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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 151160 of 339 papers

TitleStatusHype
Neural Outlier Rejection for Self-Supervised Keypoint LearningCode0
Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph ApproachCode0
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation ModelCode0
MK-Pose: Category-Level Object Pose Estimation via Multimodal-Based Keypoint LearningCode0
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint DetectionCode0
MONET: Multiview Semi-supervised Keypoint Detection via Epipolar DivergenceCode0
Interspecies Knowledge Transfer for Facial Keypoint DetectionCode0
Improving the matching of deformable objects by learning to detect keypointsCode0
Deep Alignment Network: A convolutional neural network for robust face alignmentCode0
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localizationCode0
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