The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection
Open accessAbstract
General human action recognition requires understanding of various visual cues. In this paper, we propose a network architecture that computes and integrates the most important visual cues for action recognition: pose, motion, and the raw images. For the integration, we introduce a Markov chain model which adds cues successively. The resulting approach is efficient and applicable to action classification as well as to spatial and temporal action localization. The two contributions clearly improve the performance over respective baselines. The overall approach achieves state-of-the-art action classification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover, it yields state-of-the-art spatio-temporal action localization results on UCF101 and J-HMDB.
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Self-citationGradient-based learning applied to document recognition
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Learning Spatiotemporal Features with 3D Convolutional Networks
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Video Google: a text retrieval approach to object matching in videos
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Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
Flownet: Learning optical flow with convolutional networks
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Self-citationHmdb: a large video database for human motion recognition
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AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions
Gu, C., Sun, C., Ross, D. A., Vondrick, C., Pantofaru, C., Li, Y., Vijayanarasimhan, S., Toderici, G., Ricco, S., Sukthankar, R., et al. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00633 · Cited by 986
Human 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 601
Boulahia, S. Y., Amamra, A., Madi, M. R., & Daikh, S. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01249-8 · Cited by 236
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition
Zhou, B., Wang, P., Wan, J., Liang, Y., Wang, F., Zhang, D., Lei, Z., Li, H., & Jin, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01952 · Cited by 53
A Unified Multimodal De- and Re-Coupling Framework for RGB-D Motion Recognition
Zhou, B., Wang, P., Wan, J., Liang, Y., & Wang, F. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3274783 · Cited by 38
Metadata
- Authors
- Zolfaghari, M., Oliveira, G. L., Sedaghat, N., & Brox, T.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 2923-2932
- OpenAlex
- W2605111198
- Cited by
- 235
- References
- 14
- FWCI
- 9.55
- Type
- conference paper
- Language
- en
- Open access
- green
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