https://github.com/AbiterVX/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action 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/Afsaan/CNN-Architectures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AlbertFeng-0405/SceneTransformer-pytorch
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/AlbertoSabater/Domain-and-View-point-Agnostic-Hand-Action-Recognition
https://github.com/AlexMRuch/Can-x2vec-Save-Lives
https://github.com/Alexyuda/action_recognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/AlfredoZermini/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Anak2016/GAT
https://github.com/AndreiMoraru123/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Andrew-booler/BSN-ActivityNet
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/AngusMonroe/GAT-pytorch
https://github.com/Aniket7/VGGNet-CNN-architecture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Anou9531/GAT
https://github.com/Anuranjan-Kumar/kaggle_cats-vs-dogs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ArifRaihan/Deeper-CNN-Architectures-for-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Around-30/Kaggle
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
https://github.com/Atze00/MoViNet-pytorch
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/Aveek-Saha/Graph-Attention-Net
https://github.com/AveryGriffin/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BIG-CHENG/FaceRec
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BIG-S2/keras-gnm
https://github.com/BPMJG/annotated_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Bangbangbanana/r2plus1d_mindspore
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/BeanGreen247/Python-AI-Arts
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BelixRogner/SpeedChallenge
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/BigLazyPig/Pytorch-StNet-Full-Implement
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/BlurryLight/DD-Net-Pytorch
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/BoChenUIUC/YOWO
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/CAMMA-public/cholect50
Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
https://github.com/CellSMB/EM-net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/CheesyB/cpointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ChengBinJin/ActionViewer
Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN
https://github.com/ChengSashankh/trying-GAT
https://github.com/ChengyuSun/gat
https://github.com/ChetanTayal138/HAR-Web
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.