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]
In the citing paper
“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]
Citations
Cites
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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
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Flagged first, then by citations
5 of the 92 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- 10.1088/0954-898X/14/3/302 · DOI
- adelson, 1984, Pyramid methods in image processing, RCA Engineer, 29, 33
- bouguet, 2001, Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm, Intel Corporation, 5, 4
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A review of convolutional neural networks in computer vision
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. D. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10721-6 · Cited by 1,477
Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072
ImageBind One Embedding Space to Bind Them All
Girdhar, R., El-Nouby, A., Liu, Z., Singh, M., Alwala, K. V., Joulin, A., & Misra, I. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01457 · Cited by 863
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966
“[134] Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, and Cordelia Schmid.”
BackgroundHuman Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 719
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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