README.md

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

<a id="top"></a>
<div align="center">
  <h1>🎬 (ICASSP 2026) RELATE: Enhance Composed Video Retrieval via Minimal-Redundancy Hierarchical Collaboration (Model Weights)</h1>
  <div>
  Shiqi&#160;Zhang<sup>1</sup>,
  <a target="_blank" href="https://zivchen-ty.github.io/">Zhiwei&#160;Chen</a><sup>1</sup>,
  <a target="_blank" href="https://lee-zixu.github.io/">Zixu&#160;Li</a><sup>1</sup>,
  <a target="_blank" href="https://zhihfu.github.io/">Zhiheng&#160;Fu</a><sup>1</sup>,
  Wenbo&#160;Wang<sup>1</sup>,
  Jiajia&#160;Nie<sup>1</sup>,
 <a target="_blank" href="https://faculty.sdu.edu.cn/weiyinwei1/zh_CN/index.htm">Yinwei&#160;Wei</a><sup>1</sup>,
  <a target="_blank" href="https://faculty.sdu.edu.cn/huyupeng1/zh_CN/index.htm">Yupeng&#160;Hu</a><sup>1&#9993</sup>
  </div>
  <sup>1</sup>School of Software, Shandong University &#160&#160&#160</span> <br>
  <sup>&#9993&#160;</sup>Corresponding author&#160;&#160;</span>
  <br/>
  <p>
      <a href="https://arxiv.org/abs/coming soon"><img alt='arXiv' src="https://img.shields.io/badge/arXiv-Coming.Soon-b31b1b.svg?style=flat-square"></a>
      <a href="https://github.com/iLearn-Lab/ICASSP26-RELATE"><img alt='GitHub' src="https://img.shields.io/badge/GitHub-Repository-black?style=flat-square&logo=github"></a>
  </p>
</div>

This repository hosts the official pre-trained model weights for **RELATE**, a minimal-redundancy hierarchical collaborative network designed to enhance both Composed Video Retrieval (CVR) and Composed Image Retrieval (CIR) tasks.


---

## 📌 Model Information

### 1. Model Name
**RELATE** (Enhance Composed Video Retrieval via Minimal-Redundancy Hierarchical Collaboration) Checkpoints.

### 2. Task Type & Applicable Tasks
- **Task Type:** Composed Video Retrieval (CVR) and Composed Image Retrieval (CIR).
- **Applicable Tasks:** Retrieving target videos or images based on a reference visual input and modification text. The model excels by addressing the neglect of the internal hierarchical structure of modification texts and the insufficient suppression of video temporal redundancy.

### 3. Project Introduction
**RELATE** is an advanced open-source PyTorch framework built on top of BLIP-2. It achieves State-of-the-Art (SOTA) performance across major benchmarks through three key innovations:
- 🧩 **Hierarchical Query Generation:** Parses the internal hierarchical structure of the text to understand the roles of various parts of speech, using noun phrases for object-level features and the complete text for global semantics.
- ✂️ **Temporal Sparsification:** Adaptively attenuates redundant tokens corresponding to static backgrounds while amplifying critical dynamic information tokens.
- 🎯 **Modification-Driven Modulation Learning:** Leverages global semantics of the modification text to perform attention-based filtering on the sparsified visual features.

### 4. Training Data Source & Hosted Weights
The RELATE framework seamlessly supports and is evaluated on standard video and image retrieval benchmarks. This repository provides pre-trained weights for the following datasets:
* **CVR:** WebVid-CoVR dataset.
* **CIR:** FashionIQ and CIRR datasets.

*(Note: Please download the respective `.ckpt` or `.pt` files hosted in the "Files and versions" tab of this Hugging Face repository).*

---

## 🚀 Usage & Basic Inference

These weights are designed to be evaluated using the official Hydra-configured [RELATE GitHub repository](https://github.com/iLearn-Lab/ICASSP26-RELATE).

### Step 1: Prepare the Environment
We recommend using Anaconda to manage your environment. Clone the repository and install the required dependencies:
```bash
git clone https://github.com/iLearn-Lab/ICASSP26-RELATE
cd RELATE
conda create -n relate python=3.8 -y
conda activate relate
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt
```

### Step 2: Download Model Weights
Download the required checkpoints from this repository and place them into your local workspace. Ensure your dataset paths are correctly configured in `configs/machine/default.yaml`.

### Step 3: Run Evaluation
To evaluate a trained model, use `test.py` and specify the target benchmark and your checkpoint path via Hydra overrides:
```bash
python test.py \
    model.ckpt_path=/path/to/your/downloaded_checkpoint.ckpt \
    +test=webvid-covr # or fashioniq / cirr-all
```

---

## ⚠️ Limitations & Notes

- **Configuration:** The entire framework is managed by **Hydra** and **Lightning Fabric**. Ensure you adjust hyperparameter overrides or modify the YAML files in the `configs/` directory to suit your specific local setup.
- **Environment Dependency:** This project was developed and extensively tested with Python 3.8 and PyTorch 2.1.0.

---

## 📝⭐️ 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 ICASSP 2026 paper:

```bibtex
@inproceedings{RELATE,
  title={RELATE: Enhance Composed Video Retrieval via Minimal-Redundancy Hierarchical Collaboration},
  author={Zhang, Shiqi and Chen, Zhiwei and Li, Zixu and Fu, Zhiheng and Wang, Wenbo and Nie, Jiajia and Wei, Yinwei and Hu, Yupeng},
  booktitle={Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing},
  year={2026}
}
```