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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 111120 of 339 papers

TitleStatusHype
Non-local Neural NetworksCode1
Cascaded Pyramid Network for Multi-Person Pose EstimationCode1
Generative Partition Networks for Multi-Person Pose EstimationCode1
Mask R-CNNCode1
KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model0
GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative SpacecraftCode0
FPC-Net: Revisiting SuperPoint with Descriptor-Free Keypoint Detection via Feature Pyramids and Consistency-Based Implicit Matching0
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection0
Reading a Ruler in the Wild0
MK-Pose: Category-Level Object Pose Estimation via Multimodal-Based Keypoint LearningCode0
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