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https://doi.org/10.2139/ssrn.4646398

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-46487-9_50openalex-extracted
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
inferredcites →https://doi.org/10.1007/s11760-014-0677-9openalex-extracted
Optical flow-motion history image (OF-MHI) for action recognition
inferredcites →https://doi.org/10.1038/nature14539openalex-extracted
Deep learning
inferredcites →https://doi.org/10.1109/cvpr.2014.108openalex-extracted
Super Normal Vector for Activity Recognition Using Depth Sequences
inferredcites →https://doi.org/10.1109/cvpr.2016.115openalex-extracted
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
inferredcites →https://doi.org/10.1109/cvpr.2017.502openalex-extracted
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
inferredcites →https://doi.org/10.1109/cvpr.2019.01230openalex-extracted
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/iccv.2015.510openalex-extracted
Learning Spatiotemporal Features with 3D Convolutional Networks
inferredcites →https://doi.org/10.1109/icmew.2017.8026285openalex-extracted
Skeleton-based action recognition with convolutional neural networks
inferredcites →https://doi.org/10.1109/iscas45731.2020.9181235openalex-extracted
A Vertex-Edge Graph Convolutional Network for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/thms.2015.2504550openalex-extracted
Action Recognition From Depth Maps Using Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/tmm.2020.2978637openalex-extracted
3-D Human Behavior Understanding Using Generalized TS-LSTM Networks
inferredcites →https://doi.org/10.1109/tpami.2012.59openalex-extracted
3D Convolutional Neural Networks for Human Action Recognition
inferredcites →https://doi.org/10.1145/2393347.2396382openalex-extracted
Recognizing actions using depth motion maps-based histograms of oriented gradients
inferredcites →https://doi.org/10.1145/3343031.3351170openalex-extracted
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
inferredcites →https://doi.org/10.1609/aaai.v31i1.11212openalex-extracted
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
inferredcites →https://doi.org/10.1609/aaai.v32i1.12328openalex-extracted
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1609/aaai.v33i01.33018561openalex-extracted
Spatio-Temporal Graph Routing for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.3390/s150305197openalex-extracted
Continuous Human Action Recognition Using Depth-MHI-HOG and a Spotter Model
inferredcites →https://doi.org/10.48550/arxiv.1509.09292openalex-extracted
Convolutional Networks on Graphs for Learning Molecular Fingerprints
inferredcites →https://doi.org/10.48550/arxiv.1604.02808openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1611.06067openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1801.07455openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1804.06055openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1811.12013openalex-extracted
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