Upload README.md

README.md

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 license: apache-2.0
+library_name: pytorch
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+
+# DAAN: Mitigating Information Asymmetries for Point-Supervised Video Moment Retrieval (TPAMI 2026)
+
+**Hao Liu**<sup>1</sup>, **Yupeng Hu**<sup>1</sup>, **Kun Wang**<sup>1</sup>, **Junchao Wang**<sup>1</sup>, **Zhiran Li**<sup>1</sup>, **Liqiang Nie**<sup>2</sup>
+
+<sup>1</sup> School of Software, Shandong University, Jinan, China  
+<sup>2</sup> School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
+
+This repository hosts the official pretrained checkpoints for **DAAN**, a point-supervised Video Moment Retrieval (VMR) framework that mitigates Semantic-Positional Asymmetry and Instance-Duration Asymmetry through **Center-Guided Calibration (CGC)** and a **Duration-Aware Proposal Discriminator (DPD)**.
+
+🔗 **Paper:** [IEEE TPAMI 2026](https://doi.org/10.1109/TPAMI.2026.3742682)  
+🔗 **GitHub Repository:** [iLearn-Lab/TPAMI26-DAAN](https://github.com/iLearn-Lab/TPAMI26-DAAN)
+
+---
+
+## Model Information
+
+### 1. Model Name
+
+**DAAN** (Dual-Asymmetry Aligned Network).
+
+### 2. Task Type & Applicable Tasks
+
+- **Task Type:** Point-Supervised Video Moment Retrieval (VMR) / Temporal Localization / Vision-Language Learning.
+- **Applicable Tasks:** Locating the temporal interval in an untrimmed video that matches a natural language query, using only a single annotated frame per training instance instead of full temporal boundaries.
+
+### 3. Project Introduction
+
+Point-supervised VMR substantially reduces annotation effort but introduces two inherent information asymmetries:
+
+- **Semantic-Positional Asymmetry:** An annotated frame confirms the event semantics but does not reveal its relative position within the target moment.
+- **Instance-Duration Asymmetry:** An annotated frame confirms the event's occurrence but provides no explicit information about its temporal duration.
+
+DAAN addresses these challenges with two complementary components. **CGC** dynamically estimates a pseudo temporal center and recalibrates attention through local and global cross-modal cues, while **DPD** learns query-conditioned compatibility between proposal content and boundary representations to distinguish plausible temporal spans without boundary supervision.
+
+### 4. Training Data Source
+
+DAAN is trained and evaluated on standard point-supervised VMR benchmarks:
+
+- **Charades-STA** (I3D, C3D, and VGG visual features)
+- **TACoS** (C3D visual features)
+- **ActivityNet Captions** (C3D visual features)
+
+---
+
+## Usage & Basic Inference
+
+This model repository provides pretrained weights and their corresponding configuration files. For the evaluation code and dataset preparation instructions, please refer to the [official GitHub repository](https://github.com/iLearn-Lab/TPAMI26-DAAN).
+
+### Step 1: Prepare the Environment
+
+Clone the GitHub repository and install dependencies:
+
+```bash
+git clone https://github.com/iLearn-Lab/TPAMI26-DAAN.git
+cd TPAMI26-DAAN
+
+python3.10 -m venv .venv
+source .venv/bin/activate  # Linux / Mac
+
+pip install torch==2.3.0 torchtext==0.18.0 "numpy<2" scipy pyyaml tqdm matplotlib huggingface_hub
+```
+
+Choose the PyTorch build appropriate for your CUDA environment.
+
+### Step 2: Download Model Weights & Data
+
+Download the pretrained checkpoints from this Hugging Face repository:
+
+```bash
+hf download iLearn-Lab/TPAMI26-DAAN --local-dir checkpoints/TPAMI26-DAAN
+```
+
+The downloaded files are organized as follows:
+
+```text
+checkpoints/TPAMI26-DAAN/
+├── cha-i3d/
+│   ├── config.yaml
+│   └── model_best.pt
+├── cha-c3d/
+│   ├── config.yaml
+│   └── model_best.pt
+├── cha-vgg/
+│   ├── config.yaml
+│   └── model_best.pt
+└── tacos/
+    ├── config.yaml
+    └── model_best.pt
+```
+
+
+Prepare video features and point annotations following [ViGA](https://github.com/r-cui/ViGA)'s dataset preparation protocol. **The benchmark datasets and extracted video features are not included in this model repository.**
+
+Before evaluation, update the local paths in:
+
+- `checkpoints/TPAMI26-DAAN/<checkpoint>/config.yaml`: `feature_dir` (and ensure `feature_dim` matches the extracted features).
+- `src/utils/utils.py`: annotation directories for the corresponding datasets.
+
+
+### Step 3: Run Inference
+
+Evaluate a downloaded checkpoint from the root of the GitHub repository:
+
+```bash
+python -m src.experiment.eval --exp checkpoints/TPAMI26-DAAN/cha-c3d
+```
+
+
+---
+
+## Limitations & Notes
+
+**Disclaimer:** These checkpoints are provided for academic research purposes.
+
+- The model requires access to the original benchmark datasets and extracted video features for evaluation.
+- Some configuration files currently contain local path settings and should be updated before use.
+
+---
+
+## Citation
+
+If you find our work useful in your research, please consider citing our paper:
+
+```bibtex
+@article{liu2026daan,
+  title={DAAN: Mitigating Information Asymmetries for Point-Supervised Video Moment Retrieval},
+  author={Liu, Hao and Hu, Yupeng and Wang, Kun and Wang, Junchao and Li, Zhiran and Nie, Liqiang},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  year={2026},
+  doi={10.1109/TPAMI.2026.3742682}
+}
+```
+
+---
+
+## Contact
+
+If you have any questions, feel free to contact me at **liuh90210@gmail.com**.