Papers with Code 아카이브
Temporal Action Localization
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Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wen227/Pokemon_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wenkaip-1836890/pyromid_l5prediction
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/westny/dronalize
https://github.com/wfwf10/feature-based-federated-transfer-learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/whut2962575697/gat_sementic_segmentation
https://github.com/whwu95/DSANet
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/winycg/MCL-OKD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/witignite/Frustum-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wkostuch/wild-style
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wonderland-dsg/pointnet-grid
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/woodfrog/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/woongjoonchoi/DeepLearningPaper-Reproducing/tree/master/Vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wuryantoAji/POINTNET
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wushidonguc/two-stream-action-recognition-keras
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/wzmsltw/BSN-boundary-sensitive-network
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/wzzheng/doe
https://github.com/xcyan/eccv18_mtvae
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics
https://github.com/xiaogangLi/tensorflow-VTN
Lightweight Network Architecture for Real-Time Action Recognition
https://github.com/xiaoiker/GCN-NAS
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
https://github.com/xincoder/GRIP_Trajectory
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/xiuyu0000/new_papers_codes/tree/main/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/xiuyu0000/vision/blob/main/mindvision/ms3d/models/pointnet2cls.py
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/xiuyu0000/vision/blob/main/mindvision/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/xiuyu0000/vision/blob/main/mindvision/msvideo/models/r2plus1d.py
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/xjtupanda/ACGNet
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
https://github.com/xlliu7/BMN-Boundary-Matching-Network
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/xrenaa/Human-Motion-Analysis-with-Deep-Metric-Learning
https://github.com/xuhongzuo/DeepOD
https://github.com/xurui1217/pointnet.pytorch-master
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/xxcheng0708/BSNPlusPlus-boundary-sensitive-network
https://github.com/y2kmz/pointnetv2
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yCobanoglu/infinite-width-gnns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yangboz/TransferLearning4Dentist
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.