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3D action recognition - analysis of human actions based on 3D skeleton data - becomes popular recently due to its succinctness, robustness, and view-invariant representation.
Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
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Automatic reconstruction of 3d human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking
Zou, B., Chen, S., Shi, C., Providence, U.M.: · 2009
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Action recognition based on a bag of 3d points
Li, W., Zhang, Z., Liu, Z.: · 2010
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Extensions of recurrent neural network language model
Mikolov, T., Kombrink, S., Burget, L., Černockỳ, J.H., Khudanpur, S.: · 2011
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Articulated pose estimation with flexible mixtures-of-parts
Yang, Y., Ramanan, D.: · 2011
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View invariant human action recognition using histograms of 3d joints
Xia, L., Chen, C., Aggarwal, J.: · 2012
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Lstm neural networks for language modeling
Sundermeyer, M., Schlüter, R., Ney, H.: · 2012
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In: Supervised Sequence Labelling with Recurrent Neural Networks. Springer (2012)
Graves, A · 2012
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Two-person interaction detection using body-pose features and multiple instance learning
Yun, K., Honorio, J., Chattopadhyay, D., Berg, T.L., Samaras, D.: · 2012
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Group sparsity and geometry constrained dictionary learning for action recognition from depth maps
Luo, J., Wang, W., Qi, H.: · 2013
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Joint angles similarities and hog 2 for action recognition
Ohn-Bar, E., Trivedi, M.: · 2013
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Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding
Mesnil, G., He, X., Deng, L., Bengio, Y.: · 2013
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Learning maximum margin temporal warping for action recognition
Wang, J., Wu, Y.: · 2013
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Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.r., Hinton, G.: · 2013
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Berkeley mhad: A comprehensive multimodal human action database
Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: · 2013
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Fusing spatiotemporal features and joints for 3d action recognition
Zhu, Y., Chen, W., Guo, G.: · 2013
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Real-time human action recognition from motion capture data
Vantigodi, S., Babu, R.V.: · 2013
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Effective 3d action recognition using eigenjoints
Yang, X., Tian, Y.: · 2014
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Skeletal quads: Human action recognition using joint quadruples
Evangelidis, G., Singh, G., Horaud, R.: · 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
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Learning actionlet ensemble for 3d human action recognition
Wang, J., Liu, Z., Wu, Y., Yuan, J.: · 2014
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Real time action recognition using histograms of depth gradients and random decision forests
Rahmani, H., Mahmood, A., Huynh, D.Q., Mian, A.: · 2014
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Multi-modal feature fusion for action recognition in rgb-d sequences
Shahroudy, A., Wang, G., Ng, T.T.: · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, 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.
Interactive body part contrast mining for human interaction recognition
Ji, Y., Ye, G., Cheng, H.: · 2014
Cited alongside, same era.
Sequence of the most informative joints (smij): A new representation for human skeletal action recognition
Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: · 2014
Cited alongside, same era.
Action recognition from motion capture data using meta-cognitive rbf network classifier
Vantigodi, S., Radhakrishnan, V.B.: · 2014
Cited alongside, same era.
Action recognition on motion capture data using a dynemes and forward differences representation
Kapsouras, I., Nikolaidis, N.: · 2014
Cited alongside, same era.
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: · 2015
Cited alongside, same era.
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
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Structural-rnn: Deep learning on spatio-temporal graphs
Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: · 2016
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Social lstm: Human trajectory prediction in crowded spaces
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: · 2016
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Structure inference machines: Recurrent neural networks for analyzing relations in group activity recognition
Deng, Z., Vahdat, A., Hu, H., Mori, G.: · 2016
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A hierarchical deep temporal model for group activity recognition
Ibrahim, M.S., Muralidharan, S., Deng, Z., Vahdat, A., Mori, G.: · 2016
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Learning activity progression in lstms for activity detection and early detection
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Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: · 2015
Cited alongside, same era.
Beyond short snippets: Deep networks for video classification
Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: · 2015
Cited alongside, same era.
Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., Salakhudinov, R.: · 2015
Cited alongside, same era.
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: · 2015
Cited alongside, same era.
Modeling spatial-temporal clues in a hybrid deep learning framework for video classification
Wu, Z., Wang, X., Jiang, Y.G., Ye, H., Xue, X.: · 2015
Cited alongside, same era.
Hierarchical recurrent neural network for skeleton based action recognition
Du, Y., Wang, W., Wang, L.: · 2015
Cited alongside, same era.
Differential recurrent neural networks for action recognition
Veeriah, V., Zhuang, N., Qi, G.J.: · 2015
Cited alongside, same era.
Ma, S., Sigal, L., Sclaroff, S.: · 2016
Closest in time.
Progressively parsing interactional objects for fine grained action detection
Ni, B., Yang, X., Gao, S.: · 2016
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Online human action detection using joint classification-regression recurrent neural networks
Li, Y., Lan, C., Xing, J., Zeng, W., Yuan, C., Liu, J.: · 2016
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A siamese long short-term memory architecture for human re-identification
Varior, R.R., Shuai, B., Lu, J., Xu, D., Wang, G.: · 2016
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Gated siamese convolutional neural network architecture for human re-identification
Varior, R.R., Haloi, M., Wang, G.: · 2016
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Action recognition by learning deep multi-granular spatio-temporal video representation
Li, Q., Qiu, Z., Yao, T., Mei, T., Rui, Y., Luo, J.: · 2016
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Ntu rgb+d: A large scale dataset for 3d human activity analysis
Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: · 2016
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Multimodal multipart learning for action recognition in depth videos
Shahroudy, A., Ng, T.T., Yang, Q., Wang, G.: · 2016
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Mining 3d key-pose-motifs for action recognition
Wang, C., Wang, Y., Yuille, A.L.: · 2016
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A hierarchical pose-based approach to complex action understanding using dictionaries of actionlets and motion poselets
Lillo, I., Carlos Niebles, J., Soto, A.: · 2016
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Real-time rgb-d activity prediction by soft regression
Hu, J.F., Zheng, W.S., Ma, L., Wang, G., Lai, J.: · 2016
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Fusion of depth, skeleton, and inertial data for human action recognition
Chen, C., Jafari, R., Kehtarnavaz, N.: · 2016
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3d action recognition from novel viewpoints
Rahmani, H., Mian, A.: · 2016
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3d-based deep convolutional neural network for action recognition with depth sequences
Liu, Z., Zhang, C., Tian, Y.: · 2016
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Effective active skeleton representation for low latency human action recognition
Cai, X., Zhou, W., Wu, L., Luo, J., Li, H.: · 2016
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Spatiotemporal representation of 3d skeleton joints-based action recognition using modified spherical harmonics
Al Alwani, A.S., Chahir, Y.: · 2016
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Deep multimodal feature analysis for action recognition in rgb+d videos
Shahroudy, A., Ng, T.T., Gong, Y., Wang, G.: · 2016
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Representation learning of temporal dynamics for skeleton-based action recognition
Du, Y., Fu, Y., Wang, L.: · 2016
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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.: · 2016
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