The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
Open accessAbstract
The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. Inspired by this, in this paper, we study how to learn multi-scale feature representations in transformer models for image classification. To this end, we propose a dual-branch transformer to com-bine image patches (i.e., tokens in a transformer) of different sizes to produce stronger image features. Our approach processes small-patch and large-patch tokens with two separate branches of different computational complexity and these tokens are then fused purely by attention multiple times to complement each other. Furthermore, to reduce computation, we develop a simple yet effective token fusion module based on cross attention, which uses a single token for each branch as a query to exchange information with other branches. Our proposed cross-attention only requires linear time for both computational and memory complexity instead of quadratic time otherwise. Extensive experiments demonstrate that our approach performs better than or on par with several concurrent works on vision transformer, in addition to efficient CNN models. For example, on the ImageNet1K dataset, with some architectural changes, our approach outperforms the recent DeiT by a large margin of 2% with a small to moderate increase in FLOPs and model parameters. Our source codes and models are available at https://github.com/IBM/CrossViT.
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Cites
Deep Residual Learning for Image Recognition
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
ImageNet: A large-scale hierarchical image database
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
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Cited by
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
Transformers in Vision: A Survey
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595
Pvt v2: Improved baselines with pyramid vision transformer
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0274-8 · Cited by 2,439
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
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SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
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Metadata
- Authors
- Chen, C.-F. R., Fan, Q., & Panda, R.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 347-356
- OpenAlex
- W3151130473
- Cited by
- 2,074
- References
- 53
- FWCI
- 105.34
- Type
- conference paper
- Language
- en
- Open access
- green
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- Chen, C.-F. R., Fan, Q., & Panda, R. (2021). CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.00041
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