iLearn-Lab/TPAMI26-DAAN

DAAN: Mitigating Information Asymmetries for Point-Supervised Video Moment Retrieval

Hao Liu¹  Yupeng Hu^1✉  Kun Wang¹  Junchao Wang¹  Zhiran Li¹  Liqiang Nie²

¹School of Software, Shandong University, Jinan, China
²School 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
🔗 GitHub Repository: iLearn-Lab/TPAMI26-DAAN

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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)

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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.

Step 1: Prepare the Environment

Clone the GitHub repository and install dependencies:

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:

hf download iLearn-Lab/TPAMI26-DAAN --local-dir checkpoints/TPAMI26-DAAN

The downloaded files are organized as follows:

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'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:

python -m src.experiment.eval --exp checkpoints/TPAMI26-DAAN/cha-c3d

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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.

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Citation

If you find our work useful in your research, please consider citing our paper:

@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}
}

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Contact

If you have any questions, feel free to contact me at liuh90210@gmail.com.