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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 271280 of 339 papers

TitleStatusHype
Distribution-Aware Coordinate Representation for Human Pose EstimationCode0
kPAM-SC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion0
Pose Neural Fabrics SearchCode0
Learning Enhanced Resolution-wise features for Human Pose Estimation0
Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila0
A Comparison of CNN and Classic Features for Image Retrieval0
Single-Stage Multi-Person Pose MachinesCode0
GLAMpoints: Greedily Learned Accurate Match pointsCode0
Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos0
R2D2: Repeatable and Reliable Detector and DescriptorCode1
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