Cited by 313Human Pose and Action RecognitionMultimodal Machine Learning ApplicationsAdvanced Neural Network Applications

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Multiview Transformers for Video Recognition

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Yan, Shen, Xiong, Xuehan, Arnab, Anurag, Lu, Zhichao, Zhang, Mi, Sun, Chen, Schmid, Cordelia·2022·2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr52688.2022.00333

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Abstract

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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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)
ORCID: Arnab, Anurag
Lu, Zhichao
Zhang, Mi
Sun, Chen
Schmid, Cordelia
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)
DOI: 10.1109/cvpr52688.2022.00333
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
Licence: cc-by
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Multiview Transformers for Video Recognition

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