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 4150 of 339 papers

TitleStatusHype
MamKPD: A Simple Mamba Baseline for Real-Time 2D Keypoint Detection0
Edge Weight Prediction For Category-Agnostic Pose EstimationCode1
OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction on Edge Devices with NPUsCode1
IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose0
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension0
Silver medal Solution for Image Matching Challenge 20240
From Web Data to Real Fields: Low-Cost Unsupervised Domain Adaptation for Agricultural Robots0
Facial Chick Sexing: An Automated Chick Sexing System From Chick Facial Image0
Unsupervised Model Diagnosis0
Self-Supervised Keypoint Detection with Distilled Depth Keypoint Representation0
Show:102550
← PrevPage 5 of 34Next →

No leaderboard results yet.