SOTAVerified

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 251260 of 339 papers

TitleStatusHype
Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD0
LatentKeypointGAN: Controlling Images via Latent KeypointsCode0
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types0
Regressive Domain Adaptation for Unsupervised Keypoint DetectionCode0
FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images0
Semi-supervised Keypoint Localization0
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos0
Robust Automatic Monocular Vehicle Speed Estimation for Traffic Surveillance0
Conditional Link Prediction of Category-Implicit Keypoint Detection0
Efficient grouping for keypoint detection0
Show:102550
← PrevPage 26 of 34Next →

No leaderboard results yet.