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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 171180 of 339 papers

TitleStatusHype
Single-Stage Multi-Person Pose MachinesCode0
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB ImagesCode0
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task LearningCode0
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
ViewSynth: Learning Local Features from Depth using View SynthesisCode0
Human Keypoint Detection by Progressive Context RefinementCode0
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint DetectionCode0
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding ModelCode0
Spatial regularisation for improved accuracy and interpretability in keypoint-based registrationCode0
StarMap for Category-Agnostic Keypoint and Viewpoint EstimationCode0
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