The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
Open accessAbstract
The dominant paradigm for video-based action segmentation is composed of two steps: first, compute low-level features for each frame using Dense Trajectories or a Convolutional Neural Network to encode local spatiotemporal information, and second, input these features into a classifier such as a Recurrent Neural Network (RNN) that captures high-level temporal relationships. While often effective, this decoupling requires specifying two separate models, each with their own complexities, and prevents capturing more nuanced long-range spatiotemporal relationships. We propose a unified approach, as demonstrated by our Temporal Convolutional Network (TCN), that hierarchically captures relationships at low-, intermediate-, and high-level time-scales. Our model achieves superior or competitive performance using video or sensor data on three public action segmentation datasets and can be trained in a fraction of the time it takes to train an RNN.
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Citations
Cites
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
Action recognition with trajectory-pooled deep-convolutional descriptors
Wang, L., Qiao, Y., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299059 · Cited by 1,194
Learning to recognize objects in egocentric activities
Fathi, A., Ren, X., & Rehg, J. M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995444 · Cited by 557
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
Singh, B., Marks, T. K., Jones, M., Tuzel, O., & Shao, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.216 · Cited by 461 · cited 1×
“It has been shown that in practice LSTM only keeps a memory of about 4 seconds on some video-based action segmentation datasets [15].”
BackgroundCombining embedded accelerometers with computer vision for recognizing food preparation activities
Stein, S., & McKenna, S. J. (2013) · Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing · doi:10.1145/2493432.2493482 · Cited by 448
Segmental Spatiotemporal CNNs for Fine-Grained Action Segmentation
Lea, C., Reiter, A., Vidal, R., & Hager, G. D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_3 · Cited by 246
Self-citationTemporal Action Detection Using a Statistical Language Model
Richard, A., & Gall, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.341 · Cited by 215 · cited 3×
“[14] evaluated their model on the mid-level action granularity of 50 Salads which has 17 action classes.”
MethodResultInfluential
Cited by
Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502
“They have also been used by the instance-sensitive FCN [31] and applied to action segmentation and detection from video frames [32].”
MethodEEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation
Gao, Z., Wang, X., Yang, Y., Mu, C., Cai, Q., Dang, W., & Zuo, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2886414 · Cited by 482
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448
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
Metadata
- Authors
- Lea, C., Vidal, R., Reiter, A., & Hager, G. D.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 47-54
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2510642588
- Cited by
- 924
- References
- 7
- FWCI
- 6.34
- Type
- conference paper
- Language
- en
- Open access
- green
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