ViTAR: Vision Transformer with Any Resolution
Authors: name: Qihang Fan, name: Quanzeng You, name: Xiaotian Han, name: Yongfei Liu, name: Yunzhe Tao, name: Huaibo Huang, name: Ran He, name: Hongxia Yang

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 Vision Transformer to
provide consistent positional awareness across multiple resolutions, thereby
preventing overfitting to any single training resolution. Our resulting model,
ViTAR (Vision Transformer with Any Resolution), demonstrates impressive
adaptability, achieving 83.3\% top-1 accuracy at a 1120x1120 resolution and
80.4\% accuracy at a 4032x4032 resolution, all while reducing computational
costs. ViTAR also shows strong performance in downstream tasks such as instance
and semantic segmentation and can easily combined with self-supervised learning
techniques like Masked AutoEncoder. Our work provides a cost-effective solution
for enhancing the resolution scalability of ViTs, paving the way for more
versatile and efficient high-resolution image processing.