td>0.7700</td></tr><tr><td>SAM2</td><td>0.8211</td><td>0.8817</td><td>0.6817</td><td>0.7753</td></tr><tr><td>GleSAM2 (Ours)</td><td>0.8611</td><td>0.9133</td><td>0.6869</td><td>0.7836</td></tr></table>
+
+Table 3. Zero-shot performance comparison on RobustSeg-style [8] degradations. Performance is tested on the unseen ECSSD and COCO datasets. Note that our methods are not trained on such degradations. The superior performance of our method demonstrates robustness against various forms of degradation.  
+
+<table><tr><td rowspan="2">Method</td><td colspan="2">ECSSD</td><td colspan="2">COCO</td></tr><tr><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td></tr><tr><td>Baseline</td><td>0.6054</td><td>0.7107</td><td>0.4763</td><td>0.5725</td></tr><tr><td>+ Gle &amp; CRE</td><td>0.6567</td><td>0.7657</td><td>0.4958</td><td>0.5963</td></tr><tr><td>+ Gle &amp; CRE &amp; FDA</td><td>0.7104</td><td>0.8020</td><td>0.5166</td><td>0.6174</td></tr></table>
+
+clearest latent feature is obtained when combining all modules. Additionally, in Tab. 5, without fine-tuning the decoder, our method improves the results on LQ images by about 7 points, while also preserving the robustness of SAM on clear conditions, indicating the robustness of our method for both degraded and clear images.
+
+Effect of Fine-tuning SAM. We explore two common configurations to fine-tune SAM: fine-tuning the entire SAM's decoder and the output mask token. Our experiments reveal that directly fine-tuning SAM's decoder or output mask token on degraded images leads to a significant drop in zero-shot performance on clear images, with the IoU decreasing by nearly 20 points. In contrast, our method further improves performance on both low-quality and clear images by fine-tuning the entire decoder or mask token, enabling the segmentation of images with any quality.
+
+Table 4. Ablation study of each component in the proposed method, evaluated on the unseen ECSSD and COCO datasets. Each additional component positively affects the performance, demonstrating the effectiveness of the proposed methods.  
+
+<table><tr><td rowspan="2">Method</td><td colspan="2">LQ</td><td colspan="2">Clear</td><td colspan="2">Average</td></tr><tr><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td><td>IoU</td><td>Dice</td></tr><tr><td colspan="7">w/o Fine-tuning SAM:</td></tr><tr><td>SAM</td><td>0.5407</td><td>0.6416</td><td>0.7830</td><td>0.8554</td><td>0.6619</td><td>0.7485</td></tr><tr><td>Ours</td><td>0.6135</td><td>0.7097</td><td>0.7846</td><td>0.8567</td><td>0.6991</td><td>0.7832</td></tr></table>
+
+<table><tr><td colspan="7">Fine-tuning SAM:</td></tr><tr><td>SAM-FT-T</td><td>0.6305</td><td>0.7374</td><td>0.5847</td><td>0.7029</td><td>0.6076</td><td>0.7202</td></tr><tr><td>SAM-FT-D</td><td>0.6327</td><td>0.7385</td><td>0.6071</td><td>0.7242</td><td>0.6199</td><td>0.7314</td></tr><tr><td>Ours-FT-T</td><td>0.6759</td><td>0.7751</td><td>0.8061</td><td>0.8747</td><td>0.7410</td><td>0.8249</td></tr><tr><td>Ours-FT-D</td><td>0.6848</td><td>0.7825</td><td>0.8022</td><td>0.8704</td><td>0.7435</td><td>0.8265</td></tr></table>
+
+Table 5. Effect of Fine-tuning SAM. The performance is evaluated on the unseen ECSSD and COCO datasets. "FT-T" and "FT-D" indicate fine-tuning the SAM's mask token and decoder respectively. "LQ" indicates the mean performance on three levels of degraded data, and "Clear" indicates the results on the original clear images.
+
+![](images/f9c72fd87fb2dba082fce58ccef92fa4832cfb341ecd0eb56148ade9d01e6aba.jpg)  
+Figure 5. Qualitative visualization of the enhanced latent features. The clearest feature is obtained when combining all modules.
+
+# 5.4. Detailed Analysis
+
+Analysis of channel expansion methods. To address the issue of channel mismatch between SAM and the pretrained U-Net, we explored various strategies including (a) using two simple convolutional layers to reduce and expand the channels of segmentation features as needed, (b) fine-tuning new head and tail layers from scratch, and (c) our CRE method. Our results (in Tab. 6 and Fig. 7) show that strategies (a) and (b) are ineffective, likely because the new layers couldn't leverage the pre-trained knowledge. In contrast, our method effectively resolves this issue by replicating pre-trained parameters and fine-tuning with LoRA.
+
+Analysis of hyperparameter  $\gamma$ . We use an adaption weight
+
+![](images/c5dcb088b2ae42449b2edaaaa54ffa86ac76c4bff0c288406cab5864c2ce65a1.jpg)  
+(a) ECSSD  
+Figure 6. Ablation study of adaption weight  $\gamma$ .
+
+![](images/632d40407e9f22afaf7185829e5b57ebaa1f8edfafdd61894c580f085849e0f6.jpg)  
+(b) COCO
+
+<table><tr><td>Method</td><td>IoU</td><td>Dice</td><td>PA</td></tr><tr><td>(a) Additional encoder and decoder</td><td>0.4544</td><td>0.5842</td><td>0.6106</td></tr><tr><td>(b) New head and tail layers</td><td>0.6014</td><td>0.7077</td><td>0.7782</td></tr><tr><td>(c) Replicate and Expansion (Ours)</td><td>0.6567</td><td>0.7657</td><td>0.8400</td></tr></table>
+
+![](images/4922927e210e8414e17ecf1e0236673027c312a734367e9dcf7f4a0b4a2ce36f.jpg)  
+Figure 7. Qualitative visualization of the enhanced latent features generated by different channel expansion methods. Our proposed CRE method (c) produces more salient features.
+
+![](images/a3a6d01c799d9ac63d2a4d2096798ae342ba7d5ef090064ad311df72455583a4.jpg)
+
+![](images/1a7d02d70d6c49e5c04377da352773dc6dd6603ceeb9fe11241e687c1a818191.jpg)
+
+![](images/cbaa8fb398c1ccb6e323099f35b1b2a0b0b00d0e2a2429efd6b58976d8c9c132.jpg)
+
+$\gamma$  to align the distribution of the latent space between LDM and SAM. To determine the optimal value of  $\gamma$ , we empirically test five different values on the ECSSD and COCO datasets. The results, shown in Fig. 6, suggest that  $\gamma = 5$  is the most effective setting, providing strong generalization across all models and datasets. Therefore, we adopt  $\gamma = 5$  as the default value in our experiments.
+
+Analysis of LoRA ranks. We incorporate learnable LoRA layers to fine-tune the pre-trained denoising U-Net. Here, we evaluate the impact of different LoRA ranks on segmentation performance. The results are shown in Tab. 8. We tested the results and the corresponding number of learnable parameters for setting a rank to 4, 8, and 16. We found that setting the rank to 8 can obtain good results while maintaining an acceptable number of parameters.
+
+Analysis of degradation levels. To investigate the necessity of training on all three degradation levels together, we train GleSAM on LQ datasets for each degradation level (LQ-1, LQ-2, LQ-3) individually. We evaluate the models' performance on ECSSD and COCO datasets using three degraded levels and RobustSeg-style degradations. The results in Tab. 7 show that training on all three degrada
+
+Table 6. Analysis of the proposed CRE. It significantly outperforms alternative approaches, achieving higher scores.  
+
+<table><tr><td rowspan="2">Training Data</td><td colspan="3">Ours</td><td rowspan="2">LQ-RS</td><td rowspan="2">AVG</td></tr><tr><td>LQ-1</td><td>LQ-2</td><td>LQ-3</td></tr><tr><td>LQ-1</td><td>0.6981</td><td>0.6663</td><td>0.6076</td><td>0.7587</td><td>0.6827</td></tr><tr><td>LQ-2</td><td>0.7168</td><td>0.6843</td><td>0.6318</td><td>0.7621</td><td>0.6988</td></tr><tr><td>LQ-3</td><td>0.7185</td><td>0.6910</td><td>0.6403</td><td>0.7619</td><td>0.7030</td></tr><tr><td>LQ-1,2,3</td><td>0.7208</td><td>0.6922</td><td>0.6414</td><td>0.7623</td><td>0.7042</td></tr></table>
+
+Table 7. Ablation study for degradation levels during training. LQ-RS indicates the RobustSeg-style [8] degradation. AVG indicates the average performance. We report the IoU for comparison. The results on the unseen ECSSD and COCO datasets reveal that multi-level degradation contributes to more robust performance.  
+
+<table><tr><td rowspan="2">Rank</td><td colspan="3">LQ</td><td rowspan="2">Learnable 
+Params</td></tr><tr><td>IoU</td><td>Dice</td><td>PA</td></tr><tr><td>4</td><td>0.7760</td><td>0.8597</td><td>0.9434</td><td>16.25M</td></tr><tr><td>8</td><td>0.7844</td><td>0.8644</td><td>0.9456</td><td>32.49M</td></tr><tr><td>16</td><td>0.7697</td><td>0.8543</td><td>0.9418</td><td>64.99M</td></tr></table>
+
+Table 8. Analysis of LoRA ranks in U-Net.  
+
+<table><tr><td>Method</td><td>Learnable Params</td><td>Num. GPUs</td><td>Training Time (h)</td><td>Inference Speed (s)</td></tr><tr><td>SAM</td><td>1250 MB</td><td>256</td><td>N/A</td><td>0.32</td></tr><tr><td>GleSAM</td><td>47 MB</td><td>4</td><td>30</td><td>0.38 (+0.06)</td></tr></table>
+
+Table 9. Computational requirements of GleSAM vs SAM.
+
+tion levels together contributes to more robust performance across varying levels of image degradation.
+
+Analysis of computational requirements. In Tab. 9, we report detailed training and inference comparisons between our GleSAM and SAM. Although GleSAM demonstrates significantly improved robustness, it introduces only marginal learnable parameters and incurs a slight trade-off in inference speed. The additional parameters can be efficiently optimized in 30 hours on four A100 GPUs.
+
+# 6. Conclusion
+
+We present GleSAM, a solution to enhance Segment Anything Models (SAM and SAM2) for robust segmentation across images of any quality, particularly those with severe degradation. We incorporate the generative ability of pretrained diffusion models into the latent space of SAMs to enhance low-quality features, thus promoting more robust segmentation. Our approach is further supported by the LQSeg dataset, which includes diverse degradation types and levels, allowing for more comprehensive model training and evaluation. Extensive experiments demonstrate that GleSAM achieves superior performance on degraded images while maintaining generalization to clear ones, and it performs well on unseen degradations, highlighting its robustness and versatility. This work extends SAM capabilities, offering a practical approach for degraded scenarios.
+
+# Acknowledgements
+
+This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62372382.
+
+# References
+
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+# Segment Anything, Even Occluded
+
+Wei-En Tai $^{1*}$  Yu-Lin Shih $^{1*}$  Cheng Sun $^{2}$
+
+$^{1}$ National Tsing Hua University  $^{2}$ NVIDIA
+
+Yu-Chiang Frank Wang $^{2,3}$  Hwann-Tzong Chen $^{1,4}$
+
+$^{3}$ National Taiwan University  $^{4}$ Aeolus Robotics
+
+# Abstract
+
+Amodal instance segmentation, which aims to detect and segment both visible and invisible parts of objects in images, plays a crucial role in various applications, including autonomous driving, robotic manipulation, and scene understanding. While existing methods require training both front-end detectors and mask decoders jointly, this approach lacks flexibility and fails to leverage the strengths of pre-existing modal detectors. To address this limitation, we propose SAMEO, a novel framework that adapts the Segment Anything Model (SAM) as a versatile mask decoder capable of interfacing with various front-end detectors to enable mask prediction even for partially occluded objects. Acknowledging the constraints of limited amodal segmentation datasets, we introduce Amodal-LVIS, a large-scale synthetic dataset comprising 300K images derived from the modal LVIS and LLVIS datasets. This dataset significantly expands the training data available for amodal segmentation research. Our experimental results demonstrate that our approach, when trained on the newly extended dataset, including Amodal-LVIS, achieves remarkable zero-shot performance on both COCOA-cls and D2SA benchmarks, highlighting its potential for generalization to unseen scenarios.
+
+# 1. Introduction
+
+Human visual perception extends beyond what is directly visible in a scene. We can naturally imagine and understand the complete shape of partially occluded objects through a combination of object recognition and prior knowledge about object categories. Even when correctly classifying objects is difficult, we can often infer the complete shape of partially visible objects by analyzing visible parts and reasoning about common occlusion patterns [9, 20, 24]. Amodal instance segmentation seeks to replicate this remarkable human capability by detecting and localizing objects in images and predicting their complete shapes, including both visible and occluded portions (Figure 1).
+
+![](images/28277ee719419dd090a46267b1b9114237aeb47cf2765c19693e60cd623b1098.jpg)  
+Figure 1. Amodal segmentation examples: The top row shows the original images. The middle row displays EfficientSAM predicted modal masks that only cover the visible parts of objects. The bottom row illustrates amodal masks that reveal the complete object shapes predicted by our method, SAMEO—a Segment Anything Model Even under Occlusion.
+
+![](images/0c942627b7d3b020fc2a00fc815876c0af364419260f3f03d048e8cb4ae1a983.jpg)
+
+![](images/12bbd276a74802f1d3957a7f6b961f28aa93c7dc1d4b08ce15199756a666f740.jpg)
+
+![](images/1210fba3f247425aca246803e5b46fe029ccc69c102389cb11aab2d4f2af74ba.jpg)
+
+An effective approach to addressing amodal instance segmentation is to divide the task into two main components: object detection and mask segmentation. In recent years, significant advances have been made in object detection, with state-of-the-art models such as RTMDet [16] and ConvNeXt-V2 [28] achieving impressive performance. However, current amodal segmentation approaches often require training both the detector and mask decoder jointly, which prevents them from fully utilizing these powerful pre-trained modal detectors. This limitation motivated us to develop a more flexible framework that can leverage existing modal detectors while still maintaining strong amodal segmentation capabilities. The emergence of foundation models for visual understanding has opened up new possibilities in segmentation tasks. Among these, the Segment Anything Model (SAM) [11] and its efficient variant, EfficientSAM [29], have demonstrated remarkable capabilities in prompt-based modal segmentation. We leverage EfficientSAM's architecture, which features a lightweight encoder for faster inference, and adapt it for amodal segmen
+
+tation through specialized training. Our approach enables the model to process both amodal and modal prompts for generating amodal mask predictions while maintaining potential zero-shot capabilities. Besides the improvements in algorithms and architectures, datasets are also crucial for learning-based methods, yet current amodal segmentation datasets encounter several challenges:
+
+- Limited Scale: Existing datasets contain relatively few images, hindering the development of robust models.  
+- Annotation Quality: Several datasets relying on automatic generation methods can lead to inconsistent and sometimes incorrect instance annotations when not properly validated.  
+- Irrelevant Objects: A significant portion of annotated objects, such as walls and floors, contribute little to meaningful scene understanding.
+
+To address these limitations, we present Amodal-LVIS, a new large-scale dataset derived from LVIS [6] and LVVIS [26]. Our dataset contains 300K carefully curated images, where each image contains one instance annotation. These annotations form paired examples between synthetic occluded instances and their original unoccluded versions. Furthermore, we have processed and refined existing datasets to create a comprehensive training collection comprising approximately 1M images and 2M instance annotations.
