The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
Abstract
In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions. Since the choices of selecting representative frames are multitudinous for each video, we model the frame selection as a progressive process through deep reinforcement learning, during which we progressively adjust the chosen frames by taking two important factors into account: (1) the quality of the selected frames and (2) the relationship between the selected frames to the whole video. Moreover, considering the topology of human body inherently lies in a graph-based structure, where the vertices and edges represent the hinged joints and rigid bones respectively, we employ the graph-based convolutional neural network to capture the dependency between the joints for action recognition. Our approach achieves very competitive performance on three widely used benchmarks.
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Cites
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Reinforcement Learning: An Introduction
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Two-Stream Convolutional Networks for Action Recognition in Videos
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Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,613
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995316 · Cited by 3,491
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Convolutional Sequence to Sequence Learning
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“On the other hand, the CNN-based model, which captures the relationship of neighboring frames at lower layers and long-term dependency at higher layers [18], is more effective and obtains promising performance recently [15, 16].”
Background
Cited by
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01230 · Cited by 2,071
Dynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
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
Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching
Peng, W., Hong, X., Chen, H., & Zhao, G. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5652 · Cited by 361
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., & Tang, H. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413941 · Cited by 341
Metadata
- Authors
- Tang, Y., Tian, Y., Lu, J., Li, P., & Zhou, J.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5323-5332
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2798644314
- Cited by
- 444
- References
- 51
- FWCI
- 21.08
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tang, Y., Tian, Y., Lu, J., Li, P., & Zhou, J. (2018). Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00558
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