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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 191200 of 339 papers

TitleStatusHype
A Low Rank Promoting Prior for Unsupervised Contrastive Learning0
Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle0
Exploring Set Similarity for Dense Self-supervised Representation Learning0
Task-Aware Sampling Layer for Point-Wise Analysis0
Greedy Offset-Guided Keypoint Grouping for Human Pose EstimationCode1
Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?0
Polarized Self-Attention: Towards High-quality Pixel-wise RegressionCode1
SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via StereoCode1
Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks0
GKNet: grasp keypoint network for grasp candidates detection0
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