+
+Experimental results show that our method with the EfficientSAM architecture, when trained on our combined dataset, achieves remarkable zero-shot performance that surpasses previous supervised amodal segmentation methods. These results validate our approach of leveraging an efficient existing architecture with high-quality, large-scale training data for amodal segmentation tasks.
+
+Our main contributions can be summarized as follows:
+
+1. Flexible Amodal Framework: The proposed method, SAMEO, adapts EfficientSAM for amodal instance segmentation that works with both modal and amodal detector prompts through specialized training.  
+2. Large-scale Dataset: A new Amodal-LVIS dataset containing 300K images, forming paired examples between synthetic occluded instances and their original unoccluded versions.  
+3. Dataset Collection: A comprehensive training collection of 1M images and 2M instances created by combining and refining existing amodal datasets with AmodalLVIS.  
+4. Zero-shot Performance: State-of-the-art zero-shot results on both COCOA-cls and D2SA benchmarks, surpassing previous supervised methods.
+
+![](images/902242121c4bf82d48b1bc857a55b5634093eb84423b43f75e8bf1eefed73dd6.jpg)  
+Figure 2. Overview of our amodal segmentation pipeline. Given an input image, existing object detectors first generate either modal boxes (showing visible regions) or amodal boxes (showing complete object extent). Our SAMEO then processes these detections to produce amodal masks that recover the full shape of objects, including occluded parts.
+
+# 2. Related Work
+
+# 2.1. Instance Segmentation
+
+Instance segmentation is a fundamental computer vision task that simultaneously addresses object detection and segmentation, aiming to both locate objects in a scene and generate precise mask predictions for each detected instance. Initially focusing on visible parts of objects (modal instance segmentation), this field continues to evolve with the emergence of deep learning architectures. State-of-the-art methods demonstrate improvements through transformer-based feature extraction [27], modernized convolutions [21, 28], and optimized speeds [16]. Further detection models built upon the DETR architecture [1] have achieved additional advances through specialized query selection mechanisms and training schemes [33, 35].
+
+Building upon modal instance segmentation, amodal instance segmentation extends the task to predict complete object shapes, including occluded regions. This extension is first formalized by Li and Malik [12], leading to various architectural innovations [5, 30, 31]. Notable approaches include ORCNN [4], ASN [22], which enhance Mask R-CNN [7] with occlusion reasoning capabilities, and BC-Net [10] with its bilateral layers for handling object overlaps. Currently, AISFormer [25] represents the state-of-the-art in amodal instance segmentation by introducing transformers to effectively model long-range dependencies.
+
+# 2.2. Segment Anything Model
+
+The Segment Anything Model (SAM) [11] represents a significant advancement in foundational computer vision models, capable of segmenting any visual object based on various prompts, including points or boxes. Trained on a dataset
+
+of 11M images, SAM demonstrates outstanding zero-shot generalization capabilities across diverse object categories and domains.
+
+The original SAM model, despite its strong performance, faces practical limitations due to high computational demands, including significant memory requirements and slow inference speed. EfficientSAM [29] addresses these challenges by using a Masked Autoencoder (MAE) [8] pre-training method to learn the feature embeddings from SAM's original ViT-H encoder, resulting in faster inference speed and reduced model size while maintaining comparable segmentation performance.
+
+# 2.3. Amodal Datasets
+
+Several datasets have been introduced for amodal segmentation. COCOA [34] is the first amodal dataset, providing semantic-level amodal annotations for COCO images. D2SA/COCOA-cls [4] extends this with instance-level annotations. DYCE [3] offers synthetic indoor scenes with accurate ground truth. The KINS dataset [22] focuses on traffic scenarios with 14K images of vehicles and pedestrians. More recently, MUVA [13] introduces a multi-view shopping scenario dataset, while MP3D-Amodal [32] provides real-world indoor scenes from Matterport3D. WALT [23] uniquely utilizes time-lapse imagery to obtain amodal ground truth, and KITTI-360-APS [18] extends KITTI-360 [14] with amodal panoptic annotations. Furthermore, datasets from related amodal completion works, such as pix2gestalt [19], have contributed to the development of the field.
+
+# 3. Our Approach
+
+# 3.1. Enabling Amodal Mask Prediction
+
+Preliminaries: EfficientSAM. Segment Anything Model (SAM) [11] is a foundation model for image segmentation that can generate high-quality object masks based on any prompt. The original SAM architecture consists of three main components: i) an image encoder that transforms the input image into image embeddings, ii) a lightweight transformer-based prompt encoder that converts prompts (points, boxes) into unified embeddings, and iii) a mask decoder that utilizes a transformer architecture with two cross-attention layers to process both image and prompt embeddings for generating the final segmentation mask. In our approach, we mainly use EfficientSAM [29], a compact adaptation of the original SAM model. EfficientSAM replaces SAM's image encoder with a lightweight ViT variant [2] while maintaining the original prompt encoder and mask decoder.
+
+Model Architecture. We propose SAMEO for amodal instance segmentation, retaining a lightweight image en
+
+coder  $\mathcal{E}$  as in the original architecture of EfficientSAM, a transformer-based prompt encoder  $\mathcal{P}$ , and a mask decoder  $\mathcal{D}$  with dual cross-attention layers. Given an input image  $I$  and a bounding box prompt  $B$ , the proposed SAMEO pipeline predicts the amodal mask  $\hat{M}$  and the estimated IoU  $\hat{\rho}$  as follows:
+
+$$
+\hat {M}, \hat {\rho} = \mathcal {D} (\mathcal {E} (I), \mathcal {P} (B)). \tag {1}
+$$
+
+Training Strategy. During training, we exclusively fine-tune EfficientSAM's mask decoder while keeping the original weights of the image encoder and prompt encoder unchanged. The model receives two inputs: an image and a bounding box prompt derived from ground-truth annotations. The box prompts are randomly selected from modal and amodal ground-truth boxes with equal probability. The training objective combines Dice loss [17], Focal loss [15], and L1 loss for IoU estimation:
+
+$$
+\mathcal {L} = \mathcal {L} _ {\text {D i c e}} + \mathcal {L} _ {\text {F o c a l}} + \lambda \mathcal {L} _ {\text {I o U}}, \tag {2}
+$$
+
+where
+
+$$
+\mathcal {L} _ {\mathrm {D i c e}} = 1 - \frac {2 | \hat {M} \cap M _ {\mathrm {g t}} |}{| \hat {M} | + | M _ {\mathrm {g t}} |}, \tag {3}
+$$
+
+$$
+\mathcal {L} _ {\text {F o c a l}} = - (1 - p _ {t}) ^ {\gamma} \log (p _ {t}), \tag {4}
+$$
+
+$$
+\mathcal {L} _ {\mathrm {I o U}} = \left| \hat {\rho} - \mathrm {I o U} (\hat {M}, M _ {\mathrm {g t}}) \right|. \tag {5}
+$$
+
+Here,  $M_{\mathrm{gt}}$  represents the ground truth amodal mask,  $p_t$  is the predicted probability for the target class,  $\gamma$  is the focusing parameter set to 2 in our experiments, and  $\lambda$  is empirically set to 0.05.
+
+Inference Pipeline. For inference, SAMEO can be flexibly integrated with various object detectors, including both amodal detectors (e.g., AISFormer [25]) and conventional modal detectors (e.g., RTMDet [16]). The detection outputs serve as box prompts for our model, which then generates corresponding amodal masks (Figure 2). This modular design allows our model to enhance existing detection systems with amodal segmentation capabilities while achieving state-of-the-art performance.
+
+# 3.2. Amodal Dataset Collection
+
+Limitations of Existing Amodal Datasets. Existing amodal datasets have inherent limitations in both manual and synthetic annotation mechanisms. Human-annotated datasets, while closely representing real-world scenarios, are costly to produce and prone to errors in occluded region estimation. Synthetic datasets, though efficient to generate, lack reliable verification mechanisms for object completeness and may not accurately reflect natural occlusion patterns (Figure 3).
+
+<table><tr><td>Dataset</td><td>Type</td><td># Instances</td><td># Images</td><td>POI (%)</td><td>Average ROR (%)</td></tr><tr><td>COCOA (no stuff) [34]</td><td>Real</td><td>32,926</td><td>5,073</td><td>54.9</td><td>30.2</td></tr><tr><td>COCOA-cls [4]</td><td>Real</td><td>10,562</td><td>3,499</td><td>49</td><td>21.8</td></tr><tr><td>KINS [22]</td><td>Real</td><td>188,085</td><td>14,993</td><td>54.3</td><td>42.2</td></tr><tr><td>KITTI-360-APS [18]</td><td>Real</td><td>89,938</td><td>12,496</td><td>50</td><td>33.9</td></tr><tr><td>D2SA [4]</td><td>Synthetic</td><td>28,720</td><td>5,600</td><td>53.6</td><td>23.7</td></tr><tr><td>MUVA [13]</td><td>Synthetic</td><td>198,573</td><td>26,406</td><td>76.7</td><td>32.1</td></tr><tr><td>WALT* [23]</td><td>Synthetic</td><td>485,369</td><td>40,000</td><td>32</td><td>36.5</td></tr><tr><td>DYCE* [3]</td><td>Synthetic</td><td>66,453</td><td>5,229</td><td>77.8</td><td>29</td></tr><tr><td>MP3D-amodal* [32]</td><td>Synthetic</td><td>2,968</td><td>2,549</td><td>100</td><td>41.1</td></tr><tr><td>pix2gestalt [19]</td><td>Synthetic</td><td>849,667</td><td>849,667</td><td>100</td><td>35.9</td></tr><tr><td>Amodal-LVIS</td><td>Synthetic</td><td>399,398</td><td>301,493</td><td>50</td><td>34.5</td></tr><tr><td>Total</td><td>-</td><td>2,352,659</td><td>1,267,424</td><td>-</td><td>-</td></tr></table>
+
+Table 1. Amodal dataset collection. Datasets marked with * are generated or refined by ourselves. POI is calculated as the percentage of occluded instances (where modal mask is not equal to amodal mask) over all instances. Meanwhile, average ROR is the average ratio of occluded regions, considering only instances with occlusions.
+
+![](images/4c6c2a5846ebe11e65a6e1bd7846a01dc7874f8dee28b2af76acfc7f71619e9a.jpg)  
+(a) DYCE
+
+![](images/3187e777fb267d2b48737a06faefe51dd93890fa43d7ae511e42a8af6aa09198.jpg)  
+(b) MP3D-amodal
+
+![](images/f909521fb192f060ca747c75f4d91d3ee49dd56c38515fa1e3e9424c440fac5f.jpg)  
+(c) pix2gestalt  
+Figure 3. Examples of limitations in existing amodal datasets: (a) DYCE and (b) MP3D-amodal show meaningless architectural elements rendered from 3D meshes that dominate the image space, while (c) pix2gestalt contains potentially incomplete amodal masks due to restrictive generation criteria.
+
+Dataset Collection and Quality Control. To leverage the advantages of both mechanisms, we have collected and filtered datasets of both annotation types. Our cleaning process addresses specific issues in each dataset to ensure data quality while maintaining realistic occlusion representations (Table 1).
+
+For synthetic datasets DYCE and MP3D-amodal, generated using 3D furniture meshes, we identify and address two main quality issues: meaningless architectural elements (walls, floors, ceilings) occupying the majority of image space and objects with minimal visible areas. We implement filters to remove cases where visible parts are less than  $10\%$  of the whole object, objects occupying more than  $90\%$  of the image area, and architectural element annotations.
+
+The WALT dataset leverages road surveillance timelapse footage for synthetic data generation. It obtains bounding boxes for cars and people using a pre-trained detector and then identifies complete objects by analyzing these bounding box intersections. These discovered com
+
+![](images/9d69c4799297f7d0656c35531df66230454a14801633a8ff6a7047fc03c0a440.jpg)  
+Original Image  
+Figure 4. Amodal-LVIS dataset generation process. From left to right: original image with unoccluded objects, a selected occluder object, and the synthesized image with occlusion. Our dataset includes both the original and the synthesized image for each instance to prevent occlusion bias during training.
+
+![](images/bbbbe17969c3135948245e1eaa177f4ae0a8987e7a413352d77e4747bb1c8d8f.jpg)  
+Occluder
+
+![](images/e95b0ccbbe66bbd6ce01f9d26828cf35fc1df94dbd527b60c6397895c132c4e5.jpg)  
+Synthesized Image
+
+plete objects are then composited back into the same scenes to generate synthetic training data. However, their layer-by-layer placement can create unrealistic occlusions. We address this by implementing an occlusion threshold filter to ensure natural occlusion patterns.
+
+For other datasets with class annotation, such as CO-COA, the availability of semantic labels enabled straightforward quality control. We filter out "stuff" class annotations across these datasets to focus on meaningful objects that align with amodal instance segmentation goals.
+
+# 3.3.Amodal-LVIS
+
+We propose a synthetic dataset for amodal mask segmentation through precise object occlusion generation, incorporating complete object collection, synthetic occlusion generation, and a dual annotation mechanism to prevent model bias. Combined with existing datasets, our collection totals 1M images and 2M instance annotations.
+
+Complete Object Collection. To obtain complete objects for synthetic occlusion, we utilize SAMEO, which is pre-trained on the previously mentioned amodal datasets, to generate pseudo labels for instances within LVIS and LVVIS datasets. Our model predicts amodal masks for each instance, which are then compared with the ground-truth visible mask annotations. This comparison helps us identify complete, unoccluded objects.
+
+Synthetic Occlusion Generation. The occlusion generation process involves pairing randomly selected complete objects from our collected pool. To ensure that the occlusions look realistic, we normalize the paired objects to similar sizes while maintaining their aspect ratios. Object positioning and occlusion rates are controlled using bounding box annotations, which allow for precise management of how objects occlude one another.
+
+Dual Annotation Mechanism. Our experiments in Section 4 show that training solely on occluded masks leads to model confusion, resulting in an over-prediction of occluded objects even when the prompts are intended to target foreground instances. To resolve this issue, we include both occluded and original unoccluded versions of instances in our dataset (Figure 4). This dual annotation mechanism prevents occlusion bias while providing comprehensive training examples for both states.
+
+# 4. Experiments
+
+# 4.1. Settings
+
+Implementation Details. Our model is trained on NVIDIA Tesla V100/A100 GPUs for 1,440/2,340/22,500 iterations on COCOA-cls/D2SA/MUVA respectively. For zero-shot SAMEO, we increase the batch size to 32 and train for 40,000 iterations. We use the Adam optimizer with a learning rate of  $1 \times 10^{-4}$  without any learning rate scheduler. For each instance during training, we randomly select either the amodal or modal ground truth bounding box as the prompt with equal probability.
+
+Datasets and Baselines. For training, we use our dataset collection and the proposed Amodal-LVIS dataset. As for evaluation, COCOA-cls, D2SA and MUVA datasets are utilized. We primarily compare our approach against AIS-Former, the current state-of-the-art model in amodal instance segmentation. Unlike conventional instance segmentation models that incorporate both object detection and mask decoding components, SAMEO functions solely as a mask decoder. This allows our model to flexibly integrate with existing amodal and modal instance segmentation models, using their object box predictions as prompts to
+
+generate refined amodal masks. For a comprehensive evaluation, we compare our results against the original mask predictions from these front-end models.
+
+To evaluate our zero-shot performance, we extend the comparisons beyond AISFormer to include both modal and amodal front-end models equipped with the original EfficientSAM as their mask decoder. This comparison demonstrates our successful adaptation of EfficientSAM for amodal mask segmentation while maintaining zero-shot capabilities.
