| --- |
| license: apache-2.0 |
| task_categories: |
| - image-segmentation |
| - text-to-image |
| - image-to-text |
| tags: |
| - composed-image-retrieval |
| - fashioniq |
| - cirr |
| - shoes |
| - acm-mm-2025 |
| --- |
| |
| <a id="top"></a> |
| <div align="center"> |
| <h1>(ACM MM 2025) OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval</h1> |
| <div align="center"> |
| <a target="_blank" href="https://zivchen-ty.github.io/">Zhiwei Chen</a><sup>1</sup>, |
| <a target="_blank" href="https://faculty.sdu.edu.cn/huyupeng1/zh_CN/index.htm">Yupeng Hu</a><sup>1✉</sup>, |
| <a target="_blank" href="https://lee-zixu.github.io/">Zixu Li</a><sup>1</sup>, |
| <a target="_blank" href="https://zhihfu.github.io/">Zhiheng Fu</a><sup>1</sup>, |
| <a target="_blank" href="https://xuemengsong.github.io">Xuemeng Song</a><sup>2</sup>, |
| <a target="_blank" href="https://liqiangnie.github.io/index.html">Liqiang Nie</a><sup>3</sup> |
| </div> |
| <sup>1</sup>School of Software, Shandong University    </span> |
| <br /> |
| <sup>2</sup>Department of Data Science, City University of Hong Kong,    </span> |
| <br /> |
| <sup>3</sup>School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen),    </span> <br /> |
| <sup>✉ </sup>Corresponding author  </span> |
| <br/> |
| <p> |
| <a href="https://acmmm2025.org/"><img src="https://img.shields.io/badge/ACM_MM-2025-blue.svg?style=flat-square" alt="ACM MM 2025"></a> |
| <a href="https://arxiv.org/abs/2507.05631"><img alt='arXiv' src="https://img.shields.io/badge/arXiv-2507.05631-b31b1b.svg"></a> |
| <a href="https://github.com/iLearn-Lab/MM25-OFFSET"><img alt='GitHub' src="https://img.shields.io/badge/GitHub-Repository-black?style=flat-square&logo=github"></a> |
| </p> |
| </div> |
| |
| This dataset contains the official pre-computed dominant portion segmentation data used in the **OFFSET** framework for Composed Image Retrieval (CIR). |
|
|
| --- |
|
|
| ## 📌 Dataset Information |
|
|
| ### 1. Dataset Source |
| This dataset is derived from the official visual data of three widely-used Composed Image Retrieval (CIR) datasets: **FashionIQ**, **Shoes**, and **CIRR**. |
| The segmentation data within this repository was machine-generated using visual language models (BLIP-2) to create image captions as a role-supervised signal, dividing images into dominant and noisy regions by CLIPSeg. |
|
|
| ### 2. Dataset Purpose |
| This data serves as the foundational input for the **Dominant Portion Segmentation** module in the OFFSET architecture. It is designed to: |
| * Effectively mask noise information in visual data. |
| * Act as a guiding signal for the Dual Focus Mapping (Visual and Textual Focus Mapping branches). |
| * Address visual inhomogeneity and text-priority biases in Composed Image Retrieval tasks. |
|
|
| ### 3. Field Descriptions & Structure |
| The dataset is provided as a single compressed archive: `OFFSET_dominant_portion_segmentation.zip`. Once extracted, it contains pre-computed segmentation masks corresponding to the reference and target images of the downstream datasets. |
|
|
| * **Image ID / Filename:** Corresponds directly to the original image names in FashionIQ (e.g., `B000ALGQSY.jpg`), Shoes (e.g., `img_womens_athletic_shoes_375.jpg`), and CIRR (e.g., `train-10108-0-img0.png`). |
| * **Segmentation Mask/Data:** The processed dominant portion arrays/tensors indicating the salient regions versus noisy background regions. |
|
|
| ### 4. Data Split |
| The segmentation data aligns strictly with the official dataset splits of the corresponding benchmarks: |
| * **FashionIQ:** `train` / `val` |
| * **Shoes:** `train` / `test` |
| * **CIRR:** `train` / `dev` / `test1` |
|
|
| ### 5. License & Commercial Use |
| This segmentation dataset is released under the **Apache 2.0 License**, which permits commercial use, modification, and distribution. |
| *Note:* While this specific segmentation data is Apache 2.0, users must still comply with the original licenses of the underlying FashionIQ, Shoes, and CIRR datasets when using them in conjunction. |
|
|
| ### 6. Usage Restrictions & Ethical Considerations |
| * **Limitations:** This data is specifically optimized for the OFFSET model architecture and standard CIR tasks. Generalizing these specific masks to completely unrelated dense prediction tasks may yield sub-optimal results. |
| * **Privacy & Ethics:** The source datasets consist of publicly available e-commerce product images (FashionIQ, Shoes) and natural real-world images (NLVR2/CIRR). The pre-computed segmentation process does not introduce new personally identifiable information (PII) or ethical risks beyond those present in the original public benchmarks. |
|
|
| --- |
|
|
| ## 🚀 How to Use |
|
|
| This dataset is designed to be used directly with the official OFFSET GitHub repository. |
|
|
| **1. Download the Data:** |
| Download `OFFSET_dominant_portion_segmentation.zip` from the Files section and extract it. |
|
|
| **2. Organize the Directory:** |
| Place the extracted segmentation data into your local environment alongside the original datasets, following the directory requirements specified in the [OFFSET GitHub Repository Data Preparation guide](https://github.com/iLearn-Lab/MM25-OFFSET#--data-preparation). |
|
|
| **3. Run Training/Evaluation:** |
| Point the training script to the extracted data paths: |
| ```bash |
| python3 train.py \ |
| --model_dir ./checkpoints/ \ |
| --dataset {shoes, fashioniq, cirr} \ |
| --cirr_path "path/to/CIRR" \ |
| --fashioniq_path "path/to/FashionIQ" \ |
| --shoes_path "path/to/Shoes" |
| ``` |
|
|
| --- |
|
|
| ## 📝⭐️ Citation |
|
|
| If you find this dataset or the OFFSET framework useful in your research, please consider leaving a **Star**⭐️ on our GitHub repository and **Citing**📝 our ACM MM 2025 paper: |
|
|
| ```bibtex |
| @inproceedings{OFFSET, |
| title = {OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval}, |
| author = {Chen, Zhiwei and Hu, Yupeng and Li, Zixu and Fu, Zhiheng and Song, Xuemeng and Nie, Liqiang}, |
| booktitle = {Proceedings of the ACM International Conference on Multimedia}, |
| pages = {6113–6122}, |
| year = {2025} |
| } |
| ``` |
|
|
