The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning Activity Progression in LSTMs for Activity Detection and Early Detection
Open accessAbstract
In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection tasks. Conventionally, when training a Recurrent Neural Network, specifically a Long Short Term Memory (LSTM) model, the training loss only considers classification error. However, we argue that the detection score of the correct activity category, or the detection score margin between the correct and incorrect categories, should be monotonically non-decreasing as the model observes more of the activity. We design novel ranking losses that directly penalize the model on violation of such monotonicities, which are used together with classification loss in training of LSTM models. Evaluation on ActivityNet shows significant benefits of the proposed ranking losses in both activity detection and early detection tasks.
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Cites
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
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
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
ActivityNet: A large-scale video benchmark for human activity understanding
Heilbron, F. C., Escorcia, V., Ghanem, B., & Niebles, J. C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298698 · Cited by 2,578
Cited by
Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930
TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
Gao, J., Yang, Z., Sun, C., Chen, K., & Nevatia, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.392 · Cited by 498
Furnari, A., & Farinella, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00635 · Cited by 182
“While previous approaches attempted to address these two sub-tasks jointly [2, 8, 39, 58], our method disentangles them using two separate LSTMs.”
BackgroundMethodInfluentialMulti-Modality Multi-Task Recurrent Neural Network for Online Action Detection
Liu, J., Li, Y., Song, S., Xing, J., Lan, C., & Zeng, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2799968 · Cited by 62
Ezzat, M. A., Abd El Ghany, M. A., Almotairi, S., & Salem, M. A.-M. (2021) · Sensors · doi:10.3390/s21093222 · Cited by 39
Metadata
- Authors
- Ma, S., Sigal, L., & Sclaroff, S.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1942-1950
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2472970127
- Cited by
- 377
- References
- 4
- FWCI
- 21.85
- Type
- conference paper
- Language
- en
- Open access
- green
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