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/mindspore-lab/mindone
https://github.com/mirthai/csa-net
https://github.com/mirzaevinom/data_science_bowl_2018
https://github.com/mks0601/TF-SimpleHumanPose
https://github.com/mohammadreyaz/openposered
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/mohammadreyaz/pose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/mstfakts/building-detection-maskrcnn
https://github.com/multimodallearning/pytorch-mask-rcnn
https://github.com/murdockhou/Single-Stage-Multi-person-Pose-Machines
https://github.com/muyistarsky/MaskRCNN
https://github.com/naver/kapture
https://github.com/naviocean/CenterNet
https://github.com/nerminsamet/houghnet-vid
HoughNet: Integrating near and long-range evidence for visual detection
https://github.com/nikhithakarennagari/1311
https://github.com/nikhithakarennagari/Git
https://github.com/noelcodes/Mask_RCNN
https://github.com/notha99y/mean_teacher_domain_adapt
https://github.com/o-evgeny/MRCNN_DeepFashion2
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmaction2
https://github.com/open-mmlab/mmdetection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/open-mmlab/mmpose
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
https://github.com/openpifpaf/openpifpaf
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
https://github.com/openpifpaf/openpifpaf_posetrack
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
https://github.com/openpifpaf/openpifpafwebdemo
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
https://github.com/orkqueen/depplabseongil
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/osmr/imgclsmob
https://github.com/oublalkhalid/AI-OpenPose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/owena11/papers
https://github.com/paradisetechsoftsolutions/Deep-learning-model-using-Mask-R-CNN-for-predicting-AC-remote
https://github.com/periakiva/periakiva
ViewSynth: Learning Local Features from Depth using View Synthesis
https://github.com/phtruongan/py-faster-rcnn-docker
https://github.com/phykn/film-defect-detection
https://github.com/pj1920/mask-r-cnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.