A Survey of Visual Transformers

• URL: http://arxiv.org/abs/2111.06091v2
• Date: Sat, 13 Nov 2021 08:53:19 GMT
• Title: A Survey of Visual Transformers
• Authors: Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao Shi, Jianping Fan, Zhiqiang He
• Abstract summary: Transformer, an attention-based encoder-decoder architecture, has revolutionized the field of natural language processing. Some pioneering works have recently been done on adapting Transformer architectures to Computer Vision (CV) fields. We have provided a comprehensive review of over one hundred different visual Transformers for three fundamental CV tasks.
• Score: 30.082304742571598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer, an attention-based encoder-decoder architecture, has revolutionized the field of natural language processing. Inspired by this significant achievement, some pioneering works have recently been done on adapting Transformerliked architectures to Computer Vision (CV) fields, which have demonstrated their effectiveness on various CV tasks. Relying on competitive modeling capability, visual Transformers have achieved impressive performance on multiple benchmarks such as ImageNet, COCO, and ADE20k as compared with modern Convolution Neural Networks (CNN). In this paper, we have provided a comprehensive review of over one hundred different visual Transformers for three fundamental CV tasks (classification, detection, and segmentation), where a taxonomy is proposed to organize these methods according to their motivations, structures, and usage scenarios. Because of the differences in training settings and oriented tasks, we have also evaluated these methods on different configurations for easy and intuitive comparison instead of only various benchmarks. Furthermore, we have revealed a series of essential but unexploited aspects that may empower Transformer to stand out from numerous architectures, e.g., slack high-level semantic embeddings to bridge the gap between visual and sequential Transformers. Finally, three promising future research directions are suggested for further investment.

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