Visual Variational Autoencoder Prompt Tuning

• URL: http://arxiv.org/abs/2503.17650v1
• Date: Sat, 22 Mar 2025 04:59:51 GMT
• Title: Visual Variational Autoencoder Prompt Tuning
• Authors: Xi Xiao, Yunbei Zhang, Yanshuh Li, Xingjian Li, Tianyang Wang, Jihun Hamm, Xiao Wang, Min Xu,
• Abstract summary: This paper introduces V$2$APT (Visual Variational Autoencoder Prompt Tuning), a novel framework that generates dynamic, input-dependent prompts.<n>Experiments on FGVC, HTA, and VTAB-1k benchmarks demonstrate that our approach consistently outperforms state-of-the-art PEFT methods.
• Score: 20.387933505896388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large vision transformers to downstream tasks without the prohibitive computational costs of full fine-tuning. While existing visual prompt tuning (VPT) methods have made significant strides, they predominantly rely on static, domain-specific prompts that fail to capture the rich visual diversity within individual instances. This paper introduces V$^2$APT (Visual Variational Autoencoder Prompt Tuning), a novel framework that generates dynamic, input-dependent prompts using a variational autoencoder architecture. By learning a latent representation of image-specific features and decoding them into customized prompts, V$^2$APT adapts to the unique visual characteristics of each input. Extensive experiments on FGVC, HTA, and VTAB-1k benchmarks demonstrate that our approach consistently outperforms state-of-the-art PEFT methods. Notably, V$^2$APT achieves +3.2\% improvement over VPT-Deep on HTA, with an average performance gain of +2.0\% across all three datasets.

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