The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
CAT: Cross Attention in Vision Transformer
Open accessAbstract
Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for replacing word tokens with image patches for Transformer after the tokenization of the image is vast(e.g., ViT), which bottlenecks model training and inference. In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps to capture global information. Both operations have less computation than standard self-attention in Transformer. Based on that, we build a hierarchical network called Cross Attention Transformer(CAT) for other vision tasks. Our model achieves 82.8% on ImageNet-1K, and improves the performance of other methods on COCO and ADE20K, illustrating that our network has the potential to serve as general backbones. The code and models are avalible at https://github.com/linhezheng19/CAT.
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Cites
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Going deeper with convolutions
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Cited by
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
Zhang, P., Ma, J., & Chen, T. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3576488 · Cited by 13
Metadata
- Authors
- Lin, H., Cheng, X., Wu, X., & Shen, D.
- Venue
- 2022 IEEE International Conference on Multimedia and Expo (ICME)
- Publisher
- IEEE
- Published
- 2022-07-18
- Pages
- 1-6
- OpenAlex
- W3168825659
- Cited by
- 269
- References
- 89
- FWCI
- 20.57
- Type
- conference paper
- Language
- en
- Open access
- green
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- Lin, H., Cheng, X., Wu, X., & Shen, D. (2022). CAT: Cross Attention in Vision Transformer. 2022 IEEE International Conference on Multimedia and Expo (ICME). https://doi.org/10.1109/icme52920.2022.9859720
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