Update README.md

README.md

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 ---
 license: apache-2.0
+pipeline_tag: image-segmentation
+tags:
+  - infrared-small-target-detection
+  - multimodal
+  - vision-language
+  - pytorch
+
 ---
+
+
+<h1>ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection</h1>
+
+<p>
+  <b>Tongtong Wang</b><sup>1</sup>&nbsp;
+  <b>Mingzhu Xu</b><sup>1✉</sup>&nbsp;
+  <b>Chenglong Yu</b><sup>1</sup>&nbsp;
+  <b>Jing Wang</b><sup>1</sup>&nbsp;
+  <b>Xiaohui Lin</b><sup>1</sup>&nbsp;
+  <b>Weili Guan</b><sup>2</sup>
+</p>
+
+
+<p>
+  <sup>1</sup>School of Software, Shandong University<br>
+  <sup>2</sup>Harbin Institute of Technology, Shenzhen<br>
+  <sup>✉</sup>Corresponding author
+</p>
+
+
+<p>
+  <a href="<paper-link>">
+    <img src="https://img.shields.io/badge/ACM%20MM-2026-blue" alt="ACM MM 2026">
+  </a>
+  <a href="https://github.com/iLearn-Lab/MM26-ADGNet">
+    <img src="https://img.shields.io/badge/GitHub-MM26--ADGNet-black?logo=github" alt="GitHub">
+  </a>
+</p>
+
+---
+
+## 📖 Model Description
+
+This repository provides the official model checkpoints for **ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection**, accepted by **ACM Multimedia 2026**.
+
+Infrared Small Target Detection aims to accurately segment weak and tiny targets from complex infrared backgrounds. Existing pure-vision methods rely mainly on pixel-level information, while existing vision-language methods commonly describe targets and backgrounds using a single textual prompt. Such a symmetric design overlooks the inherent semantic differences between sparse infrared targets and structurally complex backgrounds.
+
+ADGNet addresses this problem through three main components:
+
+- **Asymmetric Dual-text Prompt (ADP):** uses an abstract, image-independent target prompt and a detailed, image-dependent background prompt.
+- **Asymmetric Dual-Branch Interaction (ADBI):** independently performs target localization and background suppression using their corresponding textual priors.
+- **Adaptive Feature Aggregation (AFA):** dynamically fuses target-enhanced and background-suppressed features for accurate segmentation.
+
+The model uses the pretrained **CLIP ViT-B/16** text encoder to extract semantic representations from the target and background prompts.
+
+---
+
+## 🏆 Available Checkpoints
+
+All ADGNet checkpoints are hosted in this Hugging Face model repository.
+
+Download the required checkpoint directly from the **Files and versions** section of this repository.
+
+|  Dataset   |                          Checkpoint                          |
+| :--------: | :----------------------------------------------------------: |
+|  IRSTD-1K  | [`ADGNet_mIoU_72.38_IRSTD-1K.pth.tar`](<irstd-1k-checkpoint-link>) |
+| NUDT-SIRST | [`ADGNet_mIoU_95.53_NUDT-SIRST.pth.tar`](<nudt-sirst-checkpoint-link>) |
+|   SIRST    | [`ADGNet_mIoU_83.08_SIRST.pth.tar`](<sirst-checkpoint-link>) |
+
+---
+
+## 🚀 Usage
+
+These checkpoints are designed to be used with the official ADGNet implementation:
+
+```text
+https://github.com/iLearn-Lab/MM26-ADGNet
+```
+
+### 1. Clone the Official Repository
+
+```bash
+git clone https://github.com/iLearn-Lab/MM26-ADGNet.git
+cd MM26-ADGNet
+```
+
+### 2. Prepare the Checkpoints
+
+Place the downloaded checkpoints in:
+
+```text
+MM26-ADGNet/
+└── SOTA_pth/
+    ├── ADGNet_mIoU_72.38_IRSTD-1K.pth.tar
+    ├── ADGNet_mIoU_95.53_NUDT-SIRST.pth.tar
+    └── ADGNet_mIoU_83.08_SIRST.pth.tar
+```
+
+### 3. Prepare the CLIP Text Encoder
+
+ADGNet uses the pretrained **CLIP ViT-B/16** model:
+
+```bash
+git clone https://huggingface.co/openai/clip-vit-base-patch16
+```
+
+Update the local CLIP model path in the corresponding project configuration or source file before inference.
+
+### 4. Run Evaluation
+
+Example evaluation on IRSTD-1K:
+
+```bash
+python train.py \
+    --trainset "IRSTD-1K" \
+    --testset "IRSTD-1K" \
+    --dataset_dir "./datasets" \
+    --mode test \
+    --ckpt "./SOTA_pth/ADGNet_mIoU_72.38_IRSTD-1K.pth.tar"
+```
+
+Replace the dataset name and checkpoint path when evaluating on NUDT-SIRST or SIRST.
+
+---
+
+## 📂 Dataset and Text Annotation Preparation
+
+The original infrared images and ground-truth masks are not included in this model repository. Please obtain **IRSTD-1K**, **NUDT-SIRST**, and **SIRST** from their respective official sources.
+
+The asymmetric text annotations used by ADGNet are released separately in our Hugging Face dataset repository:
+
+- **AITIR Text Annotations:** [`Download`](<huggingface-text-dataset-link>)
+
+After downloading the original datasets and text annotations, organize them according to the official ADGNet repository:
+
+```text
+datasets/
+├── IRSTD-1K/
+│   ├── images/
+│   ├── masks/
+│   ├── img_idx/
+│   └── text/
+├── NUDT-SIRST/
+│   ├── images/
+│   ├── masks/
+│   ├── img_idx/
+│   └── text/
+└── SIRST/
+    ├── images/
+    ├── masks/
+    ├── img_idx/
+    └── text/
+```
+
+---
+
+## 🎯 Intended Use
+
+The released checkpoints are intended for:
+
+- Academic research on infrared small target detection
+- Reproduction of the results reported in the ADGNet paper
+- Evaluation on IRSTD-1K, NUDT-SIRST, and SIRST
+- Research on multimodal and text-guided infrared image segmentation
+- Comparison with other infrared small target detection methods
+
+---
+
+## ⚠️ Limitations
+
+- The model requires both infrared images and corresponding textual prompts.
+- Detection performance may vary when applied to datasets or scenes that differ substantially from the training distribution.
+- The released checkpoints are designed for the dataset splits and evaluation settings used in the paper.
+- The model depends on the pretrained CLIP ViT-B/16 text encoder.
+- The original infrared datasets are subject to their respective licenses and terms of use.
+
+---
+
+## 🔗 Related Resources
+
+- **GitHub Repository:** [iLearn-Lab/MM26-ADGNet](https://github.com/iLearn-Lab/MM26-ADGNet)
+- **Paper:** [`ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection`](<paper-link>)
+- **Text Annotations:** [`AITIR Text Annotations`](<huggingface-text-dataset-link>)
+
+---
+
+## 📚 Citation
+
+If you find ADGNet or the released checkpoints useful in your research, please consider citing our paper:
+
+```bibtex
+
+```
+
+Please also consider checking out and citing our other related work:
+
+```bibtex
+
+```
+
+---
+
+## 📄 License
+
+This model repository is released under the [Apache License 2.0](./LICENSE).
+
+The original datasets and the pretrained CLIP model remain subject to their respective licenses and terms of use.
+