+
+Evaluation Metrics. We evaluate our method using two standard metrics: mean Average Precision (AP) and mean Average Recall (AR). Since our model is class-agnostic, we compute both metrics without considering class labels. For a fair comparison, we reproduce baseline methods and evaluate them using the same class-agnostic AP and AR. For methods that use SAMEO as mask decoder, we refine the confidence score  $\hat{\rho}_{\mathrm{front}}$  of front-end models using the estimated IoU  $\hat{\rho}_{\mathrm{ours}}$  predicted by SAMEO when calculating these metrics. The refined confidence score  $\hat{\rho}_{\mathrm{ref}}$  of these cases is computed as follows:
+
+$$
+\hat {\rho} _ {\text {r e f}} = \hat {\rho} _ {\text {f r o n t}} \times \hat {\rho} _ {\text {o u r s}}. \tag {6}
+$$
+
+# 4.2. Results
+
+Quantitative Results. We evaluate our proposed SAMEO on three widely-used datasets: COCOA-cls, D2SA, and MUVA. For each dataset, we train our model on their respective training sets and evaluate on the corresponding test sets (Table 2). To demonstrate SAMEO's effectiveness and versatility, we attach it to various pretrained front-end models, where modal front-ends are trained with modal annotations and amodal front-ends are trained with amodal annotations of these datasets. Our experimental results show that SAMEO significantly outperforms the current state-of-the-art method, AISFormer, achieving higher AP and AR across all datasets. Notably, our model exhibits robust performance in mask refinement independent of the front-end model's original mask type. Regardless of whether the front-end models produce modal or amodal mask predictions, SAMEO successfully refines them to achieve comparable high performance, demonstrating its strong capability to utilize both types of prompts.
+
+Qualitative Results. For qualitative evaluation, we compare our model's predictions against AISFormer on both COCOA-cls and MUVA datasets (Figure 5). Our method exhibits superior performance across various challenging scenarios, including complex still life arrangements with multiple overlapping objects (e.g., bottles and containers),
+
+<table><tr><td rowspan="2">Model</td><td colspan="4">COCOAc-cls</td><td colspan="4">D2SA</td><td colspan="4">MUVA</td></tr><tr><td>AP</td><td>\(AP_{50}\)</td><td>\(AP_{75}\)</td><td>AR</td><td>AP</td><td>\(AP_{50}\)</td><td>\(AP_{75}\)</td><td>AR</td><td>AP</td><td>\(AP_{50}\)</td><td>\(AP_{75}\)</td><td>AR</td></tr><tr><td>AISFormer [25]</td><td>40.6</td><td>70.5</td><td>42.5</td><td>55.2</td><td>66.3</td><td>89.9</td><td>72.8</td><td>76.1</td><td>69.4</td><td>90.3</td><td>75.6</td><td>80.7</td></tr><tr><td>RTMDet* [16]</td><td>49.8</td><td>71.2</td><td>54.7</td><td>69.6</td><td>59.7</td><td>81.3</td><td>63.4</td><td>78.2</td><td>46.0</td><td>68.2</td><td>46.4</td><td>57.8</td></tr><tr><td>ConvNeXt-V2* [28]</td><td>46.7</td><td>67.5</td><td>52.0</td><td>70.9</td><td>66.1</td><td>89.1</td><td>69.8</td><td>75.3</td><td>47.9</td><td>69.0</td><td>46.6</td><td>61.7</td></tr><tr><td>ViTDet* [27]</td><td>47.4</td><td>69.4</td><td>52.5</td><td>68.8</td><td>63.0</td><td>86.4</td><td>65.8</td><td>75.4</td><td>45.3</td><td>66.9</td><td>44.7</td><td>60.7</td></tr><tr><td>AISFormer+SAMEO</td><td>54.3</td><td>74.0</td><td>59.7</td><td>69.3</td><td>79.8</td><td>92.7</td><td>84.8</td><td>84.2</td><td>76.2</td><td>90.8</td><td>80.9</td><td>85.2</td></tr><tr><td>RTMDet*+SAMEO</td><td>55.3</td><td>75.2</td><td>60.8</td><td>74.3</td><td>72.7</td><td>85.8</td><td>77.5</td><td>84.2</td><td>75.8</td><td>89.2</td><td>79.9</td><td>83.1</td></tr><tr><td>ConvNeXt-V2*+SAMEO</td><td>54.1</td><td>73.1</td><td>59.3</td><td>74.0</td><td>80.8</td><td>94.0</td><td>85.1</td><td>87.1</td><td>79.2</td><td>93.1</td><td>82.6</td><td>81.3</td></tr><tr><td>ViTDet*+SAMEO</td><td>54.1</td><td>73.3</td><td>59.2</td><td>72.3</td><td>78.6</td><td>92.3</td><td>82.6</td><td>84.7</td><td>74.1</td><td>89.0</td><td>78.2</td><td>82.3</td></tr></table>
+
+Table 2. Quantitative comparison on various datasets. Models marked with * are modal instance segmentation methods that detect objects and segment their visible masks without handling occlusions, with performance metrics calculated using their modal mask predictions. SAMEO takes bounding box predictions from the front-end models as prompts to generate amodal instance masks. Evaluation metrics include Average Precision (AP) at different IoU thresholds and Average Recall (AR). Bold numbers indicate the best performance.  
+
+<table><tr><td rowspan="2">Model</td><td colspan="4">COCOA-cls</td><td colspan="4">D2SA</td></tr><tr><td>AP</td><td>\(AP_{50}\)</td><td>\(AP_{75}\)</td><td>AR</td><td>AP</td><td>\(AP_{50}\)</td><td>\(AP_{75}\)</td><td>AR</td></tr><tr><td>AISFormer</td><td>40.6</td><td>70.5</td><td>42.5</td><td>55.2</td><td>66.3</td><td>89.9</td><td>72.8</td><td>76.1</td></tr><tr><td>AISFormer+EfficientSAM†</td><td>47.6</td><td>70.0</td><td>51.7</td><td>64.2</td><td>69.6</td><td>89.2</td><td>72.3</td><td>77.7</td></tr><tr><td>RTMDet*+EfficientSAM†</td><td>48.7</td><td>71.1</td><td>53.2</td><td>65.9</td><td>63.0</td><td>82.7</td><td>64.8</td><td>74.3</td></tr><tr><td>RetinaNet* [21]+EfficientSAM†</td><td>44.8</td><td>67.1</td><td>48.6</td><td>68.4</td><td>60.6</td><td>77.5</td><td>62.9</td><td>79.3</td></tr><tr><td>DINO* [33]+SAMEO†</td><td>50.2</td><td>70.4</td><td>55.7</td><td>73.7</td><td>69.8</td><td>86.2</td><td>72.6</td><td>75.9</td></tr><tr><td>RetinaNet*+SAMEO†</td><td>51.0</td><td>72.2</td><td>56.6</td><td>72.6</td><td>62.5</td><td>78.0</td><td>64.8</td><td>80.7</td></tr><tr><td>AISFormer+SAMEO†</td><td>52.8</td><td>73.4</td><td>57.9</td><td>67.9</td><td>74.1</td><td>90.2</td><td>78.3</td><td>79.9</td></tr><tr><td>ViTDet*+SAMEO†</td><td>53.2</td><td>73.0</td><td>58.6</td><td>71.2</td><td>72.1</td><td>89.0</td><td>74.9</td><td>80.7</td></tr><tr><td>ConvNeXt-V2*+SAMEO†</td><td>53.4</td><td>73.0</td><td>59.0</td><td>73.0</td><td>74.3</td><td>91.6</td><td>78.0</td><td>83.1</td></tr><tr><td>CO-DETR* [35]+SAMEO†</td><td>54.0</td><td>74.8</td><td>60.2</td><td>73.5</td><td>75.0</td><td>91.0</td><td>78.5</td><td>82.2</td></tr><tr><td>RTMDet*+SAMEO†</td><td>54.4</td><td>75.0</td><td>60.2</td><td>73.4</td><td>68.4</td><td>84.5</td><td>71.0</td><td>77.7</td></tr></table>
+
+Table 3. Zero-shot performance on COCOA-cls and D2SA datasets. The results show SAMEO not only significantly outperforms AIS-Former but also successfully adapts EfficientSAM's modal segmentation capability to amodal segmentation, demonstrating consistent performance improvements when paired with various front-end detectors.  $\dagger$  indicates zero-shot evaluation without training on the test dataset. * denotes modal object detectors that provide modal bounding boxes as prompts. Bold numbers indicate the best performance.
+
+scenes with intricate occlusions (e.g., people behind barriers), and diverse object categories and poses. Results show that our model generates significantly more precise amodal masks with sharper boundaries while providing more reasonable predictions for occluded parts. The qualitative comparison clearly demonstrates our method's improvements over the baseline method in both mask quality and occlusion reasoning capabilities, validating the effectiveness of SAMEO for real-world amodal segmentation tasks.
+
+# 4.3. Zero-shot Performance
+
+To evaluate SAMEO's zero-shot generalization capability, we train our model on our dataset collection and the proposed Amodal-LVIS dataset, excluding COCOA-cls and D2SA. During training, for each batch, a dataset is sampled with probability proportional to the logarithm of its
+
+size divided by the sum of log sizes across all datasets. We then test these two held-out datasets to demonstrate zero-shot performance (Table 3). For comparison, we include AISFormer and RTMDet (both trained on target datasets) combined with the original EfficientSAM, showing that our model successfully adapts EfficientSAM for amodal segmentation while preserving its zero-shot capability. Furthermore, we experiment with various pre-trained modal front-end detectors to demonstrate SAMEO's robust zero-shot performance regardless of the front-end choice.
+
+The results demonstrate SAMEO's superior performance, achieving up to 13.8 AP improvement over AIS-Former on COCOA-cls with RTMDet and 8.7 AP over on D2SA with CO-DETR, reaching state-of-the-art results and validating its strength as a robust zero-shot amodal segmentation solution.
+
+![](images/46211eb7441e155202e6bb7c76cc8c7515b9b83004767426817c0203ad466d36.jpg)  
+Figure 5. Qualitative comparison of amodal mask predictions. For each row: SAMEO's amodal prediction (top) with AISFormer box prompts, and AISFormer's prediction (bottom). Our method demonstrates superior mask quality, exhibiting more precise boundary delineation and robust handling of complex occlusion scenarios. Original images used for evaluation are available in supplementary materials.
+
+<table><tr><td rowspan="2">IoU Prediction</td><td colspan="3">COCOA-cls</td></tr><tr><td>AP</td><td>AP50</td><td>AP75</td></tr><tr><td>×</td><td>52.4</td><td>73.2</td><td>57.8</td></tr><tr><td>✓</td><td>54.3</td><td>74.0</td><td>59.7</td></tr></table>
+
+Table 4. Ablation study of IoU prediction refinement on COCOAc-cls dataset, using AISFormer as front-end.  $\times$  and  $\checkmark$  indicate without and with IoU prediction refinement,respectively.  
+
+<table><tr><td rowspan="2">Box Prompt</td><td colspan="4">COCOA-cls</td></tr><tr><td>AP</td><td>\(AP_{50}\)</td><td>\(AP_{75}\)</td><td>AR</td></tr><tr><td>amodal</td><td>53.0</td><td>72.9</td><td>58.0</td><td>71.1</td></tr><tr><td>modal</td><td>53.7</td><td>73.3</td><td>59.3</td><td>71.2</td></tr><tr><td>random</td><td>54.2</td><td>73.5</td><td>59.5</td><td>71.6</td></tr></table>
+
+Table 5. Comparison of different prompt types during training. We evaluate three variants on the COCOA-cls dataset using both modal and amodal front-ends, reporting the averaged performance over both types. The results show that training with random box prompts has optimal performance across diverse front-end models.
+
+# 4.4. Ablation Study
+
+Effect of IoU Prediction. To validate the effectiveness of the IoU prediction branch in our model, we have conducted experiments comparing the performance metrics before and after confidence score refinement using SAMEO's predicted IoU. Using AISFormer as our front-end model and evaluating this setting on the COCOA-cls dataset, the experimental results demonstrate that SAMEO's precise IoU prediction significantly contributes to improving the ranking of segmentation results (Table 4). Specifically, we observe that incorporating the predicted IoU for confidence score refinement leads to notable improvements in AP metric, confirming that the IoU prediction branch plays a crucial role in enhancing the overall performance of our model.
+
+Impact of Training Prompt Types. We investigate the optimal prompt-type strategy for training SAMEO to achieve balanced performance across both modal and amodal front-end prompts. We train three variants of SAMEO using ground truth amodal boxes, modal boxes, and a random mixture of both with equal probability. For evaluation, we integrate each trained model variant with both amodal and modal front-end detectors and evaluate their performance separately. The final performance metric is calculated by averaging the AP and AR scores across both front-end scenarios (Table 5). Our findings reveal that training with random boxes with equal probability yields the most balanced performance when handling various front-end prompt types, demonstrating the model's ability to generalize across different input scenarios.
+
+![](images/11febd1ffacac072ccf72c7cc4aa508fdc1d01b9609d4fa2ef1f439624b9b4bf.jpg)  
+Figure 6. Visualization of over-prediction. Given a detector box clearly intended for the foreground dog, SAMEO(occ), trained solely on occluded instances, mistakenly predicts the mask of the background towel. After rebalancing our dataset to include both occluded and non-occluded instances, SAMEO Mix) produces results that closely match the EfficientSAM baseline.
+
+Dataset Composition Analysis. To understand the importance of diverse instance types in training data, we have conducted an experiment training SAMEO exclusively on datasets containing only occluded instances (e.g., pix2gestalt). Visualization results reveal a significant limitation: the model exhibits over-prediction of background instances, even when the input box prompt clearly indicates a foreground object (Figure 6). This observation motivates our design choice for the Amodal-LVIS dataset, which maintains an equal distribution of occluded and non-occluded annotations. This balanced composition prevents bias and ensures robustness across various scenarios.
+
+# 5. Conclusion
+
+We present a flexible approach to amodal instance segmentation by adapting foundation segmentation models to handle both visible and occluded portions of objects. Our framework successfully leverages pre-trained modal detectors while maintaining strong amodal segmentation capabilities. The introduction of Amodal-LVIS, containing 300K carefully curated images, along with our comprehensive collection of 1M images and 2M instance annotations, addresses critical limitations in existing datasets and provides the necessary scale for robust model development.
+
+Our extensive experiments demonstrate that SAMEO consistently outperforms state-of-the-art methods on COCOA-cls, D2SA, and MUVA datasets. Most notably, when trained on our dataset collection, including AmodalLVIS, SAMEO achieves strong zero-shot performance on unseen datasets. The model's robust generalization abilities persist across various front-end detectors, validating our approach of adapting foundation models for amodal segmentation without compromising performance. We further address the limitations and possible future work of SAMEO in the appendix.
+
+# Acknowledgements
+
+This work was supported in part by NSTC grants 113-2221-E-001-010-MY3 and 112-2221-E-A49-100-MY3 of Taiwan, as well as funding from NVIDIA Taiwan AI R&D Center. We appreciate the National Center for High-performance Computing for providing computational resources and facilities.
