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 111120 of 339 papers

TitleStatusHype
MulT: An End-to-End Multitask Learning TransformerCode1
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud RegistrationCode1
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose EstimationCode1
Improving Convolutional Networks With Self-Calibrated ConvolutionsCode1
Cross-Domain 3D Hand Pose Estimation With Dual Modalities0
Corn Ear Detection and Orientation Estimation Using Deep Learning0
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples0
A Fast Keypoint Based Hybrid Method for Copy Move Forgery Detection0
Conditional Link Prediction of Category-Implicit Keypoint Detection0
IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose0
Show:102550
← PrevPage 12 of 34Next →

No leaderboard results yet.