CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task

• URL: http://arxiv.org/abs/2408.14961v1
• Date: Tue, 27 Aug 2024 11:07:19 GMT
• Title: CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task
• Authors: Lingyun Huang, Jianxu Mao, Yaonan Wang, Junfei Yi, Ziming Tao,
• Abstract summary: Cross Visual Prompt Tuning is a new type of visual fine-tuning. CVPT calculates cross-attention between the prompt tokens and the embedded tokens, which allows us to compute the semantic relationship between them. CVPT significantly improves VPT's performance and efficiency in visual tasks.
• Score: 15.642102189777072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the rapid expansion of model sizes has led to large-scale pre-trained models demonstrating remarkable capabilities. Consequently, there has been a trend towards increasing the scale of models. However, this trend introduces significant challenges, including substantial computational costs of training and transfer to downstream tasks. To address these issues, Parameter-Efficient Fine-Tuning (PEFT) methods have been introduced. These methods optimize large-scale pre-trained models for specific tasks by fine-tuning a select group of parameters. Among these PEFT methods, adapter-based and prompt-based methods are the primary techniques. Specifically, in the field of visual fine-tuning, adapters gain prominence over prompts because of the latter's relatively weaker performance and efficiency. Under the circumstances, we refine the widely-used Visual Prompt Tuning (VPT) method, proposing Cross Visual Prompt Tuning (CVPT). CVPT calculates cross-attention between the prompt tokens and the embedded tokens, which allows us to compute the semantic relationship between them and conduct the fine-tuning of models exactly to adapt visual tasks better. Furthermore, we introduce the weight-sharing mechanism to initialize the parameters of cross-attention, which avoids massive learnable parameters from cross-attention and enhances the representative capability of cross-attention. We conduct comprehensive testing across 25 datasets and the result indicates that CVPT significantly improves VPT's performance and efficiency in visual tasks. For example, on the VTAB-1K benchmark, CVPT outperforms VPT over 4% in average accuracy, rivaling the advanced adapter-based methods in performance and efficiency. Our experiments confirm that prompt-based methods can achieve exceptional results in visual fine-tuning.

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