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Vision Transformers: State of the Art and Research Challenges

2022-07-07 02:01:56
Bo-Kai Ruan, Hong-Han Shuai, Wen-Huang Cheng

Abstract

Transformers have achieved great success in natural language processing. Due to the powerful capability of self-attention mechanism in transformers, researchers develop the vision transformers for a variety of computer vision tasks, such as image recognition, object detection, image segmentation, pose estimation, and 3D reconstruction. This paper presents a comprehensive overview of the literature on different architecture designs and training tricks (including self-supervised learning) for vision transformers. Our goal is to provide a systematic review with the open research opportunities.

Abstract (translated)

URL

https://arxiv.org/abs/2207.03041

PDF

https://arxiv.org/pdf/2207.03041.pdf


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