Fetching the paper…
Reading the bibliography…
Graph convolutional networks (GCNs), which generalize CNNs to more generic non-Euclidean structures, have achieved remarkable performance for skeleton-based action recognition.
J. Atwood and D. Towsley, “Diffusion-convolutional neural networks,” in
2001
Earlier work this paper cites.
H. Wang and C. Schmid, “Action Recognition with Improved Trajectories,” in
2013
Earlier work this paper cites.
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,”
2013
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos,” in
2014
Earlier work this paper cites.
R. Vemulapalli, F. Arrate, and R. Chellappa, “Human action recognition by representing 3d skeletons as points in a lie group,” in
2014
Earlier work this paper cites.
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral Networks and Locally Connected Networks on Graphs,” in
2014
Earlier work this paper cites.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning Spatiotemporal Features With 3d Convolutional Networks,” in
2015
Earlier work this paper cites.
B. Fernando, E. Gavves, J. M. Oramas, A. Ghodrati, and T. Tuytelaars, “Modeling video evolution for action recognition,” in
2015
Earlier work this paper cites.
Y. Du, W. Wang, and L. Wang, “Hierarchical recurrent neural network for skeleton based action recognition,” in
2015
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional Networks on Graphs for Learning Molecular Fingerprints,” in
2015
Earlier work this paper cites.
M. Henaff, J. Bruna, and Y. LeCun, “Deep convolutional networks on graph-structured data,”
2015
Earlier work this paper cites.
A. Shahroudy, J. Liu, T.-T. Ng, and G. Wang, “NTU RGB+D: A Large Scale Dataset for 3d Human Activity Analysis,” in
2016
Earlier work this paper cites.
J. Liu, A. Shahroudy, D. Xu, and G. Wang, “Spatio-Temporal LSTM with Trust Gates for 3d Human Action Recognition,” in
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,”
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering,” in
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in
2016
Earlier work this paper cites.
J. Carreira and A. Zisserman, “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset,” in
2017
Earlier work this paper cites.
S. Song, C. Lan, J. Xing, W. Zeng, and J. Liu, “An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data.” in
2017
Earlier work this paper cites.
P. Zhang, C. Lan, J. Xing, W. Zeng, J. Xue, and N. Zheng, “View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition From Skeleton Data,” in
2017
Cited alongside, same era.
T. S. Kim and A. Reiter, “Interpretable 3d human action analysis with temporal convolutional networks,” in
2017
Cited alongside, same era.
Q. Ke, M. Bennamoun, S. An, F. A. Sohel, and F. Boussaïd, “A New Representation of Skeleton Sequences for 3d Action Recognition,”
2017
Cited alongside, same era.
M. Liu, H. Liu, and C. Chen, “Enhanced skeleton visualization for view invariant human action recognition,”
2017
Cited alongside, same era.
C. Li, Q. Zhong, D. Xie, and S. Pu, “Skeleton-based action recognition with convolutional neural networks,” in
2017
Cited alongside, same era.
S. Yan, Y. Xiong, and D. Lin, “Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition,” in
2018
Later among the works it cites.
Y. Tang, Y. Tian, J. Lu, P. Li, and J. Zhou, “Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition,” in
2018
Later among the works it cites.
Y. Zhang, C. Cao, J. Cheng, and H. Lu, “EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition,”
2018
Later among the works it cites.
T. Kipf, E. Fetaya, K.-C. Wang, M. Welling, and R. Zemel, “Neural relational inference for interacting systems,” in
2018
Later among the works it cites.
J. Hu, L. Shen, S. Albanie, G. Sun, and A. Vedaldi, “Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks,” in
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
B. Li, Y. Dai, X. Cheng, H. Chen, Y. Lin, and M. He, “Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN,” in
2017
Cited alongside, same era.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in
2017
Cited alongside, same era.
F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein, “Geometric deep learning on graphs and manifolds using mixture model CNNs,” in
2017
Cited alongside, same era.
F. Wang, M. Jiang, C. Qian, S. Yang, C. Li, H. Zhang, X. Wang, and X. Tang, “Residual Attention Network for Image Classification,” in
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Å. Kaiser, and I. Polosukhin, “Attention is All you Need,” in
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in
2017
Cited alongside, same era.
C. Si, Y. Jing, W. Wang, L. Wang, and T. Tan, “Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning,” in
2018
Later among the works it cites.
C. Cao, C. Lan, Y. Zhang, W. Zeng, H. Lu, and Y. Zhang, “Skeleton-Based Action Recognition with Gated Convolutional Neural Networks,”
2018
Later among the works it cites.
X. Wang and A. Gupta, “Videos as Space-Time Region Graphs,”
2018
Later among the works it cites.
A. Shahroudy, T. Ng, Y. Gong, and G. Wang, “Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos,”
2018
Later among the works it cites.
D. C. Luvizon, D. Picard, and H. Tabia, “2d/3d Pose Estimation and Action Recognition Using Multitask Deep Learning,” in
2018
Later among the works it cites.
F. Baradel, C. Wolf, J. Mille, and G. W. Taylor, “Glimpse Clouds: Human Activity Recognition From Unstructured Feature Points,” in
2018
Later among the works it cites.
M. Liu and J. Yuan, “Recognizing Human Actions as the Evolution of Pose Estimation Maps,” in
2018
Later among the works it cites.
L. Shi, Y. Zhang, J. Cheng, and H. Lu, “Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition,” in
2019
Closest in time.
L. Shi, Y. Zhang, C. Jian, and L. Hanqing, “Gesture Recognition using Spatiotemporal Deformable Convolutional Represention,” in
2019
Closest in time.
S. Das, A. Chaudhary, F. Bremond, and M. Thonnat, “Where to Focus on for Human Action Recognition?” in
2019
Closest in time.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in
2023
Closest in time.
K. Xu, J. Ba, R. Kiros, K. Cho, A. C. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio, “Show, Attend and Tell: Neural Image Caption Generation with Visual Attention,” in
2057
Closest in time.