The DOI matches its Crossref record; 177 of the 177 references it cites were found in a registry or an index.
A Survey of Visual Transformers
Open accessAbstract
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some pioneering works have recently been done on employing Transformer-liked architectures in the computer vision (CV) field, which have demonstrated their effectiveness on three fundamental CV tasks (classification, detection, and segmentation) as well as multiple sensory data stream (images, point clouds, and vision-language data). Because of their competitive modeling capabilities, the visual Transformers have achieved impressive performance improvements over multiple benchmarks as compared with modern convolution neural networks (CNNs). In this survey, we have reviewed over 100 of different visual Transformers comprehensively according to three fundamental CV tasks and different data stream types, where taxonomy is proposed to organize the representative methods according to their motivations, structures, and application scenarios. Because of their differences on training settings and dedicated vision tasks, we have also evaluated and compared all these existing visual Transformers under different configurations. Furthermore, we have revealed a series of essential but unexploited aspects that may empower such visual Transformers to stand out from numerous architectures, e.g., slack high-level semantic embeddings to bridge the gap between the visual Transformers and the sequential ones. Finally, two promising research directions are suggested for future investment. We will continue to update the latest articles and their released source codes at https://github.com/liuyang-ict/awesome-visual-transformers.
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A survey of the vision transformers and their CNN-transformer based variants
Khan, A., Rauf, Z., Sohail, A., Khan, A. R., Asif, H., Asif, A., & Farooq, U. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10595-0 · Cited by 451
Semantic segmentation using Vision Transformers: A survey
Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., & Herath, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106669 · Cited by 288
Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods
Cao, Y., Zhao, H., Cheng, Y., Shu, T., Chen, Y., Liu, G., Liang, G., Zhao, J., Yan, J., & Li, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3497992 · Cited by 174
“The rapid advancements in LLMs present a viable solution to these challenges, thanks to their potent natural language understanding and reasoning abilities, coupled with recent progress in incorporating visual data processing [79].”
Transformer for object detection: Review and benchmark
Li, Y., Miao, N., Ma, L., Shuang, F., & Huang, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107021 · Cited by 135
Metadata
- Authors
- Liu, Y., Zhang, Y., Wang, Y., Hou, F., Yuan, J., Tian, J., Zhang, Y., Shi, Z., Fan, J., & He, Z.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2024-06
- Volume
- 35
- Issue
- 6
- Pages
- 7478-7498
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W4226512186
- Cited by
- 549
- References
- 177
- FWCI
- 47.46
- Type
- journal article
- Language
- en
- Open access
- green
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