README.md

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title: ReasonMatch
emoji: 🧩
colorFrom: blue
colorTo: purple
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ReasonMatch

Official Hugging Face organization for ReasonMatch-Bench and the paper:

Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching

Overview

ReasonMatch-Bench evaluates visual reasoning over point correspondences. It focuses on wide-baseline matching, where models must reason across viewpoint changes, geometric transformations, fine-grained visual evidence, and occlusion.

The project also includes Dynamic Correspondence Reinforcement Learning (DCRL), a verifiable reinforcement learning recipe for wide-baseline matching without explicit chain-of-thought supervision.

Links

Resource	Link
Paper	https://arxiv.org/abs/2606.03577
Project page	https://aim-uofa.github.io/reasonmatch/
Code	https://github.com/aim-uofa/ReasonMatch
Dataset	https://huggingface.co/datasets/ReasonMatch/ReasonMatch
Original ModelScope dataset	https://www.modelscope.cn/datasets/jxzh2020/ReasonMatchBench
Pretrained weights	To be announced

Resources

• ReasonMatch-Bench: in-domain evaluation benchmark for visual matching.
• Out-of-domain evaluation suite: additional generalization evaluation set.
• Training recipe: veRL-based DCRL training code for multimodal models.
• Evaluation code: scripts for ReasonMatch-Bench and out-of-domain evaluation.

Dataset

The public evaluation archives are hosted on Hugging Face:

pip install -U huggingface_hub

hf download ReasonMatch/ReasonMatch \
  reasonmatch_bench.tar.gz ood_dataset.tar.gz \
  --repo-type dataset --local-dir ReasonMatchBench

tar -xzf ReasonMatchBench/reasonmatch_bench.tar.gz -C ReasonMatchBench
tar -xzf ReasonMatchBench/ood_dataset.tar.gz -C ReasonMatchBench

They are also available from the original ModelScope dataset:

dataset_path=/your/absolute/path/to/datasets
dataset_repo=jxzh2020/ReasonMatchBench

modelscope download --repo-type dataset "${dataset_repo}" \
  --include 'reasonmatch_bench.tar.gz' 'ood_dataset.tar.gz' \
  --local_dir "${dataset_path}"

tar -xzf "${dataset_path}/reasonmatch_bench.tar.gz" -C "${dataset_path}"
tar -xzf "${dataset_path}/ood_dataset.tar.gz" -C "${dataset_path}"

After extraction:

• reasonmatch_bench/ contains ReasonMatch-Bench and annotation_index.json.
• ood_dataset/ contains the out-of-domain evaluation set.

Citation

@InProceedings{Zhong_2026_CVPR,
    author    = {Zhong, Hao and Zhu, Muzhi and Zeng, Shenyan and Li, Anzhou and Chen, Cong and Geng, Hua and Shi, Duochao and Ye, Wentao and Lin, Tao and Chen, Hao and Shen, Chunhua},
    title     = {Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2026},
    pages     = {16768-16778}
}