Datasets:

iLearn-Lab
/

MM25-OFFSET-SegmentationData

+---
+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&#160;Chen</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://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>,
+  <a target="_blank" href="https://xuemengsong.github.io">Xuemeng&#160;Song</a><sup>2</sup>,
+  <a target="_blank" href="https://liqiangnie.github.io/index.html">Liqiang&#160;Nie</a><sup>3</sup>
+  </div>
+  <sup>1</sup>School of Software, Shandong University &#160&#160&#160</span>
+  <br />
+  <sup>2</sup>Department of Data Science, City University of Hong Kong, &#160&#160&#160</span>
+  <br />
+ <sup>3</sup>School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), &#160&#160&#160</span>  <br />
+  <sup>&#9993&#160;</sup>Corresponding author&#160;&#160;</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}
+}
+```