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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 131140 of 339 papers

TitleStatusHype
Pose2Seg: Detection Free Human Instance SegmentationCode0
Neural Outlier Rejection for Self-Supervised Keypoint LearningCode0
Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distributionCode0
An Effective Image Copy-Move Forgery Detection Using Entropy InformationCode0
Multi-Person Pose Estimation with Local Joint-to-Person AssociationsCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Dynamic Convolution: Attention over Convolution KernelsCode0
RF-Net: An End-to-End Image Matching Network based on Receptive FieldCode0
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual NetworkCode0
Distribution-Aware Coordinate Representation for Human Pose EstimationCode0
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