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The pretrain-then-finetune paradigm has been widely adopted in computervision. But as the size of Vision Transformer (ViT) grows exponentially, thefull finetuning becomes prohibitive in view of the heavier storage overhead.Motivated by parameter-efficient transfer learning (PETL) on languagetransformers, recent studies attempt to insert lightweight adaptation modules(e.g., adapter layers or prompt tokens) to pretrained ViT and only finetunethese modules while the pretrained weights are frozen. However, these moduleswere originally proposed to finetune language models and did not take intoaccount the prior knowledge specifically for visual tasks. In this paper, wepropose to construct Convolutional Bypasses (Convpass) in ViT as adaptationmodules, introducing only a small amount (less than 0.5% of model parameters)of trainable parameters to adapt the large ViT. Different from other PETLmethods, Convpass benefits from the hard-coded inductive bias of convolutionallayers and thus is more suitable for visual tasks, especially in the low-dataregime. Experimental results on VTAB-1K benchmark and few-shot learningdatasets show that Convpass outperforms current language-oriented adaptationmodules, demonstrating the necessity to tailor vision-oriented adaptationmodules for adapting vision models.

Convolutional Bypasses Are Better Vision Transformer Adapters