https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn
Semi-Supervised Classification with Graph Convolutional Networks
Papers with Code 아카이브
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/dmlc/dgl/tree/master/examples/mxnet/sgc
https://github.com/dmlc/dgl/tree/master/examples/pytorch/arma
https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat
https://github.com/dmlc/dgl/tree/master/examples/pytorch/sgc
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/sgc
https://github.com/donshen/pointnet.phasedetection
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/doronharitan/human_activity_recognition_LRCN
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/ds4dm/sGat
https://github.com/ds4dm/sparse-gcn
https://github.com/dwtstore/sfm1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/dzb1998/pyGAT
https://github.com/dzwallkilled/IEforAR
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
https://github.com/eliasboughosn/Spatial-Temporal-Graph-Convolutional-Networks
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/emartinezs44/SparkGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ericksiavichay/cs230-final-project
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/eth212/Qmind_ModelNet10_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition
https://github.com/fanzhenliu/dagad
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/fdsa1860/skeletal
https://github.com/firework8/ProtoGCN
https://github.com/fongyk/graph-attention
https://github.com/freddieee/pn_6d_single
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ftdlyc/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/fxia22/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/galkampel/HyperNetworks
https://github.com/gayanku/FDGATII
https://github.com/gcucurull/jax-gat
https://github.com/github-zbx/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/giuseppefutia/link-prediction-code
https://github.com/gmum/umwpl2021
https://github.com/google-deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/google-research/google-research/tree/master/poem
https://github.com/gordicaleksa/pytorch-GAT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.