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/ChrisWu1997/2D-Motion-Retargeting
Learning Character-Agnostic Motion for Motion Retargeting in 2D
https://github.com/Cmeijiang/PointNet_MS
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ColdenChan/GAT_D
https://github.com/CrazySherman/goodlife
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/CryptoSalamander/pytorch_paper_implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DMarwan/NutricIA
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Dartum08/Video-Anomaly-Detection
https://github.com/Deci-AI/super-gradients
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/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/Djmcflush/Funky-Movement
https://github.com/DrRyanHuang/VGGNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DuaneNielsen/keypoints
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/Duplums/bhb10k-dl-benchmark
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/Ecgbert/Grad_CAM_PLUS_PLUS
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EdoWhite/Gate-Shift-Pose
https://github.com/EiroaMD/final_project_IH_pneumonia
Very Deep Convolutional Networks for Large-Scale Image 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/EngineeringSapien/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EverLookNeverSee/diag2model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EvgenyVendrov/DeepLearningCourse
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
https://github.com/FelixMQuintana/Deep-Vasp-E
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/Finspire13/CMCS-Temporal-Action-Localization
https://github.com/Finspire13/RL-Surgical-Gesture-Segmentation
Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.