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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

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Showing 121–130 of 339 papers

TitleStatusHype
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training—0
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint DetectionCode0
Explicit Box Detection Unifies End-to-End Multi-Person Pose EstimationCode1
Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection—0
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models—0
Towards High Performance One-Stage Human Pose EstimationCode0
SiLK: Simple Learned Keypoints—0
Cross-Domain 3D Hand Pose Estimation With Dual Modalities—0
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action—0
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same ActionCode1
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