The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Cascaded Boundary Regression for Temporal Action Detection
Open accessAbstract
Temporal action detection in long videos is an important problem. State-of-the-art methods address this problem by applying action classifiers on sliding windows. Although sliding windows may contain an identifiable portion of the actions, they may not necessarily cover the entire action instance, which would lead to inferior performance. We adapt a two-stage temporal action detection pipeline with Cascaded Boundary Regression (CBR) model. Class-agnostic proposals and specific actions are detected respectively in the first and the second stage. CBR uses temporal coordinate regression to refine the temporal boundaries of the sliding windows. The salient aspect of the refinement process is that, inside each stage, the temporal boundaries are adjusted in a cascaded way by feeding the refined windows back to the system for further boundary refinement. We test CBR on THUMOS-14 and TVSeries, and achieve state-of-the-art performance on both datasets. The performance gain is especially remarkable under high IoU thresholds, e.g. map@tIoU=0.5 on THUMOS-14 is improved from 19.0% to 31.0%.
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Citations
Cites
End-to-End Learning of Action Detection from Frame Glimpses in Videos
Yeung, S., Russakovsky, O., Mori, G., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.293 · Cited by 585
Cited by
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Lin, T., Zhao, X., Su, H., Wang, C., & Yang, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01225-0_1 · Cited by 770
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
Farha, Y. A., & Gall, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00369 · Cited by 673
“The first stage is to generate proposals, and then classify and refine the boundaries of these proposals in the second stage [32], [33], [34], [35], [36], [37], [38], [39], [40].”
BackgroundBMN: Boundary-Matching Network for Temporal Action Proposal Generation
Lin, T., Liu, X., Li, X., Ding, E., & Wen, S. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00399 · Cited by 694
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M., & Gall, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3021756 · Cited by 349
“The first stage is to generate proposals, and then classify and refine the boundaries of these proposals in the second stage [32], [33], [34], [35], [36], [37], [38], [39], [40].”
BackgroundAlleviating Over-segmentation Errors by Detecting Action Boundaries
Ishikawa, Y., Kasai, S., Aoki, Y., & Kataoka, H. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00237 · Cited by 171
Metadata
- Authors
- Gao, J., Yang, Z., & Nevatia, R.
- Venue
- Procedings of the British Machine Vision Conference 2017
- Publisher
- British Machine Vision Association
- Published
- 2017
- OpenAlex
- W2964107628
- Cited by
- 232
- References
- 1
- FWCI
- 10.27
- Type
- conference paper
- Language
- en
- Open access
- green
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