ZoomGain: Mining Global-to-Zoom Perception Gaps for Fine-Grained Visual Supervision

arXiv:2611.14076v1 Announce Type: cross Abstract: Fine-grained visual perception requires Vision-Language Models (VLMs) to locate and interpret subtle evidence within complex scenes. Constructing supervision for this ability is challenging: questions must have correct, unambiguous answers that depend on actual fine-grained visual details rather than coarse cues or language priors. Verifying these properties makes manual annotation labor-intensive and automatic generation difficult for models that themselves struggle with fine-grained perception. Yet the same models can often recognize such det

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