Rethinking Layer-Wise Information Allocation for Vision Foundation Model Adaptation

arXiv cs.CV5d4 min read

arXiv:2607.21973v1 Announce Type: new Abstract: Vision foundation models are increasingly reused as frozen backbones for downstream visual recognition, making parameter-efficient adaptation a central problem. Prompt-based adaptation, including Visual Prompt Tuning (VPT), provides a lightweight way to specialize these models, but its layer-wise behavior remains poorly understood: performance is sensitive to prompt depth, placement, and task distribution, and gains on standard in-domain benchmarks do not always translate into robust generalization. We argue that this limitation is not solely an

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Rethinking Layer-Wise Information Allocation for Vision Foundation Model Adaptation

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