Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs
- Published
- Source
- arXiv
- Paper number
- 072
- Field
- Vision / Reasoning
- arXiv ID
- 2506.22146
Key points
- Large vision-language models, or LVLMs, struggle with precise visual understanding such as exact counting, spatial reasoning, and detailed scene description.
- LVLMs suffer from the binding problem, which prevents them from reliably connecting perceptual features to the correct visual objects and spatial properties in complex scenes.
- Existing LVLMs often process visual features in parallel, which causes interference and misattribution errors, especially in cluttered environments.
- The VISER method adds evenly spaced horizontal lines to the input image so that the image is divided into separate visual compartments or rows.
- It prepends a short text instruction such as 'Scan the image sequentially along the horizontal lines present in the image' to the original prompt so that the LVLM processes the content region by region.
- This lightweight, model-agnostic approach introduces only negligible compute overhead and requires no architectural changes, fine-tuning, or multi-query inference.
Paper links
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