FGVQA

This repository contains the FGVQA benchmark suite introduced in the paper Same or Not? Enhancing Visual Perception in Vision-Language Models.FGVQA contains 12,000 challenging (image, question, answer) tuples emphasizing fine-grained image understanding.

The benchmark suite is composed of six sub-benchmarks:

1. TWIN-eval
2. ILIAS
3. Google Landmarks v2
4. MET
5. CUB
6. Inquire

For evaluating on the dataset with LMMS-eval, please refer to this repo.

Citation

If you use the FGVQA benchmark suite in your research, please use the following BibTeX entry.

@misc{marsili2025notenhancingvisualperception,
      title={Same or Not? Enhancing Visual Perception in Vision-Language Models}, 
      author={Damiano Marsili and Aditya Mehta and Ryan Y. Lin and Georgia Gkioxari},
      year={2025},
      eprint={2512.23592},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.23592}, 
}