Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- image-retrieval
+tags:
+- composed-image-retrieval
+- pytorch
+- icassp-2025
+---
+
+<div align="center">
+  <h1>(ICASSP 2025) MEDIAN: Adaptive Intermediate-grained Aggregation Network for Composed Image Retrieval</h1>
+  <div>
+    <a target="_blank" href="https://windlikeo.github.io/HQL.github.io/">Qinlei Huang</a><sup>1</sup>,
+    <a target="_blank" href="https://zivchen-ty.github.io">Zhiwei Chen</a><sup>1</sup>,
+    <a target="_blank" href="https://lee-zixu.github.io">Zixu Li</a><sup>1</sup>,
+    Chunxiao Wang<sup>2</sup>,
+    <a target="_blank" href="https://xuemengsong.github.io">Xuemeng Song</a><sup>3</sup>,
+    <a target="_blank" href="https://faculty.sdu.edu.cn/huyupeng1/zh_CN/index.htm">Yupeng Hu</a><sup>1&#9993</sup>,
+    <a target="_blank" href="https://liqiangnie.github.io/index.html">Liqiang Nie</a><sup>4</sup>
+  </div>
+  <sup>1</sup>School of Software, Shandong University<br>
+  <sup>2</sup>Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences)<br>
+  <sup>3</sup>School of Computer Science and Technology, Shandong University<br>
+  <sup>4</sup>School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)<br>
+  <sup>&#9993;</sup>Corresponding author
+  <br/>
+  <p>
+    <a href="https://ieeexplore.ieee.org/document/10890642"><img alt="Paper" src="https://img.shields.io/badge/Paper-IEEE-green.svg?style=flat-square"></a>
+    <a href="https://windlikeo.github.io/MEDIAN.github.io/"><img alt="Project Page" src="https://img.shields.io/badge/Website-orange"></a>
+    <a href="https://github.com/iLearn-Lab/ICASSP25-MEDIAN"><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 checkpoints for **MEDIAN**, a composed image retrieval framework that adaptively aggregates intermediate-grained features and performs target-guided semantic alignment to better compose reference images and modification texts.
+
+---
+
+## 📌 Model Information
+
+### 1. Model Name
+**MEDIAN** (Adaptive Intermediate-grained Aggregation Network for Composed Image Retrieval).
+
+### 2. Task Type & Applicable Tasks
+- **Task Type:** Composed Image Retrieval (CIR).
+- **Applicable Tasks:** Retrieving a target image from a gallery based on a reference image together with a modification text.
+
+### 3. Project Introduction
+MEDIAN is designed to improve cross-modal composition in CIR by introducing adaptive intermediate-grained aggregation and target-guided semantic alignment. Instead of relying only on local and global granularity, it models **local-intermediate-global** feature composition to establish more precise correspondences between the reference image and the text query.
+
+### 4. Training Data Source
+According to the project README, MEDIAN is evaluated on three standard CIR datasets:
+
+- **CIRR**
+- **FashionIQ**
+- **Shoes**
+
+### 5. Hosted Weights
+This repository currently includes the following checkpoint files:
+
+- `CIRR.pth` — MEDIAN checkpoint for CIRR
+- `FashionIQ.pt` — MEDIAN checkpoint for FashionIQ
+- `Shoes.pt` — MEDIAN checkpoint for Shoes
+
+---
+
+## 🚀 Usage & Basic Inference
+
+These checkpoints are intended to be used with the official [MEDIAN GitHub repository](https://github.com/iLearn-Lab/ICASSP25-MEDIAN).
+
+### Step 1: Prepare the Environment
+Set up the environment following the project README:
+
+```bash
+git clone https://github.com/iLearn-Lab/ICASSP25-MEDIAN
+cd ICASSP25-MEDIAN
+conda create -n pair python=3.8.10
+conda activate pair
+pip install torch==2.0.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt
+```
+
+### Step 2: Prepare Data and Weights
+The original project README documents support for the following datasets:
+
+- `CIRR`
+- `FashionIQ`
+- `Shoes`
+
+Place the corresponding checkpoint file in your preferred checkpoint directory and provide the dataset paths when training or evaluating.
+
+### Step 3: Training
+The project README documents the following training command:
+
+```bash
+python3 train.py \
+  --model_dir ./checkpoints/MEDIAN \
+  --dataset {cirr,fashioniq,shoes} \
+  --cirr_path "" \
+  --fashioniq_path "" \
+  --shoes_path ""
+```
+
+### Step 4: Testing / Evaluation
+For CIRR test submission generation, the documented command is:
+
+```bash
+python src/cirr_test_submission.py model_path
+```
+
+Example checkpoint path:
+
+```text
+model_path = /path/to/CIRR.pth
+```
+
+---
+
+## ⚠️ Limitations & Notes
+
+- These checkpoints are intended for **academic research** and for reproducing the MEDIAN results reported in the ICASSP 2025 paper.
+- Dataset preparation is required before training or evaluation, and the supported datasets documented by the project are **CIRR**, **FashionIQ**, and **Shoes**.
+- The usage commands above are adapted from the official project README. Please refer to the GitHub repository if you need the full training and evaluation workflow.
+
+---
+
+## 📝 Citation
+
+If you find this work or these checkpoints useful in your research, please consider citing:
+
+```bibtex
+@inproceedings{MEDIAN,
+  title={MEDIAN: Adaptive Intermediate-grained Aggregation Network for Composed Image Retrieval},
+  author={Huang, Qinlei and Chen, Zhiwei and Li, Zixu and Wang, Chunxiao and Song, Xuemeng and Hu, Yupeng and Nie, Liqiang},
+  booktitle={Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing},
+  pages={1--5},
+  year={2025},
+  organization={IEEE}
+}
+```