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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 111120 of 339 papers

TitleStatusHype
REF-VLM: Triplet-Based Referring Paradigm for Unified Visual DecodingCode1
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud RegistrationCode1
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose EstimationCode1
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same ActionCode1
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware AssociationCode0
Conditional Negative Sampling for Contrastive Learning of Visual RepresentationsCode0
GLAMpoints: Greedily Learned Accurate Match pointsCode0
Associative Embedding: End-to-End Learning for Joint Detection and GroupingCode0
Neural Outlier Rejection for Self-Supervised Keypoint LearningCode0
GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative SpacecraftCode0
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