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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 4150 of 339 papers

TitleStatusHype
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint EstimatesCode1
Bottom-Up Human Pose Estimation Via Disentangled Keypoint RegressionCode1
BPFNet: A Unified Framework for Bimodal Palmprint Alignment and FusionCode1
End-to-End Trainable Multi-Instance Pose Estimation with TransformersCode1
CapeX: Category-Agnostic Pose Estimation from Textual Point ExplanationCode1
Cascaded Pyramid Network for Multi-Person Pose EstimationCode1
Fast Fourier ConvolutionCode1
Centroid Distance Keypoint Detector for Colored Point CloudsCode1
A Novel Dataset for Keypoint Detection of quadruped Animals from ImagesCode1
Deep Dual Consecutive Network for Human Pose EstimationCode1
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