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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
Single-Stage Multi-Person Pose MachinesCode0
GLAMpoints: Greedily Learned Accurate Match pointsCode0
Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos0
RF-Net: An End-to-End Image Matching Network based on Receptive FieldCode0
Orthogonal Decomposition Network for Pixel-Wise Binary Classification0
Monocular 3D Object Detection via Geometric Reasoning on Keypoints0
KPTransfer: improved performance and faster convergence from keypoint subset-wise domain transfer in human pose estimation0
PifPaf: Composite Fields for Human Pose EstimationCode0
PoseFix: Model-agnostic General Human Pose Refinement NetworkCode0
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New BenchmarkCode0
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