Adapting Vision Foundation Models with Cascaded Semantics
Prompt tuning, a leading parameter-efficient adaptation paradigm in NLP, has recently been extended to computer vision. Visual prompt tuning (VPT) adapts pre-trained vision transformers (ViTs) by updating a small set of additional prompt parameters. However, existing visual prompts are randomly initialized and do not exploit prior knowledge, such as instructions in NLP. We address this gap by injecting two complementary semantic priors into VPT. Fundamental image priors, including color, texture, and shape, are extracted with classical hand-crafted operators and injected into the input space, while self-attention maps provide instance-aware semantics in the feature space. We further propo...
| Authors | Xi Xiao, Xingjian Li, Cheng Han et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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