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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 301310 of 339 papers

TitleStatusHype
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
R2D2: Reliable and Repeatable Detector and DescriptorCode0
LatentKeypointGAN: Controlling Images via Latent KeypointsCode0
AI Challenger : A Large-scale Dataset for Going Deeper in Image UnderstandingCode0
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task LearningCode0
ViewSynth: Learning Local Features from Depth using View SynthesisCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
6-DoF Object Pose from Semantic KeypointsCode0
Deep Alignment Network: A convolutional neural network for robust face alignmentCode0
Open-Vocabulary Animal Keypoint Detection with Semantic-feature MatchingCode0
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