Exploring Self-Attention for Image Recognition
Open accessAbstract
Recent work has shown that self-attention can serve as a basic building block for image recognition models. We explore variations of self-attention and assess their effectiveness for image recognition. We consider two forms of self-attention. One is pairwise self-attention, which generalizes standard dot-product attention and is fundamentally a set operator. The other is patchwise self-attention, which is strictly more powerful than convolution. Our pairwise self-attention networks match or outperform their convolutional counterparts, and the patchwise models substantially outperform the convolutional baselines. We also conduct experiments that probe the robustness of learned representations and conclude that self-attention networks may have significant benefits in terms of robustness and generalization.
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Training soiling jointly using asynchronous backpropagation achieved the same accuracy as using the frozen encoder.
“We also trained soiling jointly using asynchronous backpropagation [22], but it achieved the same accuracy as using the frozen encoder.”
From OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving (2021) · cites this paper as [22]
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Flagged first, then by citations
Cited by
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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“Our Swin-T architecture brings consistent +3.4∼4.2 box AP gains over ResNet-50, with slightly larger model size, FLOPs and latency.”
MethodInfluentialAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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“eighborhoods for each query pixel instead of globally. Such local multi-head dot-product self attention blocks can completely replace convolutions (Ramachandran et al., 2019; Cordonnier et al., 2020; Zhao et al., 2020). Alternatively, works such as Sparse Transformers (Child et al., 2019) employ scalable approximation…”
BackgroundMethodEmerging Properties in Self-Supervised Vision Transformers
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“Transformers [70] have recently emerged as an alternative to convolutional neural networks (convnets) for visual recognition [19, 69, 83].”
BackgroundPyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
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“SAN [59] explores two types of self-attention modules.”
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“PSANet formulates information aggregation as: zi = ∑ j∈Ω(i) F (xi, xj ,∆ij)xj (55) where ∆ij indicates the positional relationship between i and j.”
MethodInfluentialVision Transformers for Dense Prediction
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“There have been several works that adapt attention mechanisms to image analysis [3, 30, 31, 43, 54].”
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Multiscale vision transformers
Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., & Feichtenhofer, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00675 · Cited by 1,407 · see citing works
Understanding Robustness of Transformers for Image Classification
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“networks [4, 9, 48, 27]Ðand other attention-based architectures [53, 52, 40, 32, 54]Ðin computer vision.”
BackgroundPerceiver: General Perception with Iterative Attention
Jaegle, A., Gimeno, F., Brock, A., Zisserman, A., Vinyals, O., & Carreira, J. (2021) · arXiv · doi:10.48550/arxiv.2103.03206 · Cited by 414 · see citing works
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UniT: Multimodal Multitask Learning with a Unified Transformer
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OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving
Ravi Kumar, V., Yogamani, S., Rashed, H., Sitsu, G., Witt, C., Leang, I., Milz, S., & Mader, P. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3062324 · Cited by 117 · see citing works
“3) Linking Self-Attention and Semantic features to 2D detection: To leverage the multi-task learning setup, at first, we extract the Self-Attention Network (SAN) [28] encoder features and feed it as an input signal to the Eq.”
MethodDeep learning and the global workspace theory
VanRullen, R., & Kanai, R. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.04.005 · Cited by 128 · see citing works
VisQA: X-raying Vision and Language Reasoning in Transformers
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“Originally developed for NLP tasks, these models have been extensively applied to VQA [42, 54, 62] and recently even on pixel-level in pure image based problems [17, 18, 22, 59, 65].”
MethodVision Transformers: From Semantic Segmentation to Dense Prediction
Zhang, L., Lu, J., Zheng, S., Zhao, X., Zhu, X., Fu, Y., Xiang, T., Feng, J., & Torr, P. H. S. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02173-w · Cited by 23 · see citing works
“SAN [44] explores two types of self-attention modules.”
Influential
- Authors
- Zhao, H., Jia, J., & Koltun, V.
- Affiliations
- Chinese University of Hong Kong (HK)
- Intel (United States) (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 10073-10082
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034885317
- Semantic Scholar
- 54c7445f319823c7dcc948c830e75e2fa7460b33
- Cited by
- 1,002
- References
- 47
- Citations by year
- 2026: 50 · 2025: 142 · 2024: 191 · 2023: 232 · 2022: 209 · 2021: 157 · 2020: 21
- FWCI
- 47.68
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- self-attention, image recognition, dot-product attention, set operators, convolutional neural networks, representation robustness, generalization
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