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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 241250 of 339 papers

TitleStatusHype
RelativeNAS: Relative Neural Architecture Search via Slow-Fast LearningCode1
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes0
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images0
Joint Object Contour Points and Semantics for Instance Segmentation0
GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware SupervisionCode1
Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation0
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint EstimatesCode1
SEKD: Self-Evolving Keypoint Detection and DescriptionCode1
Improving Convolutional Networks With Self-Calibrated ConvolutionsCode1
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