The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
Open accessAbstract
We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster RCNN object detection framework. TAL-Net addresses three key shortcomings of existing approaches: (1) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations; (2) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields; and (3) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important. We achieve state-of-the-art performance for both action proposal and localization on THUMOS'14 detection benchmark and competitive performance on ActivityNet challenge.
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Cites
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Cited by
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
“detection, several approaches follow a two stage pipeline. 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]. Other approaches combine the proposal generation and classification in a single-stage arch…”
BackgroundNeimark, D., Bar, O., Zohar, M., & Asselmann, D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00355 · Cited by 551
Central Similarity Quantization for Efficient Image and Video Retrieval
Yuan, L., Wang, T., Zhang, X., Tay, F. E., Jie, Z., Liu, W., & Feng, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00315 · Cited by 413
Gaussian Temporal Awareness Networks for Action Localization
Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., & Mei, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00043 · Cited by 380
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].”
BackgroundWang, J., Jiao, J., Bao, L., He, S., Liu, Y., & Liu, W. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00413 · Cited by 210
ASFormer: Transformer for Action Segmentation
Yi, F., Wen, H., & Jiang, T. (2021) · Proceedings of the British Machine Vision Conference 2021 · doi:10.5244/c.35.49 · Cited by 170
“Two-stage methods [3, 48] adopt the Faster-RCNN [31] architecture, including proposal generation and proposal classification.”
MethodYurtsever, E., Liu, Y., Lambert, J., Miyajima, C., Takeuchi, E., Takeda, K., & Hansen, J. H. L. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917362 · Cited by 40
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M., & Gall, J. (2020) · arXiv · Cited by 350
“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].”
Background
Metadata
- Authors
- Chao, Y.-W., Vijayanarasimhan, S., Seybold, B., Ross, D. A., Deng, J., & Sukthankar, R.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1130-1139
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962876901
- Cited by
- 725
- References
- 17
- FWCI
- 26.69
- Type
- conference paper
- Language
- en
- Open access
- green
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