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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 7180 of 339 papers

TitleStatusHype
Polarized Self-Attention: Towards High-quality Pixel-wise RegressionCode1
SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via StereoCode1
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose EstimationCode1
RegionViT: Regional-to-Local Attention for Vision TransformersCode1
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud RegistrationCode1
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single CameraCode1
MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose EstimationCode1
HoughNet: Integrating near and long-range evidence for visual detectionCode1
Pose Recognition with Cascade TransformersCode1
DexYCB: A Benchmark for Capturing Hand Grasping of ObjectsCode1
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