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This paper presents a new Vision Transformer (ViT) architecture Multi-ScaleVision Longformer, which significantly enhances the ViT of\cite{dosovitskiy2020image} for encoding high-resolution images using twotechniques. The first is the multi-scale model structure, which provides imageencodings at multiple scales with manageable computational cost. The second isthe attention mechanism of vision Longformer, which is a variant of Longformer\cite{beltagy2020longformer}, originally developed for natural languageprocessing, and achieves a linear complexity w.r.t. the number of input tokens.A comprehensive empirical study shows that the new ViT significantlyoutperforms several strong baselines, including the existing ViT models andtheir ResNet counterparts, and the Pyramid Vision Transformer from a concurrentwork \cite{wang2021pyramid}, on a range of vision tasks, including imageclassification, object detection, and segmentation. The models and source codeare released at \url{https://github.com/microsoft/vision-longformer}.

Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding