Introduces group matching score for better evaluation of compositional reasoning and Test-Time Matching (TTM) algorithm for unsupervised self-improvement in multimodal models, achieving SOTA gains including surpassing GPT-4.1 and estimated human performance.
The role of chain-of-thought in complex vision-language reasoning task.arXiv preprint arXiv:2311.09193
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Perception-verified self-training with PerceptEval and two-stage curriculum learning improves VLM reasoning by up to 16% over standard self-training baselines.
The paper provides the first comprehensive survey of multimodal chain-of-thought reasoning, including foundational concepts, a taxonomy of methodologies, application analyses, challenges, and future directions.
citing papers explorer
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Test-Time Matching: Unlocking Compositional Reasoning in Multimodal Models
Introduces group matching score for better evaluation of compositional reasoning and Test-Time Matching (TTM) algorithm for unsupervised self-improvement in multimodal models, achieving SOTA gains including surpassing GPT-4.1 and estimated human performance.
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Improving Reasoning in Vision-Language Models via Perception Verified Self-Training
Perception-verified self-training with PerceptEval and two-stage curriculum learning improves VLM reasoning by up to 16% over standard self-training baselines.
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Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
The paper provides the first comprehensive survey of multimodal chain-of-thought reasoning, including foundational concepts, a taxonomy of methodologies, application analyses, challenges, and future directions.