Visual CoT
CoT: Chain-of-Thought
Overview
Visual Chain-of-Thought explores whether making intermediate visual reasoning explicit helps multimodal models interpret images and act on them. Approaches include attending to image regions in sequence, exploring reconstructed 3D scenes, predicting coordinates, and generating sketches or other visual intermediates; these are trained with supervised reasoning traces and reinforcement learning. Evaluations span spatial and geometry problems, visual grounding, medical question answering, video segmentation, image editing, and robotics. Visual intermediates can help with spatial tasks, but are not consistently better than text-only reasoning, and a central challenge is ensuring that intermediate steps are accurate and genuinely contribute to the answer.
Latest papers
19 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.