Papers with Code 아카이브
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/BupyeongHealer/Mask_RCNN_tf_2.x
https://github.com/Burf/tfdetection
https://github.com/CASIA-IVA-Lab/ISP-reID
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/CMU-Perceptual-Computing-Lab/openpose_caffe_train
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/CMU-Perceptual-Computing-Lab/openpose_train
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/CarstenIsert/DeepBurn
https://github.com/ChetanTayal138/HAR-Web
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/Cjmmmmmm/BreastUltrasound_Detection
https://github.com/Cli98/pytorch-cpn
https://github.com/ColdNoodler/py-faster-rcnn-cuda10
https://github.com/CuberrChen/HumanPoseBL-Paddle
https://github.com/D1vyansh/BodyJointDetection
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/DELTA37/LatentKeypointGAN
https://github.com/DagnyT/GLAMpoints-PyTorch
https://github.com/DavHoffmann/LearningToTrainWithSyntheticHumans
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/DeNA/Chainer_Mask_R-CNN
https://github.com/DivJAth/DeepLearning5922
https://github.com/EMI-Group/TransferLearning-Tasks
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
https://github.com/EliPassov/classification-ensembles
https://github.com/EmGarr/kerod
https://github.com/ErikGDev/instance-segmentation
https://github.com/Fangyh09/pose_nms
https://github.com/FateScript/CenterNet-better
https://github.com/GAOwy123/py-faster-rcnn
https://github.com/GH3927/Mask-RCNN-applied-to-cranes
https://github.com/George-Ogden/Mask-RCNN
https://github.com/Guanghan/CenterNet-Gluon
https://github.com/Guanghan/mxnet-centernet
https://github.com/HRNet/HRNet-Facial-Landmark-Detection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Human-Pose-Estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Image-Classification
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-MaskRCNN-Benchmark
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Object-Detection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Semantic-Segmentation
Deep High-Resolution Representation Learning for Human Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.