+
+# References
+
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+# Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation
+
+Tanner Schmidt Meta Reality Labs
+
+tanner.schmidt@meta.com
+
+Richard Newcombe Meta Reality Labs
+
+newcombe@meta.com
+
+# Abstract
+
+This paper presents Segment This Thing (STT), a new efficient image segmentation model designed to produce a single segment given a single point prompt. Instead of following prior work and increasing efficiency by decreasing model size, we gain efficiency by foveating input images. Given an image and a point prompt, we extract a crop centered on the prompt and apply a novel variable-resolution patch tokenization in which patches are downsampled at a rate that increases with increased distance from the prompt. This approach yields far fewer image tokens than uniform patch tokenization. As a result we can drastically reduce the computational cost of segmentation without reducing model size. Furthermore, the foveation focuses the model on the region of interest, a potentially useful inductive bias. We show that our Segment This Thing model is more efficient than prior work while remaining competitive on segmentation benchmarks. It can easily run at interactive frame rates on consumer hardware and is thus a promising tool for augmented reality or robotics applications.
+
+# 1. Introduction
+
+Since its introduction in 2023, the Segment Anything Model (SAM) [20] has brought new focus to the image segmentation task and enabled numerous downstream applications with its flexible design and impressive performance. However, SAM's capabilities come at a cost: the model leverages a heavyweight image encoder (ViT-H in the standard model).
+
+In this paper we focus on segmentation for streaming use cases, for example robotics or augmented/virtual reality applications. With many frames arriving every second, it is likely that only one or a few entities need to be segmented in each image. Latency is critical, as frames may be separated in time by only tens of milliseconds. Depending on the relative locations of and communication channels between sensors and compute, bandwidth may also be a concern.
+
+![](images/ee0cb32d204e8d3f63080275484bc9d91aec4fcd384f522534ede351ed3b6d76.jpg)  
+Patch Tokenization  
+Figure 1. A visualization of the foveated tokenization employed by Segment This Thing. Pixels are grouped into small patches in the center and progressively larger patches towards the edges.
+
+![](images/2e368b77190a9a7191d1bc98a8eca84a90a4269fb0745580a930ef26930b6783.jpg)  
+Foveated Tokenization
+
+We introduce a new efficient segmentation model called "Segment This Thing" (STT) that was designed for streaming video applications. There have been many other follow-up works aimed at making SAM more efficient, but they have all done so by reducing the model size. We take a different approach, and instead apply a novel biologically-inspired tokenization scheme which results in a drastic reduction in token count and a concomitant increase in efficiency of the Transformer-based image encoder. This scheme, which we refer to as "foveated tokenization," is modeled after the variability of the visual acuity of human eye, which is highest in the fovea and decreases with increased eccentricity [3]. Instead of dividing the image into evenly sized patches on a uniform grid, we resample patches with variable resolution on an irregular grid centered on a single point prompt (see Figure 1). The further a patch is from the center, the lower its resolution.
+
+In the analogy with the human eye, it is as if the model directs its visual attention towards the prompt point and estimates the extent of the thing it is "looking at" (hence the name of our model). If the object is small or far away such that it subtends a small solid angle, it will be contained within the highest resolution patches and thus will not be affected by the downsampling (so long as there is sufficient
+
+context from the surrounding regions). If the object is large, it will extend into the periphery of the image and some information will be lost. However, we argue that for larger segments, high resolution is not as important. Our experiments show that our resampling of the image results in only moderate decreases in segmentation accuracy across a wide range of segment sizes. Importantly, there is no need to know the size of an object before attempting to segment it.
+
+Our foveated tokenization reduces the token count so significantly, we can actually use larger models relative to other efficient SAM variants while still requiring fewer FLOPs and thus lower latency, due to the quadratic scaling of self-attention with respect to the token count. This gives Segment This Thing more modeling flexibility and suggests that this tokenization and image encoding strategy could be applied gainfully towards other tasks besides segmentation.
+
+In addition to reducing latency, our foveated tokenization has the benefit of reducing the bandwidth required to get pixels from the sensor to the model. The tokenization results in a significant compression of the image, and is orthogonal to other compression schemes that could be applied to the patches (e.g. JPEG compression) for further bandwidth reduction. Furthermore, the resampling is computationally cheap and would be relatively easy to implement efficiently in hardware. Altogether, this makes the Segment This Thing model a promising option for power-constrained edge devices that need to stream data off-device to be segmented elsewhere.
+
+With foveated tokenization, each prompt leads to a unique foveation and thus requires running both the image encoder and the mask decoder, whereas models like SAM can amortize the cost of the image encoder across multiple prompts. However, our model is so much faster than the baselines that it would require a number of prompts on the same image before the cost is equivalent. Furthermore, the need to segment many objects is more common in static image analysis and less relevant to streaming video use cases.
+
+We benchmark Segment This Thing on a number of publicly-available segmentation datasets to quantify the performance degradation that comes with the increased efficiency. We also show qualitative experiments in which users wear glasses with front-facing cameras and eyetracking capabilities, applying STT to estimate what objects the user may be looking by using gaze-derived prompts.
+
+# 2. Related Work
+
+Our work is built on Segment Anything [20], which aims to segment any entity in an image given a prompt in the form of a bounding box or one or more 2D points. Our model aims to address the high computational cost of running the Segment Anything Model (SAM).
+
+Efficient Prompted Segmentation A number of other papers have also introduced techniques to reduce the cost of
+
+prompted segmentation. FastSAM [49] uses an efficient CNN-based object detection and segmentation backbone to produce segmentation candidates and then uses prompts to select from and / or merge these candidates into an output mask. SqueezeSAM [39] employs a UNet architecture with a transformer at the lowest-resolution level. Their early prompt fusion approach (providing prompts as additional channels in the input to the UNet rather than injecting them in a second stage) is similar to our centering of foveated inputs on the prompt, allowing the network to focus on relevant information from the start. EfficientViT-SAM [48] substitutes the ViT image encoder used by SAM for an EfficientViT [25] encoder. EfficientSAM [40], EdgeSAM [51], and MobileSAM [46] reduce the size of SAM by applying a knowledge distillation approach to transfer knowledge from the SAM image encoder into a lighter-weight encoder, then fine-tuning on the prompted segmentation task. For a recent review of efficient SAM methods, see Sun et al. [36].
+
+Prompted Video Segmentation One of the aims of our work is to reduce the cost of prompted segmentation such that it is reasonable to run on every frame of a streaming video. Recently, Ravi et al. introduced SAM-2 [33], an extension of SAM to prompted video object segmentation (VOS). However, with SAM-2 each prompt recovers a segmentation of only one object across the video, with multiple segmentations requiring multiple prompts. Our model can segment a different object each frame depending on where the foveation center is placed. We leave the extension of foveated tokenization to video input to future work.
+
+Resolution reduction In the deep learning era of computer vision it has always been standard practice to rapidly reduce resolution in early network layers. For example, AlexNet [23] begins with a stride-4 convolution followed by a stride-2 pooling, reducing an input image of size  $224 \times 224$  to a feature map of size  $26 \times 26$  after just these two layers. This did not change as hardward advanced; ResNets [15] begin with a convolutional layer and a pooling layer each with a stride of two pixels, resulting in a quartering of the image resolution after progressing through a small fraction of the network depth. Our approach also reduces input resolution at the beginning of (in fact, before) the network, but with a rate that increases with increased distance from the prompt.
+
+Compression Other work reduces data sizes by learning from compressed representations. For example, Park and Johnson [31] train a Transformer-based image classifier directly on DCT co-efficients as used in JPEG encoding of images. Horton et al. take this idea even further and train a classifier directly on compressed byte encodings of images [17]. Other methods such as VQ-VAEs [38] or the more recent Lookup-Free Quantization [44] allow learning a compression of input data which can then be leveraged to solve downstream tasks more efficiently. However, depending on the specific architecture these techniques can incur
+
+significant costs to compute the compressed representations and are therefore not as widely applicable to deployment on power-constrained edge devices. Our foveated tokenization can be seen as a simple compression of the input image, but the compression technique is extremely cheap and largely orthogonal to these other approaches (i.e. one could apply further compression to the foveated patches).
+
+Efficient Tokenization Other prior works similarly addressed the issue of large token counts in transformer architectures. Yu et al. introduced TiTok [45], which generates a concise set of 32 or 64 tokens to represent an image. Yu et al. also make note of the ability to increase model size when token counts are low, but focused on the downstream task of image generation – the TiTok tokenizer itself is a Vision Transformer and is thus not particularly efficient. A large number of different token pruning or merging approaches have been proposed [2, 5, 13, 21, 27, 28, 32, 34, 35, 41, 42]. These methods typically assume a full input image, starting with a full token set and progressively reducing the token count from there. They therefore do not offer the same benefits resulting from input size reduction as our approach.
+
+Foveated Vision There have been some prior works applying foveation models to deep learning. Jonnalagadda et al. introduced FoveaTer [18], which uses variably-sized pooling windows applied to the output of a convolutional network to produce foveated feature maps. These foveated features are then processed by a Transformer, which like our image encoder benefits from the reduction in token count. However, the convolutional backbone of FoveaTer still requires full resolution images, which places higher constraints on sensors and bandwidth compared to our approach, in which foveation is applied as a first step (and can therefore be implemented on the sensor itself or in nearby accelerators). Peripheral Vision Transformer [29] also takes inspiration from biological vision systems, but focuses on the inductive bias aspect of foveation by modifying the position encoding of a Transformer-based image classifier. They do not achieve efficiency gains relative to their baseline. PeLK [4] applies the foveation concept to convolutional neural networks, introducing adaptive weight sharing that allocates more weights to the center of a large convolutional kernel and fewer towards the edges. This work actually moves in the opposite direction of ours by decreasing parameter counts; we instead decrease input size and increase parameter counts. Furthermore, the above works are evaluated on older datasets with small images such as ImageNet [10], ADE20K [50], or MS-COCO [24]. With our focus on efficiency, we evaluate Segment This Thing on more modern datasets with much larger images, showing the ability to segment objects ranging in size from a handful of pixels to millions of pixels with very low latency.
+
+Konrad et al. also explore foveated imaging with GazeGPT [22]. GazeGPT also investigates gaze-based
+
+![](images/45bbe3a97cffbe23bf55995fba925ac9391ef779219350aa75212d7661599b92.jpg)  
+Figure 2. (left) The foveation pattern used by our trained models. (right, top) A patch from the center maintains its original resolution. (right, bottom) A patch from the outer ring gets downsampled by a factor of 8. Best viewed digitally.
+
+![](images/65b9e25aef1f79d9a94e383093d97f8c2f282c8851d1107b8152c8c33527a171.jpg)
+
+prompting, in their case of a multimodal large language model. This model was pre-trained on uniform resolution images, which constrains the GazeGPT foveation model (it uses three overlapping images of the same size but varying receptive field).
+
+# 3. The Segment This Thing Model
+
+In this section we introduce the Segment This Thing model. First, we describe the foveation model used to map full resolution images to foveated token sets. Then we describe the architecture that maps these tokens to segmentation maps.
+
+# 3.1. Foveated Tokenization
+
+Each instance of the Segment This Thing model is trained with a fixed foveation pattern which divides an image region into a set of patches. An example of such a pattern is depicted in Figure 2. In the center of the pattern is a dense grid of patches corresponding to the center of the fovea in the analogy with human vision. Surrounding this block is a series of concentric rings of larger patches. The patches in each ring are larger than patches in the preceding ring and have a size that is an integer multiple of the central patch size. The patch sizes and the width of each ring are selected such that they can be nested without gaps or overlap<sup>1</sup>.
+
+Given an image and a point prompt, the first step is to extract a crop centered on the prompt and sized according to the pattern. If the crop region extends beyond the image boundary, we pad the image. Next, each patch in the foveation pattern is downsampled to the central patch size. This can be implemented efficiently using an integral image, which is equivalent to applying a box filter and subsampling with integer strides. This simplistic filter results in lower image quality than more sophisticated methods, but is hardware-friendly such that it could be deployed on power-constrained edge devices. The downsampling process is visualized in Figure 2. We follow SAM and use  $16 \times 16$  patches.
+
+![](images/8adedbb155f2feba90dff87c140c6a96e0dd3c5d5b9f1ec1013f2a555617dbc6.jpg)  
+Figure 3. The foveated tokenization process. It begins by cropping a square region around the point prompt with a fixed size determined by the foveation pattern. This pattern divides the crop region into a set of variably-sized patches, each of which is resampled such that they are all the same size. The set is then concatenated to form a token tensor.
+
+After downsampling, the patches are all the same size, as is the output of the standard patch tokenization used e.g. in Vision Transformers [11]. We can thus flatten each patch into a vector and stack them to form a token matrix, hence the name foveated tokenization. The token set is then passed to the Segment This Thing model for processing. The full tokenization pipeline is depicted in Figure 3.
+
+# 3.1.1. Visualization
+
+It is often useful to visualize the foveated input to Segment This Thing. However, after foveated tokenization the image is represented as a  $N \times T \times T$  tensor, where  $N$  is the token count and  $T$  is the patch size. To visualize such a tensor, we reverse the tokenization process: patches are upsampled back to their original size (typically using nearest neighbor interpolation) at which point they can be recomposed into an image. Note that while this process does not re-introduce any new information and thus provides a faithful view of what the network sees, it does introduce some distortion — the central regions take up much more space in the network input than in the visualizations thereof.
+
+![](images/6d2174e5978d08e7464d3fbf6658e6865a50287cb00e2e8b1778556d549f75bf.jpg)  
+Figure 4. Segment This Thing has a precision advantage for extremely small targets due to the additional decovolutional layers in the mask decoder. Left: an input image with prompt indicated in red. Right: a visualization of the segmentation output of SAM and STT. The crop corresponds to the region indicated by a rectangle at left. STT is able to output a smaller segment than SAM due to its higher resolution output in the central tokens. Note that this is not the most confident output mask for either model.
+
+![](images/f8255b2228b189b2d514b2b5a8ee5a0f8049eb2e5f226ab8404a357bc4b7c3c3.jpg)
+
+# 3.2. Segment This Thing
+
+Next we describe the architecture of the Segment This Thing model. It is largely based on the three-part SAM model. However, the prompt encoder has been omitted because the prompt is implicit in the centering of the crop.
+
+# 3.2.1. Image Encoder
+
+Due to the foveation, the input tokens to STT correspond to patches which are not arrayed on a 2D grid and thus the Vision Transformer model used by SAM (with local 2D windowed attention) is not applicable. However, the complexity of windowed attention was used in SAM to control compute cost, and our drastic cut in the token count removes that imperative. The image encoder for STT is thus a standard Transformer model. We simply project the patches (after downsampling to a common patch size) with a single linear layer, add learned position encodings, and pass them through the transformer to obtain a (foveated) feature map. We also include a register token to allow the model to collect and broadcast information, following Darcet et al. [8]. As the foveation pattern can extend beyond image boundaries, we also add a token mask to prevent the participation of out-of-bounds tokens in self-attention.
+
+# 3.2.2. MaskDecoder
+
+The mask decoder is also modeled on its counterpart in SAM, again with some simplifications. We do not support iterative segmentation – the model is always run just once per prompt. In SAM, the model outputs  $N$  (typically  $N = 3$ ) masks when prompted with a single point (which leads to scale ambiguity) and one mask when prompted with multiple points or a bounding box. Segment This Thing only supports single point prompts, so the decoder always outputs  $N$  masks. The query token set is thus a set of  $N + 1$  tokens, one for the intersection over union (IoU) prediction and one for each predicted mask. The context token set is the output of the encoder (including the register token). Out-of-bounds context tokens are masked during cross attention with the same token mask used in the encoder. The decoder otherwise operates in the same way as its counterpart in SAM, applying a two-way cross-attention trans
+
+![](images/7bca565e8a13f4b60789c5b4e8ecb3a4aeeb31cf849235bc23ff1c965e5cb344.jpg)  
+Figure 5. Despite the high downsampling rate in the periphery, the Segment This Thing model is capable of segmenting objects with a wide range of shapes and sizes, from those covering most of the receptive field (left) to those covering just a handful of pixels (right). All images are from SA-1B. They have been passed through foveated tokenization and are visualized as described in Section 3.1.1.
