April 9, 2024, 4:47 a.m. | Nan Zhou, Jiaxin Chen, Di Huang

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.05207v1 Announce Type: new
Abstract: Recent progress has shown great potential of visual prompt tuning (VPT) when adapting pre-trained vision transformers to various downstream tasks. However, most existing solutions independently optimize prompts at each layer, thereby neglecting the usage of task-relevant information encoded in prompt tokens across layers. Additionally, existing prompt structures are prone to interference from task-irrelevant noise in input images, which can do harm to the sharing of task-relevant information. In this paper, we propose a novel VPT …

abstract arxiv cs.cv dynamic however improving information layer progress prompt prompts prompt tuning solutions tasks tokens transformers type usage vision vision transformers visual

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