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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

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Showing 101–110 of 339 papers

TitleStatusHype
Learning Delicate Local Representations for Multi-Person Pose EstimationCode1
Towards High Performance Human Keypoint DetectionCode1
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose EstimationCode1
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose EstimationCode1
R2D2: Repeatable and Reliable Detector and DescriptorCode1
Key.Net: Keypoint Detection by Handcrafted and Learned CNN FiltersCode1
Deep High-Resolution Representation Learning for Human Pose EstimationCode1
Rethinking on Multi-Stage Networks for Human Pose EstimationCode1
Slimmable Neural NetworksCode1
Simple Baselines for Human Pose Estimation and TrackingCode1
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