Papers with Code 아카이브
Action Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 11
- 보존된 평가 기록
- 81
Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Crevass/Hybrid-Agent
https://github.com/DLR-RM/stable-baselines3
https://github.com/DanielLSM/safe-rl-tutorial
https://github.com/Dekki-Aero/DDPG
https://github.com/Diego999/pyGAT
https://github.com/DigitalBiomarkerDiscoveryPipeline/Human-Activity-Recognition
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
https://github.com/Dir-b/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/DixinFan/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/DominikBatic/EndoViT
Whether and When does Endoscopy Domain Pretraining Make Sense?
https://github.com/DylanWusee/pointconv_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ECE685-FinalProject/3D-object-recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ELKYang/2s-AGCN-paddle
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/ElliotHYLee/VisualOdometry3D
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/ElricXin/Skeleton-MixFormer
https://github.com/EyaRhouma/collaboration-competition-MADDPG
https://github.com/FlyienSHaDOw/continuous_control
https://github.com/FlyienSHaDOw/project_2_continuous_control
https://github.com/Fragjacker/Pointcloud-grad-CAM
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/FrederikSchorr/sign-language
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Frostinassiky/gtad
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/GOD-GOD-Autonomous-Vehicle/self-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Gbouna/Action-Recognition-for-Privacy-Preserving-Ambient-Assisted-Living
https://github.com/Gbouna/HT-ConvNet
https://github.com/GeyuanZhang/st-gcn-master
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Gouet/DDPG_PendulumV1
https://github.com/GraphSAINT/GraphSAINT
https://github.com/HHousen/speaker-change-detection
End-to-end speaker segmentation for overlap-aware resegmentation
https://github.com/HJDQN/HJQ
https://github.com/HamzaGbada/GCN-Numpy
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/Harut0726/my-pointnet-tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/HazyResearch/hgcn
https://github.com/HeapHop30/graph-attention-nets
https://github.com/HoganZhang/BSN-boundary-sensitive-network.pytorch
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/HoganZhang/pygcn_python3
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/HuCui2022/JPFormer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.