https://github.com/antoniolq/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
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Skeleton Based Action Recognition
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Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.
<span style="color:grey; opacity: 0.6">( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/arangoml/fastgraphml
https://github.com/asif-salim/GCN-Graph_Convolutional_Network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/aviros/pointnet_totations
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/aviros/roatationPointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/basiralab/RG-Select
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/Reproducible-Generative-Learning
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/reproduciblefedgnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/batzner/indrnn
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/bcsrn/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/benedekrozemberczki/pytorch_geometric_temporal
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/bepping/non-oversmoothing-gcns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/bestaar/predictiveFilterFlow
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
https://github.com/blueberryc/pyGAT
https://github.com/bowu/GraphSearch
https://github.com/brendanmallery9/entropic-barycenters
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/bruceyo/MMNet
https://github.com/bt77/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/calciver/Graph-Attention-Networks
https://github.com/changminwu/expandergnn
https://github.com/charlesq34/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/clin366/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/coconutzs/PointNet_zs
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/coderSkyChen/Action_Recognition_Zoo
Temporal Convolutional Networks for Action Segmentation and Detection
https://github.com/colincsl/TemporalConvolutionalNetworks
Temporal Convolutional Networks for Action Segmentation and Detection
https://github.com/cybermonic/cage-4-submission
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/daniansan/i3d_mindspore
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/danielegrattarola/keras-gat
https://github.com/darnbi/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/davidpicard/homm
https://github.com/debajyotid2/pointnet_pl
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/deepchem/moleculenet
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/dlpbc/keras-kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/dmeoli/neuro-sat
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.