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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 2130 of 339 papers

TitleStatusHype
Deep Dual Consecutive Network for Human Pose EstimationCode1
Dense Interspecies Face EmbeddingCode1
CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage RefinementCode1
A Novel Dataset for Keypoint Detection of quadruped Animals from ImagesCode1
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential InformationCode1
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARsCode1
2.5D U-Net with Depth Reduction for 3D CryoET Object IdentificationCode1
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D FeaturesCode1
Deep High-Resolution Representation Learning for Human Pose EstimationCode1
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single CameraCode1
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