Reasoning with Image Generation
ReImaGin uses image generators as flexible visual reasoning tools for multimodal LLMs, beating text-only and specialist baselines.
The paper proposes ReImaGin, which uses image generation models as an open-ended visual reasoning mechanism for multimodal LLMs instead of narrow tools such as depth estimation or object detection. Generators accept natural language commands and can remove occlusions or synthesize a floorplan from disjoint room views. Across six visual reasoning tasks, including multi-view spatial reasoning and collision prediction, ReImaGin outperforms text-only reasoning and specialist vision-tool baselines by up to 25%.
- ReImaGin uses image generators as flexible visual reasoners for multimodal LLMs.
- Generators follow language commands for open-ended visual operations, unlike fixed tools.
- Tasks include multi-view spatial reasoning and collision prediction.
- Gains reach up to 25% over text-only and specialist vision-tool baselines.
From the original article
1 min read at huggingface.co
Chain-of-thought reasoning has revolutionized natural language processing by enabling large language models (LLMs) to decompose problems into intermediate steps before answering. Yet confining reasoning to the textual domain presents limitations for tasks requiring direct manipulation of visual representations.
By Nishad Singhi, Hector Garcia Rodriguez, Aditya Arora · huggingface.co
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