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About this paper
Weiyun Wang, Yiming Ren, Haowen Luo, Tiantong Li, Chenxiang Yan, Zhe Chen, Wenhai Wang, Qingyun Li and 4 more
Researchers from OpenGVLab, Shanghai AI Laboratory and Tsinghua University developed ASMv2, a Multi-modal Large Language Model designed to improve object relation comprehension and grounding in images, reducing factual inconsistencies. The model demonstrates superior performance on newly introduced relation-specific benchmarks and achieves state-of-the-art results across general vision-language and region-level tasks.
Key insights
- Explicitly grounding predicates to specific subject and object bounding boxes is crucial for MLLMs to learn fine-grained visual relationships and mitigate relation hallucination.
- High-quality, instruction-tuned relation conversation data, even generated by powerful multimodal models like GPT-4V, can effectively train MLLMs for complex relation comprehension.
- A two-stage training approach, combining general image-level vision-language alignment with specific region-level grounding and relation comprehension, is effective for building models with broad and deep visual understanding.
Summary by alphaXiv.
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