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

• URL: http://arxiv.org/abs/2103.15358v1
• Date: Mon, 29 Mar 2021 06:23:20 GMT
• Title: Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
• Authors: Pengchuan Zhang, Xiyang Dai, Jianwei Yang, Bin Xiao, Lu Yuan, Lei Zhang, Jianfeng Gao
• Abstract summary: This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer. It significantly enhances the ViT of citedosovitskiy 2020image for encoding high-resolution images using two techniques.
• Score: 81.07894629034767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer, which significantly enhances the ViT of \cite{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 \cite{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 \cite{wang2021pyramid}, on a range of vision tasks, including image classification, object detection, and segmentation. The models and source code used in this study will be released to public soon.

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