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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 271–280 of 339 papers

TitleStatusHype
On Mutual Information in Contrastive Learning for Visual Representations—0
Learning Local Features with Context Aggregation for Visual Localization—0
Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection—0
High Accuracy Face Geometry Capture using a Smartphone Video—0
Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection Challenge Track Technical Report: Distribution-Aware Coordinate Representation for Human Pose Estimation—0
UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision—0
Neural Outlier Rejection for Self-Supervised Keypoint LearningCode0
SKD: Keypoint Detection for Point Clouds using Saliency Estimation—0
Dynamic Convolution: Attention over Convolution KernelsCode0
Self-Supervised 3D Keypoint Learning for Ego-motion EstimationCode0
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