Improve dataset card: Add task category, license, paper, and code links for ReasonSeg-Test

This PR enhances the dataset card for the `ReasonSeg-Test` dataset by adding:
- The `task_categories: image-segmentation` to the metadata.
- The `license: cc-by-nc-4.0` to the metadata.
- Relevant `tags` and `language` to the metadata for better discoverability.
- A `size_categories: n<1K` tag based on the number of examples.
- A comprehensive description of the dataset and its associated project (Seg-Zero) in the content.
- Links to the associated paper and the official GitHub repository.
- Detailed evaluation instructions as sample usage for this test dataset.
- Citation information.

README.md

@@ -1,4 +1,17 @@
 ---
+license: cc-by-nc-4.0
+task_categories:
+  - image-segmentation
+language:
+  - en
+tags:
+  - reasoning
+  - zero-shot
+  - reinforcement-learning
+  - multi-modal
+  - VLM
+size_categories:
+  - n<1K
 dataset_info:
   features:
   - name: image
@@ -28,3 +41,104 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# ReasonSeg Test Dataset
+
+This repository contains the **ReasonSeg Test Dataset**, which serves as an evaluation benchmark for the paper [Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement](https://arxiv.org/abs/2503.06520).
+
+**Code:** [https://github.com/dvlab-research/Seg-Zero](https://github.com/dvlab-research/Seg-Zero)
+
+## Paper Abstract
+
+Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes. To address these limitations, we propose Seg-Zero, a novel framework that demonstrates remarkable generalizability and derives explicit chain-of-thought reasoning through cognitive reinforcement. Seg-Zero introduces a decoupled architecture consisting of a reasoning model and a segmentation model. The reasoning model interprets user intentions, generates explicit reasoning chains, and produces positional prompts, which are subsequently used by the segmentation model to generate precious pixel-level masks. We design a sophisticated reward mechanism that integrates both format and accuracy rewards to effectively guide optimization directions. Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Seg-Zero achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities. Experiments show that Seg-Zero-7B achieves a zero-shot performance of 57.5 on the ReasonSeg benchmark, surpassing the prior LISA-7B by 18%. This significant improvement highlights Seg-Zero's ability to generalize across domains while presenting an explicit reasoning process.
+
+## About Seg-Zero
+
+Seg-Zero is a novel framework for reasoning segmentation that utilizes cognitive reinforcement to achieve remarkable generalizability and explicit chain-of-thought reasoning.
+
+<div align=center>
+<img width="98%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/overview.png"/>
+</div>
+
+Seg-Zero demonstrates the following features:
+1.  Seg-Zero exhibits emergent test-time reasoning ability. It generates a reasoning chain before producing the final segmentation mask.
+2.  Seg-Zero is trained exclusively using reinforcement learning, without any explicit supervised reasoning data.
+3.  Compared to supervised fine-tuning, our Seg-Zero achieves superior performance on both in-domain and out-of-domain data.
+
+### Model Pipeline
+
+Seg-Zero employs a decoupled architecture, including a reasoning model and segmentation model. A sophisticated reward mechanism integrates both format and accuracy rewards.
+
+<div align=center>
+<img width="98%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/pipeline.png"/>
+</div>
+
+### Examples
+
+<div align=center>
+<img width="98%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/examples.png"/>
+</div>
+
+## Sample Usage: Evaluation
+
+This dataset (`ReasonSeg-Test`) is designed for evaluating the zero-shot performance of models like Seg-Zero on reasoning-based image segmentation tasks.
+
+First, install the necessary dependencies for the Seg-Zero project:
+
+```bash
+git clone https://github.com/dvlab-research/Seg-Zero.git
+cd Seg-Zero
+conda create -n visionreasoner python=3.12
+conda activate visionreasoner
+pip install torch==2.6.0 torchvision==0.21.0
+pip install -e .
+```
+
+Then, you can run evaluation using the provided scripts. Make sure to download pretrained models first:
+
+```bash
+mkdir pretrained_models
+cd pretrained_models
+git lfs install
+git clone https://huggingface.co/Ricky06662/VisionReasoner-7B
+```
+
+With the pretrained models downloaded, you can run the evaluation script for ReasonSeg:
+
+```bash
+bash evaluation_scripts/eval_reasonseg_visionreasoner.sh
+```
+
+Adjust `'--batch_size'` in the bash scripts based on your GPU. You will see the gIoU in your command line.
+
+<div align=center>
+<img width="98%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/val_results.png"/>
+</div>
+
+## The GRPO Algorithm
+
+Seg-Zero generates several samples, calculates the rewards and then optimizes towards samples that achieve higher rewards.
+
+<div align=center>
+<img width="48%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/rl_sample.png"/>
+</div>
+
+## Citation
+
+If you use this dataset or the Seg-Zero framework, please cite the associated papers:
+
+```bibtex
+@article{liu2025segzero,
+  title        = {Seg-Zero: Reasoning-Chain Guided  Segmentation via Cognitive Reinforcement},
+  author       = {Liu, Yuqi and Peng, Bohao and Zhong, Zhisheng and Yue, Zihao and Lu, Fanbin and Yu, Bei and Jia, Jiaya},
+  journal      = {arXiv preprint arXiv:2503.06520},
+  year         = {2025}
+}
+
+@article{liu2025visionreasoner,
+  title        = {VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning},
+  author       = {Liu, Yuqi and Qu, Tianyuan and Zhong, Zhisheng and Peng, Bohao and Liu, Shu and Yu, Bei and Jia, Jiaya},
+  journal = {arXiv preprint arXiv:2505.12081},
+  year         = {2025}
+}
+```