README.md

---
license: apache-2.0
task_categories:
- image-retrieval
- vision-language-navigation
tags:
- composed-image-retrieval
- multimodal-retrieval
- pytorch
- aaai-2025
---

<a id="top"></a>
<div align="center">
  <h1>(AAAI 2025) ENCODER: Entity Mining and Modification Relation Binding for Composed Image Retrieval (Model Weights)</h1>
  <div>
  <a target="_blank" href="https://lee-zixu.github.io/">Zixu&#160;Li</a><sup>1</sup>,
  <a target="_blank" href="https://zivchen-ty.github.io/">Zhiwei&#160;Chen</a><sup>1</sup>,
  <a target="_blank" href="https://haokunwen.github.io">Haokun&#160;Wen</a><sup>2,3</sup>,
  <a target="_blank" href="https://zhihfu.github.io/">Zhiheng&#160;Fu</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>,
  <a target="_blank" href="https://homepage.hit.edu.cn/guanweili">Weili&#160;Guan</a><sup>2</sup>
  </div>
  <sup>1</sup>School of Software, Shandong University &#160&#160&#160</span>  <br />
  <sup>2</sup>School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), &#160&#160&#160</span>  <br />
  <sup>2</sup>School of Data Science, City University of Hong Kong &#160&#160&#160</span>
  <br />
  <sup>&#9993&#160;</sup>Corresponding author&#160;&#160;</span>
  <br/>
  
  <p>
      <a href="https://aaai.org/Conferences/AAAI-25/"><img src="https://img.shields.io/badge/AAAI-2025-blue.svg?style=flat-square" alt="AAAI 2025"></a>
      <a href="https://ojs.aaai.org/index.php/AAAI/article/view/32541"><img alt='Paper' src="https://img.shields.io/badge/Paper-AAAI.32541-green.svg"></a>
      <a href="https://sdu-l.github.io/ENCODER.github.io/"><img alt='Project Page' src="https://img.shields.io/badge/Website-orange"></a>
      <a href="https://github.com/Lee-zixu/ENCODER"><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 **ENCODER**, a novel network designed to explicitly mine visual entities and modification actions, and securely bind implicit modification relations in Composed Image Retrieval (CIR).

---

## 📌 Model Information

### 1. Model Name
**ENCODER** (Entity miNing and modifiCation relation binDing nEtwoRk) Checkpoints.

### 2. Task Type & Applicable Tasks
- **Task Type:** Composed Image Retrieval (CIR).
- **Applicable Tasks:** Retrieving a target image based on a reference image and a corresponding modification text. The model excels at capturing fine-grained modification relations through multimodal semantic alignment.

### 3. Project Introduction
Existing CIR approaches often struggle with the modification relation between visual entities and modification actions due to irrelevant factor perturbation, vague semantic boundaries, and implicit modification relations. 

**ENCODER** introduces three innovative modules to achieve precise multimodal semantic alignment:
- 🔍 **Latent Factor Filter (LFF):** Filters out irrelevant visual and textual factors.
- 🔗 **Entity-Action Binding (EAB):** Employs modality-shared Learnable Relation Queries (LRQ) to mine visual entities and actions, learning their implicit relations to bind them effectively.
- 🧩 **Multi-scale Composition (MSC):** Performs multi-scale feature composition to precisely push the retrieved feature closer to the target image.

### 4. Training Data Source & Hosted Weights
The models were trained across four widely-used CIR datasets: **FashionIQ**, **Shoes**, **Fashion200K**, and **CIRR**. This Hugging Face repository provides the pre-trained `.pt` checkpoint files for each corresponding dataset:

* 📄 `cirr.pt`: Checkpoint trained on the open-domain CIRR dataset.
* 📄 `fashion200k.pt`: Checkpoint trained on the Fashion200K dataset.
* 📄 `fashioniq.pt`: Checkpoint trained on the FashionIQ dataset.
* 📄 `shoes.pt`: Checkpoint trained on the Shoes dataset.

---

## 🚀 Usage & Basic Inference

These weights are designed to be evaluated seamlessly using the official [ENCODER GitHub repository](https://github.com/Lee-zixu/ENCODER).

### Step 1: Prepare the Environment
Clone the GitHub repository and install dependencies:
```bash
git clone [https://github.com/Lee-zixu/ENCODER.git](https://github.com/Lee-zixu/ENCODER.git)
cd ENCODER
conda create -n encoder_env python=3.9
conda activate encoder_env
pip install torch torchvision torchaudio --index-url [https://download.pytorch.org/whl/cu118](https://download.pytorch.org/whl/cu118)
pip install -r requirements.txt
```

### Step 2: Download Model Weights
Download the specific `.pt` files you wish to evaluate from this Hugging Face repository. Place them into a `checkpoints/` directory within your cloned GitHub repo. 

### Step 3: Run Evaluation
To test your trained model on the validation set, use the `evaluate_model.py` script and point it to the downloaded weights:
```bash
python3 evaluation_model.py \
    --model_dir checkpoints/fashioniq.pt \
    --dataset fashioniq \
    --fashioniq_path "path/to/FashionIQ"
```

To generate the predictions file for uploading to the [CIRR Evaluation Server](https://cirr.cecs.anu.edu.au/), run:
```bash
python src/cirr_test_submission.py checkpoints/cirr.pt
```

---

## ⚠️ Limitations & Notes

- **Version Compatibility:** Different versions of `open_clip` can impact model performance. To ensure consistent State-of-the-Art performance as reported in the paper, please strictly adhere to the environment dependencies specified in the `requirements.txt` file of the official repository. 
- **State Dict Version:** These hosted weights are the updated "state_dict" version for stable evaluation.

---

## 📝⭐️ Citation

If you find this code or our paper useful for your research, please consider leaving a **Star** ⭐️ on our GitHub repository and citing our AAAI 2025 paper:

```bibtex
@inproceedings{ENCODER,
  title={Encoder: Entity mining and modification relation binding for composed image retrieval},
  author={Li, Zixu and Chen, Zhiwei and Wen, Haokun and Fu, Zhiheng and Hu, Yupeng and Guan, Weili},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={5},
  pages={5101--5109},
  year={2025}
}
```