README.md

metadata

license: apache-2.0
task_categories:
  - video-retrieval
  - image-retrieval
tags:
  - composed-video-retrieval
  - composed-image-retrieval
  - vision-language
  - pytorch
  - aaai-2026

🎬 (AAAI 2026) ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval (Model Weights)

Zixu Li¹, Yupeng Hu^1✉, Zhiwei Chen¹, Qinlei Huang¹, Guozhi Qiu¹, Zhiheng Fu¹, Meng Liu²

¹School of Software, Shandong University    
²School of Computer Science and Technology, Shandong Jianzhu University   
^✉ Corresponding author  

This repository hosts the official pre-trained model weights for ReTrack, an evidence-driven framework designed to calibrate directional bias in composed features for both Composed Video Retrieval (CVR) and Composed Image Retrieval (CIR) tasks.

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📌 Model Information

1. Model Name

ReTrack (Evidence-Driven Dual-Stream Directional Anchor Calibration Network) Checkpoints.

2. Task Type & Applicable Tasks

• Task Type: Composed Video Retrieval (CVR) and Composed Image Retrieval (CIR).
• Applicable Tasks: Retrieving a target video or image based on a reference visual input combined with a modification text prompt. The model significantly reduces uncertainty caused by highly similar retrieval candidates in multi-modal queries.

3. Project Introduction

ReTrack is an advanced open-source PyTorch framework built on top of BLIP-2 (via Salesforce LAVIS) that improves multi-modal query understanding. It features:

• 🎯 Dual-Stream Directional Anchor Calibration: Explicitly identifies and calibrates visual and textual semantic contributions to resolve directional bias.
• ⚖️ Reliable Evidence-Driven Alignment: Leverages Dempster-Shafer Theory to evaluate similarity reliability, minimizing ambiguity among candidates.

4. Training Data Source & Hosted Weights

The framework is trained to support both the WebVid-CoVR dataset for video retrieval and the FashionIQ / CIRR datasets for image retrieval.

This Hugging Face repository provides the following pre-trained checkpoint:

• 📄 ReTrack-WebVid-Frame1.ckpt: The checkpoint trained on the WebVid-CoVR dataset (using a 1-frame configuration setting).

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🚀 Usage & Basic Inference

These weights are designed to be evaluated using the highly modular, Hydra-configured ReTrack GitHub repository.

Step 1: Prepare the Environment

We recommend using Anaconda. Clone the repository and install dependencies:

git clone https://github.com/iLearn-Lab/AAAI26-ReTrack.git
cd ReTrack
conda create -n retrack python=3.8 -y
conda activate retrack
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt

Step 2: Download Model Weights & Prepare Data

1. Download ReTrack-WebVid-Frame1.ckpt from this Hugging Face repository.
2. Place the checkpoint in the appropriate directory as expected by your Hydra configuration (e.g., within a checkpoints/ folder).
3. Ensure the WebVid-CoVR dataset is placed under your defined datasets_dir in configs/machine/default.yaml.

Step 3: Run Evaluation

To evaluate the trained CVR model, use test.py and specify the path to your downloaded checkpoint via Hydra CLI overrides:

python test.py \
    model.ckpt_path=/path/to/your/ReTrack-WebVid-Frame1.ckpt \
    +test=webvid-covr

(Refer to the configs/ directory in the code repository for advanced hyperparameter and path adjustments).

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⚠️ Limitations & Notes

• Configuration: ReTrack is entirely managed by Hydra and Lightning Fabric. Make sure you are familiar with overriding configurations via the CLI or modifying the YAML files in the configs/ directory.
• Environment: The project was specifically developed and evaluated on Python 3.8 and PyTorch 2.1.0; using drastically different versions may yield unexpected behaviors.

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📝⭐️ Citation

If you find our framework, code, or these weights useful in your research, please consider leaving a Star ⭐️ on our GitHub repository and citing our AAAI 2026 paper:

@inproceedings{ReTrack,
  title={ReTrack: Evidence Driven Dual Stream Directional Anchor Calibration Network for Composed Video Retrieval},
  author={Li, Zixu and Hu, Yupeng and Chen, Zhiwei and Huang, Qinlei and Qiu, Guozhi and Fu, Zhiheng and Liu, Meng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2026}
}