🎬 (ICASSP 2026) RELATE: Enhance Composed Video Retrieval via Minimal-Redundancy Hierarchical Collaboration (Model Weights)
1School of Software, Shandong University
✉ Corresponding author
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}
}
```