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--- |
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license: mit |
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datasets: |
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- lmms-lab/GQA |
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- dmarsili/Omni3D-Bench |
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- cambridgeltl/vsr_random |
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- snowclipsed/TallyQA |
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language: |
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- en |
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base_model: |
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- ShilongLiu/GroundingDINO |
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pipeline_tag: object-detection |
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tags: |
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- object-detection |
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- computer-vision |
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--- |
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# Model Card for VALOR-GroundingDINO |
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This is the verified-tuned GroundingDINO model from the paper: [No Labels, No Problem: Training Visual Reasoners with Multimodal Verifiers](https://glab-caltech.github.io/valor/) |
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For further information please refer to the [project webpage](https://glab-caltech.github.io/valor/), [paper](https://arxiv.org/abs/2512.08889), and [repository](https://github.com/damianomarsili/VALOR). |
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## Citation |
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If you use VALOR in your research, please consider citing our work: |
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**BibTeX:** |
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``` |
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@misc{marsili2025labelsproblemtrainingvisual, |
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title={No Labels, No Problem: Training Visual Reasoners with Multimodal Verifiers}, |
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author={Damiano Marsili and Georgia Gkioxari}, |
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year={2025}, |
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eprint={2512.08889}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2512.08889}, |
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} |
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``` |