+
+![](images/db1b87c170f011896a51651f7ede96708577147f5fc7af751eb11b9265eace27.jpg)
+
+![](images/8574738d2bd69964550783bd4802b3bbe5d15720d1f6c10eaecbd77c3c8b0ef9.jpg)
+
+![](images/6caaae880352483b3d9f4292e99a6908ff23941349e9bad048c092d52d04f2e7.jpg)
+
+![](images/06fea92bf2485411e64cb7328102dc10d61afadbf48104c1d6d0eb08dfc218d8.jpg)
+
+![](images/6ead748982b5701df0f739ef3290670dd561f5beb67c7157776f8356dea35095.jpg)
+
+former, followed by learned upsampling of the image tokens, and finally a dot product between the mask tokens (transformed by an MLP) and the image token maps to produce the segmentation map.
+
+In SAM, the segmentation maps are estimated at a quarter of the resolution of the input image by applying two  $2 \times 2$  deconvolutions with stride 2 to the token feature map. In other words, a  $4 \times 4$  segmentation label patch is estimated for each  $16 \times 16$  token. If full resolution maps are required, they are computed using bi-linear interpolation. This design decision was likely made to reduce memory costs during training.
+
+Another benefit of our lower token count is that we can afford to produce a full  $16 \times 16$  segmentation map per patch by stacking four deconvolutions, each of which double the resolution. Thus the labels for the central patches are estimated at full resolution, with the output resolution dropping off towards the periphery at the same rate as the input resolution. If a full resolution segmentation map is required, it can be computed by interpolating each patch such that it matches its receptive field and then restructuring the patches to form an image. Note that this means that the output resolution of STT is actually higher than SAM in the center
+
+![](images/b940be34dd3e79b4feb35a666b9c5ecdd42c34cbad22912c198f87ef4fb45ba1.jpg)  
+Figure 6. A visualization of a training example for MAE pretraining of the image encoder. Left: the foveated input image (and reconstruction target), visualized as described in 3.1.1. Center: a visualization of the token set passed to the encoder. Right: a visualization of the reconstruction produced by the network. Note that the output resolution of the decoder matches the target image pixel-for-pixel, i.e. it is also foveated.
+
+![](images/4c06a4630fc7c90b2516ef0df7995112108a40830553cf654776af948a8fd18c.jpg)
+
+![](images/e129d6e4430a27e15d6463d7cc29f20ab3b6804407ec5438fe4e7539a1a26afb.jpg)
+
+of the segmentation map, at parity in the surrounding areas, and only at a lower resolution in the periphery. We have observed at least some cases where this allows STT to segment extremely small entities that SAM misses (see Figure 4; we suspect that this capability is limited by insufficiently granular labels in SA-1B, caused in part by the use of SAM itself to generate the masks). STT is also able to segment large objects, including segments that fill most or all of the foveation pattern, albeit at lower resolution. See Figure 5 for examples of segments estimated by STT across a wide range of scales.
+
+# 4. Experiments
+
+In this section we describe how we train the Segment This Thing model, and present evaluation results.
+
+# 4.1. Training
+
+We trained our STT models using a foveation pattern with 172 tokens, a total size of 1280 pixels, and a stride of 8 pixels in the outer ring. See the supplementary materials for details. In Table 1 we compare the input sizes for SAM and our STT model based on this pattern.
+
+Following SAM, we trained our Segment This Thing model exclusively on the SA-1B dataset [20]. However, because of the novel tokenization we cannot rely on an existing pre-trained image encoder as do Kirillov et al. Thus the first step in training STT is masked autoencoder (MAE) [16] pre-training of the image encoder. Other than the novel tokenization, we follow the standard MAE pre-training procedure. We center foveations at points randomly selected from SA-1B images that are more than a threshold distance
+
+<table><tr><td>Model</td><td>Tokens</td><td>Pixels</td><td>Receptive Field</td></tr><tr><td>SAM [20]</td><td>4096</td><td>1.049M</td><td>10242</td></tr><tr><td>STT (Ours)</td><td>172</td><td>0.044M</td><td>12802</td></tr></table>
+
+Table 1. A comparison of the input size for SAM and STT.
+
+![](images/96fb24b0d4de269f54f2d43b103c968c9da56d01fe3fe0c88ea721888ced5736.jpg)  
+Figure 7. Results of evaluating our models and baselines on publicly available segmentation datasets. Models of the same family are grouped by hue, with larger models in darker shades. Results vary widely by dataset, but the trend is consistent and shows that STT outperforms MobileSAM, which runs with similar latency, and is competitive with more expensive models. See Table 5 in the supplementary for a full tabular listing of the results.
+
+from the image boundary. The reconstruction loss is applied to the in-bounds and downsampled tokens — we do not ask the network to predict the full-resolution source image. An MAE pre-training example is visualized in Figure 6.
+
+After pre-training, the decoder is discarded and the encoder weights are used to initialize the STT encoder. We then use SA-1B to train the mask decoder and fine-tune the encoder weights for the segmentation task. For each image, we select a set of segments uniformly at random, and for each segment select a foveation center uniformly within the segment. We use the same combination of Focal, Dice, and IoU prediction losses as SAM. However, we made two minor changes to handle the variable resolution of the STT output masks.
+
+First, each pixel in the STT output may represent a classification of many pixels in the binary segmentation target. Rather than upsampling the masks to the full image resolution as described in section 3.2.2, we map the ground truth to the foveated output space of STT. This is done by converting the masks to floating point and applying the same downsampling approach as used for input pixels described in section 3.1. The result is a real-valued foveated segmentation map. Each value in the map has a variably-sized receptive field and represents the probability that a pixel selected uniformly at random from within that receptive field has a positive label in the full-resolution ground truth segmentation map. After downsampling there is a 1:1 correspondence between pixels in target and estimated masks, and we can apply the Dice and Focal losses, both of which generalize to continuous target values.
+
+The second change is to the targets for IoU prediction. We found that thresholding the target and estimated masks and computing IoU of the binarized masks led to unstable loss values when significant portions of either mask are near a value of 0.5. We thus use an expected IoU of the real-valued segmentation maps. Given an estimated probability  $p_i$  that pixel  $i$  is part of the segment and probability  $q_i$  that a pixel selected uniformly at random from its receptive field
+
+<table><tr><td colspan="2">Method</td><td>Latency (ms)</td><td>GFLOPs</td></tr><tr><td rowspan="3">SAM [20]</td><td>H</td><td>572.7</td><td>6533.7</td></tr><tr><td>L</td><td>347.6</td><td>3244.5</td></tr><tr><td>B</td><td>153.9</td><td>1027.0</td></tr><tr><td rowspan="2">EfficientSAM [40]</td><td>S</td><td>78.6</td><td>489.4</td></tr><tr><td>Ti</td><td>39.8</td><td>201.2</td></tr><tr><td>MobileSAM [46]</td><td></td><td>20.7</td><td>124.4</td></tr><tr><td rowspan="3">STT (Ours)</td><td>H</td><td>26.2</td><td>223.2</td></tr><tr><td>L</td><td>13.7</td><td>108.0</td></tr><tr><td>B</td><td>7.3</td><td>30.9</td></tr></table>
+
+Table 2. Comparing the efficiency of evaluated segmentation models. Latency is measured as the time to process a single image with a single prompt on an nVidia RTX 3080 GPU, and excludes preprocessing time for all models. See supplementary materials for details on calculating GFLOPs. Note that latency is highly dependent on hardware (and, to a lesser extent, software) and that there are competing ways to compute FLOPs; we were careful to run all models on the same hardware and used the same method of computing FLOPs, and can see that FLOPs and latency are roughly linearly correlated as expected.
+
+has a positive label, the expected IoU is given by:
+
+$$
+\frac {p _ {i} q _ {i}}{1 - \left(1 - p _ {i}\right) \left(1 - p _ {q}\right)} \tag {1}
+$$
+
+See the supplementary materials for further training details, including hyperparameter settings.
+
+# 4.1.1. Foveation Center Selection
+
+The randomized selection of the foveation center and the resulting variability in the resampling of the image plays much the same role as data augmentation. However, rather than altering the data contained in the training set, randomized foveation simply provides many different views of the same data. While we revisit the same images from SA-1B repeatedly throughout training, the model almost certainly
+
+![](images/d655495d499e58f2cc2f01c3d52cb500f1fe2ecca2ac0c297bf4287911d5db57.jpg)  
+SAM
+
+![](images/f2099d81f723cce36def48dfc3c82b0bc92530634f08d19746e01ae19ff94ba7.jpg)
+
+![](images/7ba540929b7dcc1a28721294964531a05fb5d17c16ba6968761ce14fa9ec04a9.jpg)  
+Figure 8. A side-by-side comparison of our Segment This Thing model on a set of images from the SA-1B dataset [20]. We also visualize the foveated input in the rightmost column as described in Section 3.1.1. This figure is best viewed digitally — zoom in to see the relative coarsity of STT's output for larger segments. For example, the ear of the goat in the third row is missed by STT.
+
+![](images/e09aaffcd5ad0b733e641cc390636a71e98e511e4611148c373e24c6f126e4e2.jpg)  
+EfficientSAM
+
+![](images/3ab3a37b5df79f874e578acb5034b6f43822e816e16195c9d9a659e1d8d9e111.jpg)
+
+![](images/9163d1b75af74771c7efe1b11cbc550f8aa41532f2a2857d8aecdfad7729408d.jpg)
+
+![](images/b9a83a8f104649247cb3e7bde8b0d27426429ed4fe6ec7291d0511e848c6f685.jpg)  
+MobileSAM
+
+![](images/a28f82cea6c48e1ba8b880b594771d1054e27360d26b839624904852cfe52c17.jpg)
+
+![](images/3f00ccaecdb0a5ca766927c23a2af4909cf845701f706c706eb2555d5bed49a1.jpg)
+
+![](images/c6e5ddead0eff0fd63d2885b7418d1a7799cd9a42f524e34cf44ce68e98b0c34.jpg)  
+STT (Ours)
+
+![](images/a537152f62eeb8559a694238de99d42852beb5144f7e5e9b657963d41b3a0403.jpg)
+
+![](images/adc01f5ccb2a36c10a50e4d0c8ce3c450d77eff8beb5dd5066fe6d4f0c6e8d02.jpg)
+
+![](images/427638cdc194b784b5702f0b9222f38dfa113c123fae11c2570322cf473ee21e.jpg)  
+Foveated Input
+
+![](images/eb2fa8619e490b630e2ed49ba8288388bd5dda2e9f594a08d1bfa6fd8b36c40e.jpg)
+
+![](images/8d73f3425d15e8fb45b099914a84e58c713cd7ab19af6ed6fd795005780d6bca.jpg)
+
+never receives exactly the same foveated input twice. We train without drop path as used by Kirillov et al., but have not observed issues with overfitting.
+
+In some cases, one may wish to prompt a segmentation model with some uncertainty as to whether the prompt is within the intended segment. In training STT, we can optionally add a small Gaussian-distributed offset vector between the point selected from within a target segment and the center point used for prompting the model. In this setup, the model is sometimes prompted with points somewhat outside the target segment. The model then learns to produce not only segments that include the prompt point but also other nearby segments. The same strategy employed by SAM to handle size ambiguity (i.e. allowing the model to produce multiple masks, and backpropagating only gradients of the loss associated with the best mask) also naturally handles the ambiguity introduced by prompt noise. If the model is prompted with an image that contains plausible segments both at and near the center, it can simply produce both segments as output.
+
+# 4.2. Evaluation
+
+We evaluate the Segment This Thing model on the "Zero-Shot Single Point Valid Mask Evaluation" task defined by Kirillov et al. [20] in which the model is prompted with a single point in each segment that is furthest from the segment boundary. We measure accuracy using the mean intersection over union (mIoU) across all segments in each evaluation dataset.
+
+We compare against SAM [20] and the faster EfficientSAM [40] and MobileSAM [46] as baselines, all of
+
+which involve resizing the image to  $1024 \times 1024$  pixels, encoding a point prompt, and decoding a mask at  $256 \times 256$  pixel resolution. To evaluate STT, we do cropping and resampling as usual, but map each output mask back to the input image pixel space before evaluating the IoU. During training, both the height and width of training images are larger than the pattern size, such that crops centered near a corner may need padding on up to two sides. Some of the evaluation images are significantly smaller, however. To avoid a domain gap, we upsample images as necessary before cropping such that the crop needs padding on at most two sides.
+
+We analyze the efficiency of our model and the baselines in Table 2. Note that STT requires fewer FLOPs and thus achieves lower latency than other efficient variants of SAM while retaining model capacity.
+
+We use a set of nine datasets to evaluate segmentation accuracy, and plot the results for multiple sizes of each model in Figure 7. We adopt the standard transformer size denotations used by Vision Transformers [11]. Our STT-L model significantly outperforms STT-B, an indication that maintaining model capacity is important to the performance of our model. However, we find diminishing returns with STT-H. Our models all significantly outperform MobileSAM, which has similar latency to STT-H. STT-L is competitive with EfficientSAM-Ti in terms of accuracy, but despite the larger parameter count is still nearly  $3\mathbf{x}$  faster. This is again due to our reduced token count.
+
+A qualitative side-by-side comparison of the three models is shown in Figure 8. Note that some of the performance gap between STT and the more expensive models is
+
+![](images/633084403543624f17a93d688645529d696a075fe891636b266fa3f547f11cf8.jpg)  
+Figure 9. A visualization of prompting Segment This Thing with gaze. In a sequence from the Nymeria dataset [26], a user does a puzzle while wearing Aria glasses [12] equipped with eye-tracking capabilities and a forward-facing camera. The wearer's gaze is inferred and projected into the camera frame, and the resulting point is used to prompt Segment This Thing. A model trained with noise on the foveation center is capable of segmenting objects that the user is looking at even when the gaze is not entirely accurate. Left: the wear is likely looking at a puzzle piece, but the estimate of their gaze projects onto a nearby point on the table (shown in red). Center: in this zoomed-in view, we can see that the most confident segment in STT's estimation is the puzzle piece. Right: a few frames later, the wearer reaches for this exact piece, lending evidence that this indeed had been the object of the wearer's attention.
+
+![](images/4ff7f5e11333083e3fa7e6208c42172413a0a0463f73f8f299c2eab0b1f1da4b.jpg)
+
+![](images/4c96f6a14008129f941e27a2a0d335d8b49e2f1f0b3455408855632ed1a303c6.jpg)
+
+due to the relatively coarse segment boundaries caused by foveation (see the supplementary material for further analysis of the performance of STT as a function of segment size). However, we expect that this will not be critical for many downstream tasks. If a precise boundary is important, one can always prompt the model again near the boundary once its approximate contours are known.
+
+# 4.3. Gaze-based Prompting
+
+We also show qualitative results in which we prompt the Segment This Thing model with gaze data in Figure 9 and in the supplementary video. Specifically, we take sequences from the Nymeria dataset [26] in which users wear Aria glasses [12] while performing a variety of activities. The glasses have forward-facing cameras capturing the wearer's egocentric perspective as well as cameras aimed at the their eyes enabling gaze tracking. The estimated gaze vectors can then be re-projected into the frame of reference of the forward-facing camera image and used as a prompt for STT. However, there is some noise in the estimation of the wearer's gaze and therefore some uncertainty in the prompt. Instead of prompting the model with multiple points drawn from the distribution over the true gaze point, which would incur a cost that scales linearly in the number of prompts, we use a model trained with noise as described in Section 4.1.1. With this model, using a single prompt placed at our best estimate of where the wearer is looking still often results in a segmentation of the object they are actually looking at. Furthermore, if there is only one obvious segment near the gaze vector, as might be the case when the wearer gazes at an object against a featureless background, this segment will in many cases be the highest scoring segment according to the model's IoU predictions. The example shown
+
+in Figure 9 is such a case.
