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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 281290 of 339 papers

TitleStatusHype
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
Objects as PointsCode2
Key.Net: Keypoint Detection by Handcrafted and Learned CNN FiltersCode1
KPTransfer: improved performance and faster convergence from keypoint subset-wise domain transfer in human pose estimation0
PifPaf: Composite Fields for Human Pose EstimationCode0
Deep High-Resolution Representation Learning for Human Pose EstimationCode1
Rethinking on Multi-Stage Networks for Human Pose EstimationCode1
Slimmable Neural NetworksCode1
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