https://github.com/lucylow/salty-wet-man
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Action Localization
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Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lukecavabarrett/pna
https://github.com/lyhisme/ETSN
https://github.com/lyk19940625/MyRFBNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/mHealthBuet/SegCodeNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/mabhay3420/Deep-Into-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/machanic/AU_R-CNN
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/mackitron/COVID19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/magnux/DMNN
https://github.com/maharshi95/Pose2vec
https://github.com/mahendrathapa/graph-convolution-network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/GKD
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/RA-GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/manankshastri/ART
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mansimov/unsupervised-videos
https://github.com/marble0117/GNN_models_pytorch
https://github.com/marble0117/GNN_models_pytorch_geometric
https://github.com/marblet/GNN_models_pytorch_geometric
https://github.com/marblet/gat-pytorch
https://github.com/marload/ConvNets-TensorFlow2
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/marload/ImprovedResNet-TensorFlow2
https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch
https://github.com/mcgridles/LENS
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/mdda/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mdeff/paper-cnn-graph-nips2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/megvii-research/basecls/tree/main/zoo/public/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/meliketoy/graph-cnn.pytorch
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/memory-attention-networks/MANs
Memory Attention Networks for Skeleton-based Action Recognition
https://github.com/mengxingshifen1218/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/merazlab/3D_Deep_Learning_Link
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/metalbubble/moments_models
Moments in Time Dataset: one million videos for event understanding
https://github.com/metra4ok/MLSTM-FCN-Pytorch
https://github.com/mgonzalezrivero/reef_learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.