Look, Reason, Draw a Region: Recursive Refinement for Spatial Context Prediction

Key points

• Spatial relationships and physical constraints remain challenging for large vision-language models (VLMs).1
• Recursive reasoning has demonstrated strong performance on discrete and abstract reasoning tasks, motivating its use for improving spatial context prediction in natural images.1
• We introduce Spatial-TRM, a compact recursive model with frozen vision-language features, a convex region decoder, and just 11.2M trainable parameters.1
• These results show that repeated refinement improves spatial context prediction from pretrained vision-language features at a fixed trainable parameter count on this benchmark.1

Sources (1)

• [1]Look, Reason, Draw a Region: Recursive Refinement for Spatial Context Prediction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 11:06 AM

  Spatial context prediction requires visual grounding and spatial reasoning to identify vacant regions relative to objects in a scene.

  Spatial relationships and physical constraints remain challenging for large vision-language models (VLMs).

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