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--- |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: |
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- Objects.csv |
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- Segmentations.json |
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- visual_patterns.csv |
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task_categories: |
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- image-referring-segmentation |
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- image-segmentation |
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--- |
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# PixMMVP Benchmark |
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[Project Page](https://msiam.github.io/PixFoundationSeries/) | [Paper](https://huggingface.co/papers/2502.04192) | [GitHub](https://github.com/msiam/pixfoundation) |
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The PixMMVP dataset augments the [MMVP](https://huggingface.co/datasets/MMVP/MMVP) benchmark with referring expressions and corresponding segmentation masks for the objects of interest in their respective questions within the original VQA task. |
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The goal of this benchmark is to evaluate the pixel-level visual grounding and visual question answering capabilities of recent pixel-level MLLMs (e.g., OMG-Llava, Llava-G, GLAMM, and LISA). |
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# Acknowledgements |
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I acknowledge the use of MMVP dataset's images and questions/choices part of building this dataset, the original [MMVP](https://huggingface.co/datasets/MMVP/MMVP). |
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# Citation |
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Please cite the following work if you find the dataset useful: |
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```bibtex |
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@article{siam2025pixfoundation, |
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title={PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?}, |
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author={Siam, Mennatullah}, |
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journal={arXiv preprint arXiv:2502.04192}, |
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year={2025} |
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} |
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``` |
