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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 291300 of 339 papers

TitleStatusHype
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity FieldsCode2
PoseFix: Model-agnostic General Human Pose Refinement NetworkCode0
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New BenchmarkCode0
Multiview Supervision By RegistrationCode0
Blur-Countering Keypoint Detection via Eigenvalue Asymmetry0
Multi-Scale Supervised Network for Human Pose Estimation0
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual NetworkCode0
MONET: Multiview Semi-supervised Keypoint Detection via Epipolar DivergenceCode0
Learning to Refine Human Pose Estimation0
Simple Baselines for Human Pose Estimation and TrackingCode1
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