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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

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Showing 171–180 of 339 papers

TitleStatusHype
Improved 2D Keypoint Detection in Out-of-Balance and Fall Situations -- combining input rotations and a kinematic model—0
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description ExtractionCode0
Parallel Multi-Scale Networks with Deep Supervision for Hand Keypoint Detection—0
GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast DescriptorCode1
Few-shot Keypoint Detection with Uncertainty Learning for Unseen SpeciesCode1
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings—0
ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor ExtractionCode1
Dense Keypoints via Multiview Supervision—0
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware AssociationCode0
Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images—0
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