https://github.com/xuyankun/vsvig
공식 코드VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
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/xuyizdby/noiseerasar
공식 코드https://github.com/yellowtownhz/STIGCN
공식 코드Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/yfsong0709/EfficientGCNv1
공식 코드Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
https://github.com/yfsong0709/RA-GCNv2
공식 코드Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
https://github.com/yijieyang23/skelet-gcn
공식 코드Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation
https://github.com/yilliajing/action-ood
공식 코드https://github.com/yilliajing/skeleton-ood
공식 코드https://github.com/yujieouo/pstl
공식 코드Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences
https://github.com/yujieouo/smie
공식 코드Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
https://github.com/yuxiaochen1103/DG-STA
공식 코드Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
https://github.com/yysijie/st-gcn
공식 코드Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/zahrag/gg3dhar
공식 코드Hierarchical growing grid networks for skeleton based action recognition
https://github.com/zhouyuxuanyx/blockgcn
공식 코드https://github.com/zhysora/fr-head
공식 코드Learning Discriminative Representations for Skeleton Based Action Recognition
https://github.com/zzysteve/skeletonx
공식 코드SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation
https://gitee.com/gai-shaoyan/mind3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/1075225782/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/1zgh/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/stgcn
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
https://github.com/2023-MindSpore-1/ms-code-24
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/2023-MindSpore-1/ms-code-35
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/404nofound/gat
https://github.com/4pygmalion/GAT_tensorflow
https://github.com/AI-Guru/pointcloud_experiments
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/AbdurrahmanNadi/activity_recognition_web_service
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/AbiterVX/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/AlbertoSabater/Domain-and-View-point-Agnostic-Hand-Action-Recognition
https://github.com/Alexyuda/action_recognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.