Computer Science > Computer Vision and Pattern Recognition
[Submitted on 20 Mar 2024 (v1), last revised 14 Jun 2024 (this version, v2)]
Title:AdaViPro: Region-based Adaptive Visual Prompt for Large-Scale Models Adapting
View PDF HTML (experimental)Abstract:Recently, prompt-based methods have emerged as a new alternative `parameter-efficient fine-tuning' paradigm, which only fine-tunes a small number of additional parameters while keeping the original model frozen. However, despite achieving notable results, existing prompt methods mainly focus on `what to add', while overlooking the equally important aspect of `where to add', typically relying on the manually crafted placement. To this end, we propose a region-based Adaptive Visual Prompt, named AdaViPro, which integrates the `where to add' optimization of the prompt into the learning process. Specifically, we reconceptualize the `where to add' optimization as a problem of regional decision-making. During inference, AdaViPro generates a regionalized mask map for the whole image, which is composed of 0 and 1, to designate whether to apply or discard the prompt in each specific area. Therefore, we employ Gumbel-Softmax sampling to enable AdaViPro's end-to-end learning through standard back-propagation. Extensive experiments demonstrate that our AdaViPro yields new efficiency and accuracy trade-offs for adapting pre-trained models.
Submission history
From: Mengyu Yang [view email][v1] Wed, 20 Mar 2024 03:47:53 UTC (3,837 KB)
[v2] Fri, 14 Jun 2024 07:00:30 UTC (3,837 KB)
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