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As the size of transformer-based models continues to grow, fine-tuning theselarge-scale pretrained vision models for new tasks has become increasinglyparameter-intensive. Parameter-efficient learning has been developed to reducethe number of tunable parameters during fine-tuning. Although these methodsshow promising results, there is still a significant performance gap comparedto full fine-tuning. To address this challenge, we propose an Effective andEfficient Visual Prompt Tuning (E^2VPT) approach for large-scaletransformer-based model adaptation. Specifically, we introduce a set oflearnable key-value prompts and visual prompts into self-attention and inputlayers, respectively, to improve the effectiveness of model fine-tuning.Moreover, we design a prompt pruning procedure to systematically prune lowimportance prompts while preserving model performance, which largely enhancesthe model's efficiency. Empirical results demonstrate that our approachoutperforms several state-of-the-art baselines on two benchmarks, withconsiderably low parameter usage (e.g., 0.32% of model parameters on VTAB-1k).Our code is available at https://github.com/ChengHan111/E2VPT.

E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning