The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Sequential Deep Learning for Human Action Recognition
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
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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
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228
A Discriminative Feature Learning Approach for Deep Face Recognition
Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077
Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks
Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178838 · Cited by 1,678
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599174 · Cited by 1,547
Convolutional neural network: a review of models, methodologies and applications to object detection
Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119
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
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Yao, G., Lei, T., & Zhong, J. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.05.018 · Cited by 437
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Pigou, L., van den Oord, A., Dieleman, S., Van Herreweghe, M., & Dambre, J. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-016-0957-7 · Cited by 290
MFAS: Multimodal Fusion Architecture Search
Perez-Rua, J.-M., Vielzeuf, V., Pateux, S., Baccouche, M., & Jurie, F. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00713 · Cited by 193
Self-citation(2016) · MPG.PuRe (Max Planck Society) · Cited by 107
Lu, L., Dercle, L., Zhao, B., & Schwartz, L. H. (2021) · Nature Communications · doi:10.1038/s41467-021-26990-6 · Cited by 104
Densely connected convolutional networks for vibration based structural damage identification
Wang, R., Li, J., Chencho, An, S., Hao, H., Liu, W., & Li, L. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.112871 · Cited by 62
Spatio-Temporal FAST 3D Convolutions for Human Action Recognition
Stergiou, A., & Poppe, R. (2019) · 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) · doi:10.1109/icmla.2019.00036 · Cited by 21
Deep Learning for Video Classification: A Review
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Metadata
- Authors
- Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., & Baskurt, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2011
- Pages
- 29-39
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W28988658
- Cited by
- 782
- References
- 2
- FWCI
- 7.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
