The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification
Open accessAbstract
In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks [42] with learnable spatio-temporal feature aggregation [6]. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as outperforms other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Learning Spatiotemporal Features with 3D Convolutional Networks
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Cited by
Is Space-Time Attention All You Need for Video Understanding?
Bertasius, G., Wang, H., & Torresani, L. (2021) · arXiv · doi:10.48550/arxiv.2102.05095 · Cited by 1,663
A review of Convolutional-Neural-Network-based action recognition
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Temporal Deformable Residual Networks for Action Segmentation in Videos
Lei, P., & Todorovic, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00705 · Cited by 202
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
Bain, M., Nagrani, A., Varol, G., & Zisserman, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00175 · Cited by 76
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Metadata
- Authors
- Girdhar, R., Ramanan, D., Gupta, A., Sivic, J., & Russell, B.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3165-3174
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2608988379
- Cited by
- 484
- References
- 13
- FWCI
- 20.22
- Type
- conference paper
- Language
- en
- Open access
- green
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- Girdhar, R., Ramanan, D., Gupta, A., Sivic, J., & Russell, B. (2017). ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.337
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