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Recurrent neural networks (RNNs) are capable of modeling the temporal dynamics of complex sequential information.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 1958
Earlier work this paper cites.
Long short-term memory
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Earlier work this paper cites.
Learning to forget: Continual prediction with lstm
Gers, F.A., Schmidhuber, J., Cummins, F.: · 1999
Earlier work this paper cites.
Learning precise timing with lstm recurrent networks
Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: · 2002
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Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: · 2011
Earlier work this paper cites.
View invariant human action recognition using histograms of 3d joints
Xia, L., Chen, C.C., Aggarwal, J.: · 2012
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Mining actionlet ensemble for action recognition with depth cameras
Wang, J., Liu, Z., Wu, Y., Yuan, J.: · 2012
Earlier work this paper cites.
Towards understanding action recognition
Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.J.: · 2013
Earlier work this paper cites.
Action recognition with improved trajectories
Wang, H., Schmid, C.: · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: · 2014
Earlier work this paper cites.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Cho, K., van Merriënboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: · 2014
Earlier work this paper cites.
Cross-view action modeling, learning and recognition
Wang, J., Nie, X., Xia, Y., Wu, Y., Zhu, S.C.: · 2014
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: · 2014
Earlier work this paper cites.
Two-stream convolutional networks for action recognition in videos
Simonyan, K., Zisserman, A.: · 2014
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Human action recognition by representing 3d skeletons as points in a lie group
Vemulapalli, R., Arrate, F., Chellappa, R.: · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., Le, Q.V.: · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
Earlier work this paper cites.
Skeletal quads: Human action recognition using joint quadruples
Evangelidis, G., Singh, G., Horaud, R.: · 2014
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Learning actionlet ensemble for 3d human action recognition
Wang, J., Liu, Z., Wu, Y.: · 2014
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A critical review of recurrent neural networks for sequence learning
Lipton, Z.C., Berkowitz, J., Elkan, C.: · 2015
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Hierarchical recurrent neural network for skeleton based action recognition
Du, Y., Wang, W., Wang, L.: · 2015
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Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: · 2015
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An empirical exploration of recurrent network architectures
Jozefowicz, R., Zaremba, W., Sutskever, I.: · 2015
Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks
Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X., et al.: · 2016
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Spatio-temporal lstm with trust gates for 3d human action recognition
Liu, J., Shahroudy, A., Xu, D., Wang, G.: · 2016
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Theano: A python framework for fast computation of mathematical expressions
Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Belopolsky, A., et al.: · 2016
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TSN model
Xiong, Y.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Real-time rgb-d activity prediction by soft regression
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Effective approaches to attention-based neural machine translation
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Action recognition using visual attention
Sharma, S., Kiros, R., Salakhutdinov, R.: · 2015
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Jointly learning heterogeneous features for rgb-d activity recognition
Hu, J.F., Zheng, W.S., Lai, J., Zhang, J.: · 2015
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Differential recurrent neural networks for action recognition
Veeriah, V., Zhuang, N., Qi, G.J.: · 2015
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Long-term recurrent convolutional networks for visual recognition and description
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Beyond short snippets: Deep networks for video classification
Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: · 2015
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Hu, J.F., Zheng, W.S., Ma, L., Wang, G., Lai, J.: · 2016
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Attention is all you need
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Attentive contexts for object detection
Li, J., Wei, Y., Liang, X., Dong, J., Xu, T., Feng, J., Yan, S.: · 2017
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Global context-aware attention lstm networks for 3d action recognition
Liu, J., Wang, G., Hu, P., Duan, L.Y., Kot, A.C.: · 2017
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An end-to-end spatio-temporal attention model for human action recognition from skeleton data
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Space-time representation of people based on 3d skeletal data: A review
Han, F., Reily, B., Hoff, W., Zhang, H.: · 2017
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Adaptive rnn tree for large-scale human action recognition
Li, W., Wen, L., Chang, M.C., Lim, S.N., Lyu, S.: · 2017
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View adaptive recurrent neural networks for high performance human action recognition from skeleton data
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: · 2017
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Deep temporal linear encoding networks
Diba, A., Sharma, V., Van Gool, L.: · 2017
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A new representation of skeleton sequences for 3d action recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F.: · 2017
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Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: · 2057
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