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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 326339 of 339 papers

TitleStatusHype
GLAMpoints: Greedily Learned Accurate Match pointsCode0
GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative SpacecraftCode0
Data Distillation: Towards Omni-Supervised LearningCode0
FC-GNN: Recovering Reliable and Accurate Correspondences from InterferencesCode0
Self-Supervised 3D Keypoint Learning for Ego-motion EstimationCode0
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New BenchmarkCode0
Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distributionCode0
TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply ChainsCode0
Associative Embedding: End-to-End Learning for Joint Detection and GroupingCode0
Self-supervised Learning of Contextualized Local Visual EmbeddingsCode0
Video-based Sequential Bayesian Homography Estimation for Soccer Field RegistrationCode0
Conditional Negative Sampling for Contrastive Learning of Visual RepresentationsCode0
Semi-supervised Human Pose Estimation in Art-historical ImagesCode0
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detectionCode0
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