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About this paper
Ziwei Yao, Qiyang Wan, Ruiping Wang, Xilin Chen
Researchers at the Institute of Computing Technology, Chinese Academy of Sciences, developed VisKnow, a framework for building object-centric, multi-modal visual knowledge bases that integrate textual and visual information. The framework was used to construct AnimalKB, which enhances zero-shot recognition for fine-grained categories and improves MLLM performance in VQA.
Key insights
- In-depth object understanding requires systematic integration and alignment of both visual and non-visual knowledge, extending beyond basic category labels.
- A hybrid construction pipeline, leveraging expert design, crowdsourcing, and large-scale models, enables cost-effective and high-quality multi-modal knowledge base creation.
- Explicitly structured visual knowledge can significantly improve the reasoning capabilities and reduce factual errors in multi-modal large language models.
Summary by alphaXiv.
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