The DOI matches its Crossref record; 87 of the 92 references it cites were found in a registry or an index.
Multiview Transformers for Video Recognition
Open accessAbstract
Video understanding requires reasoning at multiple spatiotemporal resolutions – from short fine-grained motions to events taking place over longer durations. Although transformer architectures have recently advanced the state-of-the-art, they have not explicitly modelled different spatiotemporal resolutions. To this end, we present Multiview Transformers for Video Recognition (MTV). Our model consists of separate encoders to represent different views of the input video with lateral connections to fuse information across views. We present thorough ablation studies of our model and show that MTV consistently performs better than single-view counterparts in terms of accuracy and computational cost across a range of model sizes. Furthermore, we achieve state-of-the-art results on six standard datasets, and improve even further with large-scale pretraining. Code and checkpoints are available at: https://github.com/google-research/scenic.
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“One solution is using knowledge distillation, e.g., distilling from multimodal to uni-modal attention in Transformers [275], distilling from multiple uni-modal Transformer teachers to a shared Transformer encoder [276].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [276]
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“Distilling multimodal knowledge into small models that are adaptable to uni-modal data and tasks is a successful practice [275], [276].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [276]
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5 of the 92 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
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Wollam, A., Ashley, K., Shugaev, M., Arend, O., Semenov, I., Dashtestani, H., Ravi, S. S., & Jacobs, N. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00815
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Explicitly modeling trajectories and correlations for video analysis
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Multi-View Optimal Transport for Multi-View Crowd Localization and Tracking
Zhang, K., Zhang, Q., Chen, J., Chan, A. B., & Huang, H. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-03093-7
Metadata
- Authors
- Yan, S., Xiong, X., Arnab, A., Lu, Z., Zhang, M., Sun, C., & Schmid, C.
- Affiliations
- Michigan State University
- Google (United States) (US)
- Michigan State University (US)
- Brown University (US)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 3323-3333
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312658081
- Cited by
- 312
- References
- 92
- Citations by year
- 2026: 26 · 2025: 63 · 2024: 115 · 2023: 91 · 2022: 17
- FWCI
- 25.93
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- 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, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- video recognition, spatiotemporal resolution, view fusion, lateral connections, video encoder, pre-training, computational efficiency
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