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ViTAR: Vision Transformer with Any Resolution
March 28, 2024, 4:45 a.m. | Qihang Fan, Quanzeng You, Xiaotian Han, Yongfei Liu, Yunzhe Tao, Huaibo Huang, Ran He, Hongxia Yang
cs.CV updates on arXiv.org arxiv.org
Abstract: his paper tackles a significant challenge faced by Vision Transformers (ViTs): their constrained scalability across different image resolutions. Typically, ViTs experience a performance decline when processing resolutions different from those seen during training. Our work introduces two key innovations to address this issue. Firstly, we propose a novel module for dynamic resolution adjustment, designed with a single Transformer block, specifically to achieve highly efficient incremental token integration. Secondly, we introduce fuzzy positional encoding in the …
abstract arxiv challenge cs.cv experience image innovations issue key novel paper performance processing resolution scalability training transformer transformers type vision vision transformers work
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