Keypoint Detection
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Keypoint Detection is essential for analyzing and interpreting images in computer vision. It involves simultaneously detecting and localizing interesting points in an image. Keypoints, also known as interest points, are spatial locations or points in the image that define what is interesting or what stands out. They are invariant to image rotation, shrinkage, translation, distortion, etc. Keypoints examples are body joints, facial landmarks, or any other salient points in objects. Keypoints have uses in problems such as pose estimation, object detection and tracking, facial analysis, and augmented reality.
<span style="color:grey; opacity: 0.6">( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/ArtTrack
https://github.com/2023-MindSpore-1/ms-code-199
https://github.com/2023-MindSpore-1/ms-code-207
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DDM
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/simple_baselines
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-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-7/tree/main/tinydarknet
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/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/Code8/tree/main/AlphaPose
https://github.com/2023-MindSpore-4/Code8/tree/main/ArtTrack
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
https://github.com/2024-MindSpore-1/Code4/tree/main/OpenPose
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/2024-MindSpore-1/Code6/tree/main/ArtTrack
https://github.com/593903762/center
https://github.com/97andrea97/CenterNet-PNPDet
https://github.com/AKASH2907/bird-species-classification
https://github.com/AKASH2907/bird_species_classification
https://github.com/AkashGanesan/PedestrianAttention
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/Alexander-Whelan/Zeus
https://github.com/AlongRide/CenterNet_anchor_free
https://github.com/Aprilkaka/nonlocal_mindspore
https://github.com/Arthur-Shi/py-faster-rcnn
https://github.com/AshishSingh2261/Pedestrian_Instance_Segmentation
https://github.com/Ayaanesmail/Test.-
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/Biantian/MscProject
https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.