VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors

Vision Language Models (VLMs) excel in various multimodal tasks but struggle with fine-grained visual perception when information cannot be mapped to known language concepts, highlighting a reliance on brittle textual descriptions. This limitation affects vision-focused tasks like detecting matching entities between images, where VLMs perform better for nameable objects compared to unnameable ones, indicating that their success hinges on learned shortcuts rather than inherent multimodal capabilities.

Read the full article at arXiv cs.CV (Vision)

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