all AI news
iVPT: Improving Task-relevant Information Sharing in Visual Prompt Tuning by Cross-layer Dynamic Connection
April 9, 2024, 4:47 a.m. | Nan Zhou, Jiaxin Chen, Di Huang
cs.CV updates on arXiv.org arxiv.org
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
More from arxiv.org / cs.CV updates on arXiv.org
Compact 3D Scene Representation via Self-Organizing Gaussian Grids
2 days, 20 hours ago |
arxiv.org
Fingerprint Matching with Localized Deep Representation
2 days, 20 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne