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license: mit |
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tags: |
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- Multimodal Large Language Model (MLLM) |
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- Visual Grounding |
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- Reinforcement Fine-tuning |
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# UniVG-R1 Model Card |
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<a href='https://amap-ml.github.io/UniVG-R1-page/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> |
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<a href='https://arxiv.org/abs/2505.14231'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> |
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<a href='https://github.com/AMAP-ML/UniVG-R1'><img src='https://img.shields.io/badge/Code-GitHub-blue'></a> |
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## Model details |
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We propose UniVG-R1, a reasoning-guided MLLM for universal visual grounding, which leverages reinforcement learning to enhance reasoning across complex multi-image and multi-modal scenarios. |