A survey of the Vision Transformers and their CNN-Transformer based Variants Article Swipe
Asifullah Khan
,
Zunaira Rauf
,
Anabia Sohail
,
Abdul Rehman
,
Hifsa Asif
,
Aqsa Asif
,
Umair Farooq
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.09880
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.09880
Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with their ability to focus on global relationships in images, offer large learning capacity. However, they may suffer from limited generalization as they do not tend to model local correlation in images. Recently, in vision transformers hybridization of both the convolution operation and self-attention mechanism has emerged, to exploit both the local and global image representations. These hybrid vision transformers, also referred to as CNN-Transformer architectures, have demonstrated remarkable results in vision applications. Given the rapidly growing number of hybrid vision transformers, it has become necessary to provide a taxonomy and explanation of these hybrid architectures. This survey presents a taxonomy of the recent vision transformer architectures and more specifically that of the hybrid vision transformers. Additionally, the key features of these architectures such as the attention mechanisms, positional embeddings, multi-scale processing, and convolution are also discussed. In contrast to the previous survey papers that are primarily focused on individual vision transformer architectures or CNNs, this survey uniquely emphasizes the emerging trend of hybrid vision transformers. By showcasing the potential of hybrid vision transformers to deliver exceptional performance across a range of computer vision tasks, this survey sheds light on the future directions of this rapidly evolving architecture.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.09880
- https://arxiv.org/pdf/2305.09880
- OA Status
- green
- Cited By
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4377090694
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4377090694Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.09880Digital Object Identifier
- Title
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A survey of the Vision Transformers and their CNN-Transformer based VariantsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-17Full publication date if available
- Authors
-
Asifullah Khan, Zunaira Rauf, Anabia Sohail, Abdul Rehman, Hifsa Asif, Aqsa Asif, Umair FarooqList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.09880Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2305.09880Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2305.09880Direct OA link when available
- Concepts
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Transformer, Computer science, Convolutional neural network, Architecture, Artificial intelligence, Machine vision, Computer vision, Engineering, Electrical engineering, Voltage, Art, Visual artsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 9, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.exceptional | 199 |
| abstract_inverted_index.explanation | 114 |
| abstract_inverted_index.mechanisms, | 150 |
| abstract_inverted_index.multi-scale | 153 |
| abstract_inverted_index.performance | 200 |
| abstract_inverted_index.processing, | 154 |
| abstract_inverted_index.transformer | 128, 174 |
| abstract_inverted_index.demonstrated | 90 |
| abstract_inverted_index.specifically | 132 |
| abstract_inverted_index.transformers | 1, 58, 196 |
| abstract_inverted_index.Additionally, | 139 |
| abstract_inverted_index.applications. | 20, 95 |
| abstract_inverted_index.architecture. | 220 |
| abstract_inverted_index.architectures | 129, 145, 175 |
| abstract_inverted_index.convolutional | 10 |
| abstract_inverted_index.hybridization | 59 |
| abstract_inverted_index.relationships | 30 |
| abstract_inverted_index.transformers, | 22, 82, 104 |
| abstract_inverted_index.transformers. | 138, 188 |
| abstract_inverted_index.architectures, | 88 |
| abstract_inverted_index.architectures. | 118 |
| abstract_inverted_index.generalization | 43 |
| abstract_inverted_index.self-attention | 66 |
| abstract_inverted_index.CNN-Transformer | 87 |
| abstract_inverted_index.representations. | 78 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.87731154 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |