Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CrazySherman/goodlife
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/Cyprien0105/DataScience
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DableUTeeF/keras-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Dam930/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/Dartum08/Video-Anomaly-Detection
https://github.com/Deci-AI/super-gradients
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DeepBrainsMe/FSnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DeepMotionEditing/deep-motion-editing
https://github.com/DerrickGXD/unsupervised-keypoints
Unsupervised Learning of Object Keypoints for Perception and Control
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/DirtyHarryLYL/HAKE
HAKE: A Knowledge Engine Foundation for Human Activity Understanding
https://github.com/DixinFan/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/DuaneNielsen/keypoints
Unsupervised Learning of Object Keypoints for Perception and Control
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/EVOKE-LMM/EVOKE
In-Context Editing: Learning Knowledge from Self-Induced Distributions
https://github.com/East-Tree/groupAc_GCN
Learning Actor Relation Graphs for Group Activity Recognition
https://github.com/Ecgbert/Grad_CAM_PLUS_PLUS
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
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/EquipoVandV/mmactionVandV
https://github.com/FelixMQuintana/Deep-Vasp-E
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/FingerRec/DSM
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion
https://github.com/Fragjacker/Pointcloud-grad-CAM
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
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/Gahyeonkim09/AAPL
Learning Transferable Visual Models From Natural Language Supervision
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.