Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 31
- 보존된 평가 기록
- 235
Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PAMTRI
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PoseNet
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/simple_baselines
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/StackedHourglass
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tinydarknet
https://github.com/2023-MindSpore-1/ms-code-22
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/OpenPose
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/2023-MindSpore-1/ms-code-27
https://github.com/2023-MindSpore-1/ms-code-39
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/OpenPose
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tinydarknet
https://github.com/2023-MindSpore-1/ms-code-9/tree/main/PVNet
https://github.com/2023-MindSpore-4/Code-5/tree/main/HMR
https://github.com/2023-MindSpore-4/Code-5/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-4/Code-5/tree/main/lite-hrnet
https://github.com/2023-MindSpore-4/Code10/tree/main/MaskRCNN
https://github.com/2023-MindSpore-4/Code10/tree/main/OpenPose
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/2023-MindSpore-4/Code10/tree/main/fishnet99
https://github.com/2023-MindSpore-4/Code11/tree/main/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/2023-MindSpore-4/Code11/tree/main/StackedHourglass
https://github.com/2023-MindSpore-4/Code11/tree/main/simple_baselines
https://github.com/2023-MindSpore-4/Code11/tree/main/simple_pose
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
https://github.com/2023-MindSpore-4/Code6/tree/main/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/2023-MindSpore-4/Code7/tree/main/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/2023-MindSpore-4/Code7/tree/main/StackedHourglass
https://github.com/2023-MindSpore-4/Code7/tree/main/srcnn
https://github.com/2023-MindSpore-4/Code8/tree/main/AlphaPose
https://github.com/2023-MindSpore-4/Code8/tree/main/ArtTrack
https://github.com/2023-MindSpore-4/Code8/tree/main/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/2023-MindSpore-4/Code9/tree/main/MaskRCNN
https://github.com/2023-MindSpore-4/Code9/tree/main/OpenPose
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.