+
+# 5. Conclusion
+
+In this paper we introduced the Segment This Thing model, a new image segmentation model that builds on the success of SAM by increasing efficiency. Instead of simply reducing the size of the Transformer-based image encoder, we applied foveated tokenization to reduce the token count by roughly 24x compared to SAM. This reduces inference latency by roughly 42x compared to SAM-H or 5.6x relative to EfficientSAM. Foveated tokenization has the additional benefit of reducing the number of pixels required as input to the network, easing up on bandwidth requirements for transmitting image data between sensors and devices; our model requires only about 44K pixels as input (roughly equivalent to a  $210 \times 210$  image), but retains the ability to segment small objects at full resolution.
+
+Despite the increased efficiency, Segment This Thing remains competitive on mIoU metrics on multiple public datasets. By maintaining a relatively large parameter count, our model retains the ability to do more complex computation over the reduced token set compared to smaller models that do simpler computations over more tokens. This helps our models cope with the information lost due to foveation. Furthermore, foveated tokenization concentrates the computational cost where it matters most: near the prompt.
+
+Altogether, the Segment This Thing model is a promising choice for streaming video use cases. While we have only evaluated foveated tokenization in the context of image segmentation, we believe the approach could be equally applicable to a variety of image and video processing tasks. We leave this exploration to future work.
+
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+# Segmenting Maxillofacial Structures in CBCT Volumes
+
+Federico Bolelli<sup>1, *</sup>, Kevin Marchesini<sup>1, *</sup>, Niels van Nistelrooij<sup>2, 3</sup>, Luca Lumetti<sup>1</sup>, Vittorio Pipoli<sup>1</sup>, Elis Ficarra<sup>1</sup>, Shankeeth Vinayahalingam<sup>2</sup>, Costantino Grana<sup>1</sup>
+
+<sup>1</sup>University of Modena and Reggio Emilia, Italy  
+<sup>2</sup>Radboud University Medical Center, the Netherlands  
+<sup>3</sup>Charité - Universitätsmedizin Berlin, Germany
+
+# Abstract
+
+Cone-beam computed tomography (CBCT) is a standard imaging modality in orofacial and dental practices, providing essential 3D volumetric imaging of anatomical structures, including jawbones, teeth, sinuses, and neurovascular canals. Accurately segmenting these structures is fundamental to numerous clinical applications, such as surgical planning and implant placement. However, manual segmentation of CBCT scans is time-intensive and requires expert input, creating a demand for automated solutions through deep learning. Effective development of such algorithms relies on access to large, well-annotated datasets, yet current datasets are often privately stored or limited in scope and considered structures, especially concerning 3D annotations. This paper proposes ToothFairy2, a comprehensive, publicly accessible CBCT dataset with voxel-level 3D annotations of 42 distinct classes corresponding to maxillofacial structures. We validate the dataset by benchmarking state-of-the-art neural network models, including convolutional, transformer-based, and hybrid Mamba-based architectures, to evaluate segmentation performance across complex anatomical regions. Our work also explores adaptations to the nnU-Net framework to optimize multiclass segmentation for maxillofacial anatomy. The proposed dataset provides a fundamental resource for advancing maxillofacial segmentation and supports future research in automated 3D image analysis in digital dentistry.
+
+# 1. Introduction
+
+Medical imaging is currently employed in the clinical practice of dental treatments. Different modalities are available to assist in diagnosis, treatment planning, and surgery:
+
+![](images/c6497b378783967f7652036f8dd6199e6639712c99dd347eec98ed8f4a680ced.jpg)  
+Figure 1. Fully annotated sample from the proposed dataset.
+
+2D panoramic X-rays, 3D Intra-Oral Scans (IOS), and 3D Cone-Beam Computed Tomography (CBCT) [29]. Among the available modalities, CBCT is the only one providing comprehensive 3D volumetric information on all the anatomical structures in the orofacial area, including jawbones, complete teeth, sinuses, and neurovascular canals, becoming a standard modality for maxillofacial image analysis. Segmenting individual structures from CBCT images to reconstruct a precise 3D model is essential in digital dentistry for various clinical applications, including surgical planning and implant placement [16, 53].
+
+Manual segmentation of CBCT scans is a time-consuming and labor-intensive process that requires specialized expertise. For this reason, automated algorithms using deep learning techniques offer a promising solution to alleviate this burden, enhance efficiency, and improve the consistency of segmentation results [41]. However, developing robust deep learning models heavily depends on the availability of large, annotated datasets. Unfortunately, existing datasets often focus on a limited number of anatomical structures — i.e., teeth or alveolar canal — and usually lack 3D annotations. The few available methods trained on
+
+Table 1. Datasets used in literature to segment maxillofacial anatomical structures. Our analysis is limited to 3D imaging modalities, i.e., CBCTs and IOS.  $\clubsuit$  means that only a portion of the data is available to the research community, i.e., 148 over 4531. Among these, only 97 are effectively usable for the training of automatic algorithms.  
+
+<table><tr><td rowspan="2">Anatomical Structure(s)</td><td rowspan="2">Image Modality</td><td rowspan="2">Authors</td><td rowspan="2">Year</td><td rowspan="2">Country</td><td rowspan="2"># Train &amp; Validation</td><td rowspan="2"># Test</td><td colspan="2">Label Type</td><td rowspan="2">Public</td></tr><tr><td>Train</td><td>Test</td></tr><tr><td rowspan="6">Inferior Alveolar Canal (IAC)</td><td rowspan="6">CBCT</td><td>Jaskari et al. [28]</td><td>2020</td><td>Finland</td><td>509</td><td>128</td><td>2D</td><td>128 2D, 15 3D</td><td>X</td></tr><tr><td>Lahoud et al. [33]</td><td>2022</td><td>Belgium</td><td>205</td><td>30</td><td>3D</td><td>3D</td><td>X</td></tr><tr><td>Usman et al. [48]</td><td>2022</td><td>South Korea</td><td>510</td><td>500</td><td>3D</td><td>3D</td><td>X</td></tr><tr><td>Cipriano et al. [11]</td><td>2022</td><td>Italy</td><td>332</td><td>15</td><td>332 2D, 76 3D</td><td>15 2D, 15 3D</td><td>✓</td></tr><tr><td>Chun et al. [10]</td><td>2023</td><td>South Korea</td><td>32</td><td>18</td><td>3D</td><td>3D</td><td>X</td></tr><tr><td>Bolelli et al. [6]</td><td>2023</td><td>Italy, Netherlands</td><td>443</td><td>50</td><td>443 2D, 153 3D</td><td>3D</td><td>✓</td></tr><tr><td rowspan="4">Teeth Crown, Teeth Root</td><td rowspan="4">CBCT</td><td>Cui et al. [16]</td><td>2022</td><td>China</td><td>4531</td><td>407</td><td>3D</td><td>3D</td><td>◇</td></tr><tr><td>Cui et al. [13]</td><td>2022</td><td>China</td><td>22</td><td>~</td><td>3D</td><td>~</td><td>✓</td></tr><tr><td>Dou et al. [17]</td><td>2022</td><td>China</td><td>35</td><td>5</td><td>3D</td><td>3D</td><td>X</td></tr><tr><td>Tae Jun Jang et al. [26]</td><td>2022</td><td>China</td><td>66</td><td>7</td><td>66 3D, 31 2D</td><td>7 3D, 4 2D</td><td>X</td></tr><tr><td rowspan="2">Teeth Crown</td><td rowspan="2">IOS</td><td>Ben-Hamadou et al. [3]</td><td>2022</td><td>Tunisia</td><td>1200</td><td>600</td><td>3D</td><td>3D</td><td>✓</td></tr><tr><td>Shankeeth et al. [49]</td><td>2023</td><td>Netherlands</td><td>1400</td><td>350</td><td>3D</td><td>3D</td><td>X</td></tr><tr><td>IACs, Teeth, Others</td><td>CBCT</td><td>Ours</td><td>2024</td><td>Italy, Netherlands</td><td>480</td><td>50</td><td>3D</td><td>3D</td><td>✓</td></tr></table>
+
+3D datasets leverage very small-sized training and testing sets ( $< 50$  CBCTs) [10, 13, 17], most of the time unavailable to the research community, limiting their generalizability to scans acquired with other imaging protocols and their applicability to diverse patient populations.
+
+In this paper, we introduce a novel and unique dataset with 3D annotations of 42 distinct anatomical classes on maxillofacial CBCTs. The classes include jawbones, left and right Inferior Alveolar Canals (IACs), left and right maxillary sinus, pharynx, upper and lower teeth, including wisdom teeth, bridges, crowns, and dental implants. The proposed comprehensive dataset provides a rich resource for developing and benchmarking segmentation algorithms that require 3D anatomical annotations, providing a strong basis for the future development and evaluation of automated methods for maxillofacial image analysis (Fig. 1).
+
+To validate the dataset, we further explore the performance of various state-of-the-art neural networks used in the literature for medical image segmentation: classical Convolutional Neural Networks (CNNs) like nnU-Net and its variants [24], transformer-based models such as TransU-Net [7] and UNETR++ [47], and hybrid Mamba-based architectures like UMamba [38], Swin-UMamba [34], and VMamba [35].
+
+Specifically, we explore how to adapt and optimize the nnU-Net framework [24], a state-of-the-art, self-configuring segmentation method, to enhance the segmentation of all 42 anatomical structures in our dataset, by adjusting hyperparameters and augmentation strategies.
+
+By leveraging our comprehensive dataset, we aim to advance the development of segmentation algorithms that can handle the complexities of 3D maxillofacial imaging. Our work not only contributes a valuable dataset to the research community but also provides insights into the optimization of neural network architectures for improved segmentation performance in complex anatomical regions.
+
+In summary, our contributions are:
+
+- The largest publicly available CBCT dataset with detailed 3D annotations covering 42 distinct maxillofacial anatomical structures, addressing a critical gap in existing research resources;  
+- A comprehensive comparative evaluation of state-of-the-art neural network architectures, encompassing convolutional networks, transformers, and hybrid models, to establish performance benchmarks using our dataset;  
+- Adaptation and optimization of the nnU-Net framework to target multi-class segmentation in complex maxillofacial anatomy, highlighting the impact of customized augmentation and hyperparameters tuning.
+
+# 2. Related Works
+
+# 2.1. Existing datasets
+
+In maxillofacial analysis, existing research predominantly emphasizes developing AI applications that demonstrate high accuracy on carefully selected datasets; however, limited consideration has been given to examining the datasets employed in the training and evaluation phases of AI model development [45]. Current research has primarily used academic databases, which may not fully address AI-specific data needs, including the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles [52]. Only a few dental studies make datasets available to the research community [6, 12, 13, 16], making it essential to seek alternative data sources to mitigate these limitations and to prioritize the creation of high-quality, medically validated, and AI-ready datasets. All of these datasets are confined to specific anatomical structures, particularly the IAC [12] or the teeth complex [16], and others provide binary segmentation classes only, without considering tooth-level labels [16].
+
+Tab. 1 presents a comprehensive comparison of datasets used in state-of-the-art research.
+
+# 2.2. Teeth segmentation
+
+Multiple approaches to segmenting teeth in 3D data have been proposed, each introducing different challenges in clinical and computational applications [8].
+
+Binary segmentation. When employing a simple binary semantic segmentation, the primary goal is to separate teeth from surrounding tissues without any distinction between individual teeth. While it is useful for basic assessments, its clinical utility is limited, especially when dealing with adjacent or closely spaced teeth. To improve accuracy, recent methods have utilized CNN-based architectures with custom modules, hybrid loss functions [21], a combination of 2D and 3D networks [20], and post-processing techniques, such as posterior probability maps and dense conditional random fields [42].
+
+Instance segmentation. By contrast, when performing instance segmentation, teeth are segmented as separate instances, though they have not yet been assigned specific labels. Instance segmentation methods usually leverage a two-stage approach consisting of detection followed by segmentation. The detection stage locates individual tooth instances through various techniques, including bounding box detection [14, 18], heatmap prediction [54], or offset regression [15-17]. Once identified, localized regions around each tooth instance can be extracted to perform binary segmentation and identify precise boundaries.
+
+Multi-class segmentation. With this approach, each tooth must be identified and assigned a specific label [32, 46] according to the FDI (Fédération Dentaire Internationale, also known as World Dental Federation) notation [25]. This is the most challenging of the three approaches, especially in cases with a complex anatomy, such as missing and impacted teeth, and will likely continue to benefit from more robust datasets and enhanced model architectures, motivating once again our proposed dataset.
+
+Current literature can be split into single- and multi-stage methods. While the former leverages a single framework to produce the final classification, multi-stage methods incorporate additional steps. The more commonly employed strategy consists of downsampling the original CBCT or splitting it into multiple regions (e.g., the four dental arch quadrants) to generate a coarse multi-class segmentation, later refining individual tooth segments by isolating volumes of interest and enhancing local details [46, 50].
+
+# 2.3. IAC segmentation
+
+Since the advent of CBCT technology in the early 2000s, substantial effort has been dedicated by the scientific community to developing automated systems for segmenting the inferior alveolar canal from 3D scans [44]. With the rise of deep learning in medical diagnosis, learning-based networks [23, 27, 28] have outclassed traditional computer vision techniques [1, 4, 30, 31, 40, 51].
+
+Early work by Jaskari et al. [28] applied the U-Net [43] architecture on a coarsely annotated dataset, achieving promising results compared to traditional methods, though limited by the lack of dense annotations.
+
+Lahoud [33] advanced this line of research by training a 3D U-Net with both interpolated control points and finer voxel-level annotations, though, again, access to data and code was restricted. Significant progress in the segmentation of the IAC came with the release of the first public CBCT dataset containing 2D and 3D annotations of the mandibular canal [11, 12]. Contextually, authors introduced PosPadUNet3D, a modified 3D U-Net that segments IAC in stages by expanding sparse 2D labels into 3D annotations, thus generating additional synthetic labels and using such generated data in combination with expert-labeled 3D voxel data to train the final U-Net based model.
+
+Usman et al. [48] proposed a two-stage U-Net approach to address class imbalance between the mandibular canal and background, using CNNs to isolate regions of interest (ROIs) before segmentation, while Zhao et al. [55] introduced a Frenet frame-based method to better capture mandibular topology and maintain canal structure during segmentation. More recently, Lv et al. [37] developed a transformer-based model with adaptive image processing and a "deep label fusion" technique to enhance label consistency across sparse public data, building on Cipriano's label expansion methodology.
+
+Finally, Lumetti et al. [36] tackled issues with patch-based learning by using a memory-augmented transformer encoder to improve spatial coherence in the U-Net bottleneck, enriching patch context and improving segmentation.
+
+IAC segmentation remains a challenging area with room for further advancements, largely dependent on access to comprehensive, high-quality 3D datasets.
+
+# 3. The ToothFairy2 Dataset
+
+The proposed dataset, ToothFairy2, represents the most extensive publicly available and fully annotated collection of dental CBCT scans. It comprises 530 3D volumes, of which 480 are entirely accessible for training purposes<sup>1</sup> while the remaining 50 are reserved exclusively for evaluation. Although these 50 scans are not directly accessible for model training, researchers can evaluate their models on this test set via the grand-challenge platform,<sup>2</sup> which automatically runs the submitted models and provides performance results. In this way, we ensure that researchers will not incur the risk of mixing training and testing data and that the reported results will not suffer from the random choice of the testing data subset selection.
