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Keypoint Detection

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.

( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )

Papers

Showing 301310 of 339 papers

TitleStatusHype
Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distributionCode0
Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images0
Pose2Seg: Detection Free Human Instance SegmentationCode0
StarMap for Category-Agnostic Keypoint and Viewpoint EstimationCode0
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding ModelCode0
End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching0
Data Distillation: Towards Omni-Supervised LearningCode0
Non-local Neural NetworksCode1
Cascaded Pyramid Network for Multi-Person Pose EstimationCode1
AI Challenger : A Large-scale Dataset for Going Deeper in Image UnderstandingCode0
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