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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 201210 of 339 papers

TitleStatusHype
Joint Object Contour Points and Semantics for Instance Segmentation0
MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis0
MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching0
Monocular 3D Object Detection via Geometric Reasoning on Keypoints0
Motion-Aware Transformer For Occluded Person Re-identification0
Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network0
Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection0
Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines0
Multi-Scale Supervised Network for Human Pose Estimation0
My Emotion on your face: The use of Facial Keypoint Detection to preserve Emotions in Latent Space Editing0
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