+
+The training data (480 scans) were acquired by the Af
+
+![](images/c62f5df6e6670faef9858424eb30e7517b66151085efbd63dc75c6b21bf4fcaf.jpg)
+
+![](images/892b667ffd7904ce0080cdfa4a51863bdfce1264f010d79122b4802e1bc14dff.jpg)
+
+![](images/3d26511f772bf29bbba51f13a64f9e029685cb6a18769b09fb6e83f24fcb7c6c.jpg)
+
+![](images/6f09b7989ac5bed90862b732c669f72ef195dcd00fe729a4b9e3c127304b153a.jpg)
+
+![](images/27b27a259850fd96d2ed16232ffd86a61a29346343d3cc9e9e285e5972a0a2fe.jpg)
+
+![](images/1dde50e6d9231b74de8c8c4ccf1cac8c07d9cfed147a61ba702fc12d85b11305.jpg)
+
+![](images/d7063cc7e1721da1c808223d527d3c33cfcb2a21d92d629cbe0dcd490b0a2ed0.jpg)
+
+![](images/cc316baf243064c0b7ccad9330240050630a61a240f4e3b4ce4705919cfe9fc4.jpg)
+
+![](images/2b7f174e7dd67da0bb41bb4c9382c3928fa431e66cfc2c94e4d9daad4cab7a1d.jpg)
+
+![](images/96cef07db3696de64009751a4df59b0c8c304a4b2f41c2881e3d87cde3298eb1.jpg)
+
+![](images/410d861eb8a55c327e74b5f2c0c409f9ae75347c9a7a393845edaa4aea11fd13.jpg)
+
+![](images/1135e09da3c6521ac8d8948247a84be4b16160f73ae170521f843980fbb6e87f.jpg)
+
+![](images/5066c58cea10fc2e254ea477cd63300c603f4262d11a499795131e2df9ce855a.jpg)  
+Figure 2. Dataset samples, one patient per line. For each view, i.e., left, frontal, and right, both raw CBCT data and labels are provided.
+
+![](images/a8db2fd95d352d7a92b694a33353522ecee9478e548b15175aff376a5dc0d032.jpg)
+
+![](images/062b1c653c63a93480af8d5d8b6e3bde95df98693100483bf60bf331960a09df.jpg)
+
+![](images/64ea54a318a1650de52c08ab35ad06b351ea57c0d9757367ea59fce3947584dd.jpg)
+
+![](images/330c772e3095e469b1837dd7afe7df754f05e614f1a3dc62313235446d45a030.jpg)
+
+![](images/94c5d9027e226a36bebf5329d1a606328d5393ab226a016da7373a88f39f1661.jpg)
+
+fidea Center, a pan-European healthcare group that specializes in advanced diagnostics, laboratory analyses, rehabilitation, and cancer diagnosis and treatment. The scans were obtained through Cone Beam Computed Tomography (CBCT) via a NewTom/NTVGiMK4, operating at  $3\mathrm{mA}$ , 110 kV, and offering  $0.3\mathrm{mm}$  isotropic voxels (Fig. 2). The test data (50 scans) were provided by the Department of Oral and Maxillofacial Surgery at Radboud University Medical Centre, Nijmegen, obtained using a standard CBCT scanning protocol with the i-CAT 3D Imaging System. The scans were performed with a field of view measuring  $16\mathrm{cm}$  in diameter and  $22\mathrm{cm}$  in height. The scanning process involved two scans of 20 seconds each, resulting in an isotropic voxel size of  $0.4\mathrm{mm}$ . These were later rescaled to match the voxel size of the training data before annotation.
+
+Every patient was anonymized and we only kept a few personal details—namely gender, age, and year of the scan. Specifically,  $58.30\%$  of the patients are female (58.54% in the training set, 56.00% in the test set), all the scans were performed between 2019 and 2024, and volumes belong to patients with ages in range (10-100] with the highest frequencies in ranges (20-30] and (60-70] for the training set and (50-70] for the test set.
+
+# 3.1. Annotation protocol and tools
+
+The inclusion of densely labeled 3D data is necessary in order to achieve the full potential of CNNs [22]. 3D annotations refer to the annotation process carried out by medical professionals directly at the voxel level on CBCT scans. This annotation approach involves detailed markings applied to the individual voxels, providing a more precise depiction of the structures involved.
+
+As often happens in medical image annotation, accurate delineation of individual teeth and maxillofacial structures is a complex and time-consuming process, which requires
+
+expert eyes to be achieved. To reduce the burden of the annotators while ensuring high-quality labels, we employed a semi-automated approach that was fully supervised by clinicians. The annotation process was performed by 7 different maxillofacial experts. To prevent models trained on our dataset from being rewarded for learning annotator-specific biases and to allow for the understanding of their generalizability, 5 of the annotators focused on the training set only, while the remaining 2 handled the test cases only.
+
+Since labeling voxels directly on 3D views is ambiguous and prone to errors, our annotations have been performed on 2D slices. Moreover, to reduce the jagged effect that usually occurs when annotating 3D objects on 2D, the annotation is performed with multiple iterations, starting from the axial (or transversal) view and later refined by analyzing the scan from the sagittal and coronal (or frontal) planes.
+
+For each patient, a single annotation is provided, including the following classes: upper and lower jawbone, left and right inferior alveolar canal, maxillary sinuses, pharynx, and upper and lower teeth, including wisdom teeth, bridges, crowns, and dental implants. The annotation was produced by a single expert only but validated by a second one to reduce possible mistakes.
+
+In our work, we leverage five different base models built on the nnU-Net framework to provide an initial annotation that is later adjusted and refined by expert clinicians. Each of the models has been trained on a specific (group of) classes, including the jawbone (upper and lower), inferior alveolar canal (left and right branches), maxillary sinus (left and right), pharynx, and teeth. Regarding inferior alveolar canal branches and teeth, we leverage two datasets available in the literature, [12] and [16], respectively. Even when annotating the first volumes of our dataset, clinicians were provided with an initial segmentation of teeth and canals and were required to modify, update, or remove wrongly an
+
+![](images/437384607e6b776c109cee80ccd8efce4f2a2067ab8432ab9af2d0fbe477c63a.jpg)  
+(a) Dense Point Set
+
+![](images/374d69b7cae879f6c0f6d72f09c34df766f216429edb12533e798bf661059a6b.jpg)  
+(b) Voxelized Teeth
+
+![](images/d99b8de6de5372c3266eea0789923ad6e96984cdf720dc17f4d01e69e048f336.jpg)  
+Figure 3. Annotations post-processing. The annotations produced as described in Sec. 3 are dense and jagged (a). For this reason, we compute a concave  $\alpha$ -shape, which, after voxelization, produces the volumes in (b). Such an approach is applied to all of the anatomical structures involved in our dataset but the jawbones, whose annotations contain multiple holes.  
+(a) Point Set  
+Figure 4.  $\alpha$ -shape construction process for the point set of (a). In the Delaunay triangulation (b), triangles with circumradius  $\leq -\frac{1}{\alpha}$  form a simplicial subcomplex known as  $\alpha$ -complex (c), whose border is the  $\alpha$ -shape (d).
+
+![](images/48ccff90583382753def5c2a9840ba91413e7ccd3a414a8bedc086ff34140422.jpg)  
+(b) Triangulation
+
+![](images/4ddb4d222bd2c3a6c9be1b2c0e5e213c77fb70df9cac67a8bef255ebd66a48e3.jpg)  
+(c)  $\alpha$ -complex
+
+![](images/54083ad3159b4228138f2d246cc196dc5b99fff28d164a87329fd9b71fa959a9.jpg)  
+(d)  $\alpha$ -shape
+
+notated voxels. Following an iterative approach, as soon as 20 more annotated volumes were available, the base models were retrained from scratch by including the new data. Following such an approach, clinicians are provided with better annotation proposals at each iteration, reducing the annotation effort and time at the increase of annotated volumes. Since jawbone, maxillary sinus, and pharynx classes were not available in any public dataset, these classes have been annotated from scratch in the first 20 volumes without leveraging any automatic proposal.
+
+# 3.2. Annotation refinement
+
+Even though the annotation process is performed on multiple planes, one after the other, artifacts due to 3D annotations performed on 2D views remain a challenge, albeit reduced (Fig. 3a). In order to obtain a smooth polygonal mesh out of expert-produced annotations, we compute a concave  $\alpha$ -shape. The  $\alpha$ -shape [19] is a generalization of the convex hull aimed at representing the intuitive concept of the shape of a point set. The only parameter of the algorithms is  $\alpha \in \mathbb{R}$ , which regulates the "crudeness" of the result. Let's define a generalized disk of radius  $\frac{1}{\alpha}$  as  $D_{\alpha}$ :
+
+$$
+D _ {\alpha} \left\{ \begin{array}{l l} \text {T h e c o m p l e m e n t o f a d i s c o f r a d i u s} - \frac {1}{\alpha}, & \text {i f} \alpha <   0 \\ \text {A h a l f p l a n e ,} & \text {i f} \alpha = 0 \\ \text {A d i s c o f r a d i u s} \frac {1}{\alpha}, & \text {i f} \alpha > 0 \end{array} \right. \tag {1}
+$$
+
+Given a point set  $S$  and a value for  $\alpha$ , the  $\alpha$ -shape is con-
+
+structed in this way: an edge is put between two points  $p_i$  and  $p_j$  whenever there exists a  $D_{\alpha}$  with  $p_i$  and  $p_j$  lying on its boundary, and which contains the entire  $S$ . When  $\alpha = 0$ , this procedure constructs the convex hull; instead, cruder or finer shapes can be respectively obtained using positive or negative  $\alpha$  values. Because of the geometrical nature of the alveolar nerve, we are specifically interested in concave  $\alpha$ -shapes, achievable when  $\alpha < 0$ .
+
+The most common method for computing a concave  $\alpha$ -shape consists of taking the border of a simplicial subcomplex extracted from the Delaunay triangulation, containing only triangles with circumradius  $\leq -\frac{1}{\alpha}$ . An example of the process is depicted in Fig. 4.
+
+The above concepts can be extended to the three-dimensional case by substituting disks and triangles with spheres and tetrahedra, respectively. After creating an  $\alpha$ -shape polygonal mesh starting from dense annotations, it is converted into a binary raster volume by means of voxelization: the final result is given in Fig. 3b.
+
+# 3.3. Accessibility
+
+The resulting annotated volumes have been packed following the nnU-Net dataset format, which comprises three different components: raw images, corresponding segmentation maps, and a dataset. .json file specifying the metadata. The class IDs are an extension of the FDI notation and include a total of 42 classes. Final 3D volumes and labels are provided in the .mha format.
+
+Ethics approval. The training data received ethical committee approval from Comitato Etico dell'Area Vasta Emilia Nord (Approval Number 1374/2020/OSS/ESTMO SIRER ID 1275 - NAICBCT-D) and can be downloaded under the CC BY-SA license after user registration.
+
+# 4. Experiments
+
+In order to validate the effectiveness of the proposed dataset, experiments have been performed considering two different datasets: the one proposed with this paper and the publicly released subset of the dataset proposed by Cui et al. [15], which is the only dataset publicly available in the literature for multi-class tooth instance segmentation. Many of the dental-related scientific papers promising the release of the dataset upon request [2, 9] have ignored our emails.
+
+# 4.1. Evaluation metrics
+
+In our experiments, two widely accepted metrics for the segmentation task [39] have been employed, namely Dice Similarity Coefficient (DSC in  $\%$  ) and the 95th percentile Hausdorff Distance (HD95 in mm).
+
+The DSC has the same meaning as the IoU (Intersection over Union), but DSC is better suited when the region of interest is significantly smaller than the background. In such
+
+Table 2. Results on the test set of the proposed dataset. Classes are grouped by the main anatomical structure to which they belong. Default nnU-Net plan is employed. The best results are in bold, while the second best are underlined.  
+
+<table><tr><td rowspan="2" colspan="2">Model</td><td colspan="2">Average</td><td colspan="2">L/R IAC</td><td colspan="2">L/R Sinus</td><td colspan="2">Teeth</td><td colspan="2">Jawbones</td><td colspan="2">Pharynx</td><td colspan="2">Others</td></tr><tr><td>DSC</td><td>HD95</td><td>DSC</td><td>HD95</td><td>DSC</td><td>HD95</td><td>DSC</td><td>HD95</td><td>DSC</td><td>HD95</td><td>DSC</td><td>HD95</td><td>DSC</td><td>HD95</td></tr><tr><td rowspan="2">CNNs</td><td>nnU-Net [24]</td><td>70.92</td><td>17.86</td><td>71.34</td><td>29.11</td><td>64.81</td><td>28.39</td><td>73.17</td><td>18.32</td><td>90.31</td><td>12.53</td><td>95.66</td><td>19.23</td><td>29.50</td><td>20.95</td></tr><tr><td>nnU-Net ResEnc [24]</td><td>74.16</td><td>14.48</td><td>73.01</td><td>27.81</td><td>65.71</td><td>29.99</td><td>76.48</td><td>14.26</td><td>91.77</td><td>12.53</td><td>95.26</td><td>17.75</td><td>37.10</td><td>16.32</td></tr><tr><td rowspan="3">Transf.</td><td>TransU-Net [7]</td><td>70.32</td><td>20.17</td><td>81.96</td><td>11.99</td><td>59.69</td><td>59.76</td><td>72.46</td><td>15.04</td><td>90.33</td><td>49.02</td><td>87.89</td><td>42.87</td><td>27.68</td><td>22.65</td></tr><tr><td>nnFormer [56]</td><td>76.79</td><td>5.45</td><td>72.28</td><td>10.06</td><td>75.11</td><td>8.22</td><td>79.37</td><td>2.94</td><td>91.50</td><td>20.09</td><td>90.85</td><td>24.53</td><td>18.95</td><td>13.67</td></tr><tr><td>UNETR++ [47]</td><td>71.43</td><td>17.23</td><td>68.87</td><td>15.10</td><td>74.51</td><td>15.48</td><td>73.70</td><td>17.91</td><td>92.38</td><td>5.44</td><td>91.60</td><td>18.96</td><td>26.21</td><td>19.70</td></tr><tr><td rowspan="3">Mamba</td><td>UMamba [38]</td><td>85.05</td><td>5.28</td><td>85.26</td><td>16.23</td><td>77.02</td><td>4.35</td><td>86.58</td><td>2.23</td><td>90.05</td><td>22.17</td><td>92.18</td><td>25.25</td><td>43.89</td><td>13.97</td></tr><tr><td>VMamba [35]</td><td>73.13</td><td>5.17</td><td>60.62</td><td>9.94</td><td>73.20</td><td>9.89</td><td>75.99</td><td>3.39</td><td>90.75</td><td>7.17</td><td>88.23</td><td>23.95</td><td>14.86</td><td>14.63</td></tr><tr><td>Swin-UMamba [34]</td><td>79.64</td><td>2.94</td><td>72.64</td><td>2.02</td><td>87.75</td><td>2.38</td><td>80.71</td><td>2.59</td><td>94.29</td><td>4.25</td><td>93.05</td><td>2.79</td><td>25.30</td><td>14.93</td></tr></table>
+
+a context, more weight is given to the correctly identified region, making the DSC more robust and informative than IoU. The relationship between DSC and IoU is expressed by the following formula:
+
+$$
+D S C (P, G T) = \frac {2 \times | P \cap G T |}{| P | + | G T |} = \frac {2 \times I o U}{1 + I o U} \tag {2}
+$$
+
+where  $P$  is the model prediction and  $GT$  is the ground truth.
