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/charlesshang/fastmaskrcnn
https://github.com/chengguixiong1010/rnn_STAF_2dpose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/chenwuperth/ClaRAN
https://github.com/chenyouxin113/sota-status-investigation
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/chihyanghsu0805/object_detection_yolo
https://github.com/chingswy/HumanPoseMemo
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
https://github.com/chongchen20/Deeplabcut
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/chonyy/AI-basketball-analysis
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/chuanqichen/deepcoaching
https://github.com/cientgu/instructdiffusion
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
https://github.com/cmsflash/efficient-attention
https://github.com/code-implementation1/Code6/tree/main/Non_local
https://github.com/code-implementation1/Code8/tree/main/simple_baselines
https://github.com/code-implementation1/Code8/tree/main/simple_pose
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_mobilenetv1
https://github.com/code-implementation1/Code9/tree/main/ShuffleNet
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/conceptofmind/Region-ViT-flax
RegionViT: Regional-to-Local Attention for Vision Transformers
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/dc3ea9f/RegionViT
RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/delldu/MaskRCNN
https://github.com/deolipankaj/Stone_Detection_MRCNN
https://github.com/devsoft123/fast-cnn
https://github.com/dheerajpreddy/CenterNet-without-DCN
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/doheelab/pku-CenterNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.