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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 8190 of 339 papers

TitleStatusHype
Bottom-Up Human Pose Estimation Via Disentangled Keypoint RegressionCode1
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARsCode1
End-to-End Trainable Multi-Instance Pose Estimation with TransformersCode1
Deep Dual Consecutive Network for Human Pose EstimationCode1
Multi-Instance Pose Networks: Rethinking Top-Down Pose EstimationCode1
TransPose: Keypoint Localization via TransformerCode1
Fast Fourier ConvolutionCode1
Auto Learning AttentionCode1
UKPGAN: A General Self-Supervised Keypoint DetectorCode1
One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection TasksCode1
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