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About this paper
Daniel Rose, Vaishnavi Himakunthala, Andy Ouyang, Ryan He, Alex Mei, Yujie Lu, Michael Saxon, Chinmay Sonar and 2 more
This paper presents Visual Chain-of-Thought (VCOT), a method that extends chain-of-thought prompting to the vision-language domain by generating synthetic multimodal (text-visual) infillings. VCOT recursively bridges logical gaps in sequential data, leveraging large language models and vision models to produce consistent and novel intermediate steps, thereby enhancing performance on downstream tasks like visual storytelling and instruction summarization.
Key insights
- VCOT successfully balances generating novel information while maintaining consistency with surrounding sequential data through mechanisms like multipoint foveation and CLIP similarity alignment.
- The explicit generation of intermediate text-visual infillings enhances the interpretability of the AI model's reasoning process.
- Unifying diverse sequential data into a consistent text-visual pair format (even for initially text-only data) enables broad applicability of the VCOT method.
Summary by alphaXiv.
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