Papers with Code 아카이브
Multi-Person Pose Estimation
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Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.
<span style="color:grey; opacity: 0.6">( Image credit: Human Pose Estimation with TensorFlow )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/code-implementation1/Code9/tree/main/hrnet
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
https://github.com/cokowpublic/Nuclei-Segmentation
https://github.com/collectionslab/Omniscribe
https://github.com/collectionslab/annotations-computervision
https://github.com/collectionslab/book-annotation-classification
https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn
https://github.com/cwbabel/faster-cnn
https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/daniegr/EfficientPose
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/danielperezr88/multiposenet-aries
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
https://github.com/delldu/MaskRCNN
https://github.com/deolipankaj/Stone_Detection_MRCNN
https://github.com/devsoft123/fast-cnn
https://github.com/diamondto/IBP
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
https://github.com/dog-qiuqiu/MobileNetV2-SimplePose
https://github.com/dog-qiuqiu/Ultralight-SimplePose
https://github.com/ducongju/HRNet
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/dvl-tum/motsynth-baselines
https://github.com/eho-tacc/DeepLabCut
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/eldar/deepcut
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
https://github.com/eldar/deepcut-cnn
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
https://github.com/eldar/pose-tensorflow
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/elonashatri/pitch_mask_rcnn
https://github.com/eric-erki/pose-residual-network-pytorch
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
https://github.com/estelabalboa/Proyecto_Final_Pilates
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/evaristr/py-faster_rcnn
https://github.com/facebookresearch/detectron
https://github.com/facebookresearch/detectron2
https://github.com/facebookresearch/maskrcnn-benchmark
https://github.com/faheinrich/moon_pose_estimation_setup
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
https://github.com/faheinrich/moon_pose_with_detectron2
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
https://github.com/fcr3/OpenVINO_Tutorials
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/fcr3/wine_detector
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/fdac18/ForensicImages
https://github.com/fengyidong123/openpose-master
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.