🎬 (AAAI 2026) ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval (Model Weights)
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.
---
## 📌 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).
---
## 🚀 Usage & Basic Inference
These weights are designed to be evaluated using the highly modular, Hydra-configured [ReTrack GitHub repository](https://github.com/Lee-zixu/ReTrack).
### Step 1: Prepare the Environment
We recommend using Anaconda. Clone the repository and install dependencies:
```bash
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:
```bash
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)*.
---
## ⚠️ 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.
---
## 📝⭐️ 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:
```bibtex
@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}
}
```