Datasets:
Add task category, paper, project page, and code links, and update citation
#3
by
nielsr
HF Staff
- opened
README.md
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---
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tags:
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- Multimodal
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dataset_info:
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features:
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- name: id
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dtype: string
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- name: img_id3
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dtype: string
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- name: filename3
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dtype: string
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- name: description3
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dtype: string
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split: ica
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path: data/ria-*
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- split: ica
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path: data/ica-*
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license: cc-by-4.0
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---
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# MM-OPERA: Multi-Modal OPen-Ended Reasoning-guided Association Benchmark π§ π
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## Overview π
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MM-OPERA is a benchmark designed to evaluate the open-ended association reasoning capabilities of Large Vision-Language Models (LVLMs). With 11,497 instances, it challenges models to identify and express meaningful connections across distant concepts in an open-ended format, mirroring human-like reasoning. The dataset spans diverse cultural, linguistic, and thematic contexts, making it a robust tool for advancing multimodal AI research. πβ¨
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If you use this dataset in your work, please cite it as follows:
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```bibtex
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@
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-
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year
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publisher = {Zenodo},
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version = {1.0.0},
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doi = {10.5281/zenodo.17300924},
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url = {https://doi.org/10.5281/zenodo.17300924}
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}
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```
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---
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license: cc-by-4.0
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tags:
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- Multimodal
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task_categories:
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- image-text-to-text
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dataset_info:
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features:
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- name: id
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dtype: string
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- name: img_id3
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dtype: string
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split: ica
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- name: filename3
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dtype: string
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split: ica
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- name: description3
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dtype: string
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split: ica
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path: data/ria-*
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- split: ica
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path: data/ica-*
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---
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# MM-OPERA: Multi-Modal OPen-Ended Reasoning-guided Association Benchmark π§ π
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[Paper](https://huggingface.co/papers/2510.26937) | [Project Page](https://mm-opera-bench.github.io/) | [Code](https://github.com/MM-OPERA-Bench/MM-OPERA)
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## Overview π
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MM-OPERA is a benchmark designed to evaluate the open-ended association reasoning capabilities of Large Vision-Language Models (LVLMs). With 11,497 instances, it challenges models to identify and express meaningful connections across distant concepts in an open-ended format, mirroring human-like reasoning. The dataset spans diverse cultural, linguistic, and thematic contexts, making it a robust tool for advancing multimodal AI research. πβ¨
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If you use this dataset in your work, please cite it as follows:
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```bibtex
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@inproceedings{huang2025mmopera,
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title={{MM-OPERA: Benchmarking Open-ended Association Reasoning for Large Vision-Language Models}},
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author={Zimeng Huang and Jinxin Ke and Xiaoxuan Fan and Yufeng Yang and Yang Liu and Liu Zhonghan and Zedi Wang and Junteng Dai and Haoyi Jiang and Yuyu Zhou and Keze Wang and Ziliang Chen},
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booktitle={Advances in Neural Information Processing Systems 39},
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year={2025}
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}
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```
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