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About this paper
Wei Wang, Zhaowei Li, Qi Xu, Linfeng Li, YiQing Cai, Botian Jiang, Hang Song, Xingcan Hu and 2 more
TinyGroundingGPT introduces a series of compact Multi-Modal Large Language Models that enhance fine-grained visual understanding by implementing a comprehensive multi-scale alignment method and a specialized data synthesis pipeline. These models achieve competitive performance on grounding benchmarks and reduce object hallucination while maintaining a small parameter footprint.
Key insights
- Comprehensive alignment of all six modality pairs (coordinates-to-text, coordinates-to-image, text-to-coordinates, text-to-image, image-to-text, image-to-coordinates) is essential for robust fine-grained visual understanding.
- A systematic data synthesis pipeline can generate high-quality, multi-scale fine-grained training data, which is critical for learning precise visual grounding.
- Compact Multi-Modal Large Language Models (1.5B-3B parameters) can achieve competitive performance with much larger models by focusing on improved alignment methods and targeted data rather than just increasing model scale.
Summary by alphaXiv.
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