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

Code

• github.com/microsoft/vision-longformer
  OfficialIn paperpytorch★ 249
• github.com/microsoft/esvit
  pytorch★ 413
• github.com/microsoft/VisionLongformerForObjectDetection
  pytorch★ 34

Abstract

This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer, which significantly enhances the ViT of dosovitskiy2020image for encoding high-resolution images using two techniques. The first is the multi-scale model structure, which provides image encodings at multiple scales with manageable computational cost. The second is the attention mechanism of vision Longformer, which is a variant of Longformer beltagy2020longformer, originally developed for natural language processing, and achieves a linear complexity w.r.t. the number of input tokens. A comprehensive empirical study shows that the new ViT significantly outperforms several strong baselines, including the existing ViT models and their ResNet counterparts, and the Pyramid Vision Transformer from a concurrent work wang2021pyramid, on a range of vision tasks, including image classification, object detection, and segmentation. The models and source code are released at https://github.com/microsoft/vision-longformer.

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Reproductions