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The Role of Chain-of-Thought in Complex Vision-Language Reasoning Task
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The study explores the effectiveness of the Chain-of-Thought approach, known for its proficiency in language tasks by breaking them down into sub-tasks and intermediate steps, in improving vision-language tasks that demand sophisticated perception and reasoning. We present the "Description then Decision" strategy, which is inspired by how humans process signals. This strategy significantly improves probing task performance by 50%, establishing the groundwork for future research on reasoning paradigms in complex vision-language tasks.
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Cited by 5 Pith papers
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MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models
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ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models
Interleaving key video frames into step-by-step reasoning improves video question answering by 1.7 to 5.5 points over text-only chain-of-thought on a new self-built benchmark.
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Answer-oriented chain-of-thought prompts that generate both positive and negative reasoning data, combined with iterative DPO, improve multimodal LLM reasoning on several benchmarks.
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RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought
RSVP couples region-grid visual prompting and multimodal chain-of-thought reasoning with a BEiT-3/SAM segmentation module, achieving state-of-the-art zero-shot results on ReasonSeg and SegInW.
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