+
+On the other hand, the HD95 computes the maximum distance between two sets of points, considering the 95th percentile of these distances. In general, the 95th percentile of the distances between boundary points in A and B is defined as follows:
+
+$$
+\mathrm {d} _ {9 5} (A, B) = \mathrm {x} _ {a \in A} ^ {9 5} \left\{\min  _ {b \in B} d (a, b) \right\} \tag {3}
+$$
+
+where  $x_{a \in A}^{95}$  denotes the 95th percentile of the elements in the set enclosed within the brackets. Given the set formed by the pixels in the predicted mask  $(P)$  and the set of pixels belonging to the ground truth  $(GT)$ , the Hausdorff distance is determined as the maximum value of the two distances between  $P$  and  $GT$  and  $GT$  and  $P$  at the 95th percentile:
+
+$$
+\mathrm {H D 9 5} (\mathrm {P}, \mathrm {G T}) = \max  \left\{d _ {9 5} (P, G T), d _ {9 5} (G T, P) \right\} \tag {4}
+$$
+
+By using the 95th percentile, this metric provides a robust evaluation that is less sensitive to outliers or extreme differences between the sets of points.
+
+# 4.2. Compared algorithms
+
+Experimental analysis has been conducted on recently proposed general-purpose state-of-the-art algorithms for segmenting medical 3D volumes, considering all of the top-notch architecture (i.e., CNNs, Transformers, and Mamba-based hybrid solutions). For what concerns CNNs, we include the original nnU-Net [24] configuration making use of the U-Net architecture (nnU-Net), and its variations leveraging residual connections in the encoder (nnU-Net ResEnc). The considered Tranformer-based architectures are TransU-Net [7], UNETR++ [47], and the recently published nnFormer [56]. Finally, we include UMamba [38], VMamba [35], and Swin-UMamba [34] as the representatives of solutions based on state-space models.
+
+# 4.3. Experimental setting
+
+In all of the experiments carried out, we adopted a standardized scheme for hyperparameters configuration. More specifically, we leverage the planning provided by the nnU-Net framework. For intrinsically 3D algorithms, i.e., nnU-Net, TransU-Net, nnFormer, UNETR++, and UMamba, the nnU-Net planning applied to the training set of the proposed dataset suggests a patch size of  $80 \times 160 \times 160$ , and a batch size of 2. The only exception is the nnU-Net ResEnc, which adopts a patch size of  $122 \times 224 \times 256$ . On the other hand, models that produce 3D segmentations working slice-by-slice, i.e., VMamba and Swin-UMamba, have a suggested patch size of  $384 \times 384$  and a batch size of 19.
+
+All the images in the training set of the proposed dataset have the same spacing (0.3 mm isotropic), so no resampling is required. The volumes from the secondary dataset employed in our experiments, Cui et al. [16], have a mixed voxel spacing instead, so they have been resampled to match  $0.3\mathrm{mm} \times 0.3\mathrm{mm} \times 0.3\mathrm{mm}$ .
+
+Models are trained from scratch without any pre-training data. The nnU-Net three-fold cross-validation schema has always been employed for model selection. All the models have been trained for a total of 1000 epochs on a 48GB A40 Nvidia GPU using CUDA 11.8 and PyTorch 2.1.2.
+
+# 4.4. Results
+
+To fully explore the capabilities of state-of-the-art models on the proposed comprehensive dataset, Tab. 2 is provided. For space constraints, results are grouped by anatomical structures by averaging single-class results. The "others" column includes implants, artificial crowns, and bridges. Surprisingly, 2D models, i.e., VMamba and Swin-UMamba, outperform intrinsically 3D ones in many scenarios. Although performance is satisfying, 2D models lack the ability to fully capture and learn the spatial relationships between the 3D structures in the maxillofacial region due to a loss of contextual information and a limited capacity to learn effective representations of complex structures. This is also confirmed by the qualitative results discussed in Sec. 4.7, where it is evident that predictions are more noisy and seem fragmented. Anyway, their local ability to dis
+
+Table 3. Results on the test set of the proposed dataset when using variations of the nnU-Net ResEnc.  
+
+<table><tr><td>Model</td><td>DSC</td><td>HD95</td></tr><tr><td>Default</td><td>74.16</td><td>14.48</td></tr><tr><td>w/o l/r mirroring</td><td>80.79</td><td>12.37</td></tr><tr><td>w/o l/r mirroring, increased depth</td><td>82.11</td><td>11.86</td></tr><tr><td>w/o l/r mirroring, increased depth, post-processing</td><td>84.99</td><td>8.57</td></tr></table>
+
+criminate among classes ensures high-level performance.
+
+Overall, Mamba-based architectures are the most effective in this context. Among all, UMamba provides the best performance. Blending convolutions to model precise spatial information and state-space models to learn long-range voxel-level interactions ensures high accuracy. Mamba provides a global context alongside voxel-wise precision, the former missing in traditional convolutional layers due to limited receptive fields and the latter absent in Transformers due to computational complexity. Moreover, it requires less training data to converge, making it more suitable in the medical field where access to data is limited.
+
+Considering the different classes available in the dataset, jawbones and pharynx achieved the highest DSC scores, independently from the considered model. This could be explained by the consistent anatomical shape those structures display throughout the entire dataset. Performance drops when it comes to segmenting classes subject to more variability, like teeth; their position and orientation change considerably from patient to patient, there might be missing teeth, or they can be covered by artificial crowns, or completely replaced by implants or bridges. It is not uncommon for the models to confuse artificial with natural teeth. Independently from the model employed, the worst performances are obtained when segmenting "others" classes, which represent all of the artificial structures that restore or replace natural teeth. One reasonable explanation is that these classes are underrepresented in the proposed dataset.
+
+# 4.5. Improving default nnU-Net configuration
+
+To augment the training dataset and improve performance, the nnU-Net framework applies mirroring along multiple spatial dimensions. Such an augmentation usually improves model performance, providing additional useful training data without compromising the ability to differentiate between left and right organs. In the maxillofacial context, however, there is pronounced symmetry between structures: teeth, nerves, and maxillary sinuses are all symmetrical w.r.t. to the sagittal plane splitting the face. In such a scenario, ensuring precise orientation is fundamental. The application of the left/right mirroring augmentation has exactly the opposite effect: instead of increasing model performance and generalization capabilities, it leads to a drop in the model's ability to establish a reliable left/right distinction, downgrading overall performance (Tab. 3). Additionally, given the complexity of the proposed dataset, we
+
+Table 4. Results on the test set of the proposed dataset when using our plan w/o post-processing.  
+
+<table><tr><td rowspan="2"></td><td rowspan="2">Model</td><td colspan="2">Default Plan</td><td colspan="2">Our Plan</td></tr><tr><td>DSC</td><td>HD95</td><td>DSC</td><td>HD95</td></tr><tr><td rowspan="2">CNNs</td><td>nnU-Net [24]</td><td>70.92</td><td>17.86</td><td>78.24</td><td>13.36</td></tr><tr><td>nnU-Net ResEnc [24]</td><td>74.16</td><td>14.48</td><td>82.11</td><td>11.86</td></tr><tr><td rowspan="3">Transf.</td><td>TransUNet [7]</td><td>70.32</td><td>20.17</td><td>75.12</td><td>7.17</td></tr><tr><td>nnFormer [56]</td><td>76.79</td><td>5.45</td><td>79.12</td><td>5.15</td></tr><tr><td>UNETR++ [47]</td><td>71.43</td><td>17.23</td><td>83.57</td><td>3.04</td></tr><tr><td rowspan="3">Mamba</td><td>UMamba [38]</td><td>85.05</td><td>5.28</td><td>85.39</td><td>4.70</td></tr><tr><td>VMamba [35]</td><td>73.13</td><td>5.17</td><td>74.21</td><td>4.37</td></tr><tr><td>Swin-UMamba [34]</td><td>79.64</td><td>2.94</td><td>81.95</td><td>2.91</td></tr></table>
+
+enriched the network topology by introducing an additional layer. Such an enhancement ensures an increased receptive field in the bottleneck of the network, improving contextual information and the model's ability to identify long-range relationships between structures. Finally, considering the nature of the structures involved in the dataset, all of the voxels belonging to the same class should be in a close relationship and connected to each other. For this reason, we introduce a post-processing technique that computes connected components [5] and filters out predictions that are below a given threshold or the smallest prediction if multiple objects for the same class are identified by the model. Thresholds are computed per class using statistics on the training dataset, i.e., minimum connected component volume. Such a procedure ensures us an additional gain of more than 2 DSC points on the proposed dataset.
+
+Finally, in order to confirm the effectiveness of the improved nnU-Net planning on the proposed dataset, i.e., disabling  $1 / \mathbf{r}$  mirroring and the deeper network topology, we apply the same strategy to all of the considered models. Results are reported in Tab. 4.
+
+# 4.6. Comparison with existing datasets
+
+Tab. 5 provides a comparison of state-of-the-art models, one from each category, trained on either the proposed data or a subset of it and Cui et al. [16]. To perform a fair comparison, we selected 82 random scans from both datasets to be used for training. Since [16] includes only tooth annotations, we set all non-teeth classes to background. Selected models are evaluated on our complete test set, the full Cui dataset, and its 15 volumes not used for training.
+
+A column-wise comparison of the results suggests that the volume of data has only a marginal impact on overall tooth segmentation performance but is critical for demographic generalizability. Additionally, models trained on our dataset generalize more effectively to the Cui dataset, while those trained exclusively on Cui experience a dice score reduction of up to 10 points when evaluated on our dataset compared to their own test set. Notably, UMamba demonstrates consistently strong performance across both datasets, highlighting its robust generalization capabilities.
+
+![](images/045e0734483b549b5f7392b2b4aaa5c21dd439bfd9c032bf7ddf3f65731ffb99.jpg)  
+(a) Ground-Truth
+
+![](images/3862bf74f08fc7e82fc722f2d86e53ef8a93b80759e8118ef6012bb7eb6b34f6.jpg)  
+(b) nnU-Net ResEnc
+
+![](images/395b9e71c268f0bb7ec736d799e3cd7b96a7cb3ec5a2eeeda961622e81a3f878.jpg)  
+(c) nnFormer  
+Figure 5. Qualitative results obtained with the proposed plan w/o post-processing, one patient per line. Red squares identify relevant errors.
+
+![](images/d03e3f9ff58e7f80876a654df3f95883029dca087e7d190d1789eccb73d1494a.jpg)  
+(d) UNETR++
+
+![](images/f97b8b7e869583981ee87cb5264e8f0c9ae0264e2dd5dcef93dcc6d0c90fc4ae.jpg)  
+(e) UMamba
+
+![](images/5c3c739ce76bf7010a486f51f98885faa4330199cfe6c3fd3130e23080e2583a.jpg)  
+(f) VMamba
+
+![](images/3453eaa049d28da8dd11b1be0789002126466613f2d0eca2dca42c0fb5763bd5.jpg)  
+(g) Swin-UMamba
+
+Table 5. Dice comparison between our and Cui [16] datasets considering only teeth classes and converting others to background. Our plan w/o post-processing is employed.  
+
+<table><tr><td rowspan="2">Training Dataset</td><td rowspan="2">Model</td><td colspan="3">Testing Dataset</td></tr><tr><td>Our (test)</td><td>Cui (all)</td><td>Cui (15 scans)</td></tr><tr><td rowspan="3">Our (all the 480 training scans)</td><td>nnU-Net [24]</td><td>91.12</td><td>81.86</td><td>82.13</td></tr><tr><td>nnFormer [56]</td><td>85.16</td><td>79.19</td><td>80.25</td></tr><tr><td>UMamba [38]</td><td>90.97</td><td>87.60</td><td>87.90</td></tr><tr><td rowspan="3">Our (82 scans from training)</td><td>nnU-Net [24]</td><td>90.04</td><td>79.67</td><td>81.12</td></tr><tr><td>nnFormer [56]</td><td>82.72</td><td>74.85</td><td>77.42</td></tr><tr><td>UMamba [38]</td><td>90.18</td><td>86.23</td><td>87.71</td></tr><tr><td rowspan="3">Cui (82 scans over 97 available)</td><td>nnU-Net [24]</td><td>78.05</td><td></td><td>88.52</td></tr><tr><td>nnFormer [56]</td><td>70.63</td><td>~</td><td>74.35</td></tr><tr><td>UMamba [38]</td><td>79.95</td><td></td><td>89.74</td></tr></table>
+
+# 4.7. Qualitative evaluation
+
+Fig. 5 depicts a qualitative comparison of the selected state-of-the-art segmentation models trained on the proposed dataset when using the previously introduced personalized nnU-Net planning. The lack of contextualized 3D receptive fields of the 2D VMamba is evident, especially on elongated objects like the inferior alveolar canal (first line). Transformer-based architectures, UNETR++ and nnFormer, produce more noisy labels. While some can be easily removed by employing post-processing techniques, the majority will remain. Interestingly, all of the 3D models mistakenly label the plastic chin support used to acquire the CBCT with a mix of artificial crown (yellow label, second line) and pharynx (gray label, second line). This has a negative impact on the HD95 of both "other" and pharynx classes. The local discriminative capabilities of 2D models make them stronger, avoiding this misidentification and explaining the lower HD95 values.
+
+All of the models except UNETR++ correctly recognize the first upper premolar implant (red label within the upper jawbone, second line). However, none of them label the artificial crown of the same tooth as such.
+
+# 5. Conclusions and Limitations
+
+In this paper, we presented the largest publicly available annotated CBCT dataset with 3D segmentations for 42 dis
+
+tinct maxillofacial anatomical structures, offering a valuable resource for developing and benchmarking automated segmentation models in digital dentistry and addressing a significant gap in the literature. Our dataset supports detailed multi-class segmentation across complex maxillofacial anatomy, enabling improved model training, evaluation, and generalization for clinical applications such as surgical planning and implant placement. Extensive benchmark evaluations of state-of-the-art neural network architectures on our dataset highlight the potential of existing models in enhancing the accuracy and efficiency of maxillofacial image analysis, also demonstrating the significance of the proposed dataset. Moreover, we demonstrated different techniques that can be adopted w.r.t. the standard nnU-Net planning to improve the overall performance of state-of-the-art models when trained on complex CBCT datasets such as the one proposed in this paper.
+
+Limitations and future work. Despite its significant contributions, our dataset has limitations that should be further addressed in the future. Although training and testing data come from different machines and acquisition centers, limiting potential biases in the evaluation, training data was acquired from a single center, potentially affecting the generalizability of models trained on this dataset to other clinical environments or patient demographics. Furthermore, while the dataset includes a wide range of classes, some—implants, crowns, and bridges—are underrepresented, affecting the robustness of model performance. Additionally, some components encountered in maxillofacial imaging, such as dental braces and bone plates, are missing due to their unavailability in the current patient cohort. Future work should focus on expanding this dataset to include multi-center data, ensuring a more representative sample of anatomical and demographic diversity. Including more cases with underrepresented and missing classes would further enhance the dataset's utility, supporting more comprehensive models. We believe that this dataset provides a strong foundation for advancing automated maxillofacial segmentation, and we encourage the research community to build upon it to address the outlined limitations.
+
+# Acknowledgments
+
+This work was supported by the University of Modena and Reggio Emilia and Fondazione di Modena, through the FAR 2024 and FARD-2024 funds (Fondo di Ateneo per la Ricerca 2024) and by the Italian Ministry of Research under the complementary actions to the NRRP "Fit4MedRob - Fit for Medical Robotics" Grant (#PNC0000007).
+
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