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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 101110 of 339 papers

TitleStatusHype
Explicit Box Detection Unifies End-to-End Multi-Person Pose EstimationCode1
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARsCode1
Auto Learning AttentionCode1
GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware SupervisionCode1
Fast Fourier ConvolutionCode1
RegionViT: Regional-to-Local Attention for Vision TransformersCode1
Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited DataCode1
CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage RefinementCode1
AggPose: Deep Aggregation Vision Transformer for Infant Pose EstimationCode1
Generative Partition Networks for Multi-Person Pose EstimationCode1
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