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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 311320 of 339 papers

TitleStatusHype
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding ModelCode0
End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching0
Data Distillation: Towards Omni-Supervised LearningCode0
AI Challenger : A Large-scale Dataset for Going Deeper in Image UnderstandingCode0
Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network0
Deep Alignment Network: A convolutional neural network for robust face alignmentCode0
Hand Keypoint Detection in Single Images using Multiview BootstrappingCode0
Interspecies Knowledge Transfer for Facial Keypoint DetectionCode0
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection0
6-DoF Object Pose from Semantic KeypointsCode0
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