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README.md

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----
-license: apache-2.0
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+
+---
+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}
+}
+```
+```