Vision Transformers: State of the Art and Research Challenges

• URL: http://arxiv.org/abs/2207.03041v1
• Date: Thu, 7 Jul 2022 02:01:56 GMT
• Title: Vision Transformers: State of the Art and Research Challenges
• Authors: Bo-Kai Ruan, Hong-Han Shuai, Wen-Huang Cheng
• Abstract summary: This paper presents a comprehensive overview of the literature on different architecture designs and training tricks for vision transformers. Our goal is to provide a systematic review with the open research opportunities.
• Score: 26.462994554165697
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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