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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 181190 of 339 papers

TitleStatusHype
Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection Challenge Track Technical Report: Distribution-Aware Coordinate Representation for Human Pose Estimation0
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors0
Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features0
Keypoint Detection and Description for Raw Bayer Images0
Keypoint Detection Empowered Near-Field User Localization and Channel Reconstruction0
Keypoint Encoding for Improved Feature Extraction from Compressed Video at Low Bitrates0
Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation0
kPAM-SC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion0
KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model0
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension0
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