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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 181190 of 339 papers

TitleStatusHype
Self-supervised Learning of Contextualized Local Visual EmbeddingsCode0
RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy0
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras0
Improving the matching of deformable objects by learning to detect keypointsCode0
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment0
CoDeF: Content Deformation Fields for Temporally Consistent Video ProcessingCode0
ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition0
KP2Dtiny: Quantized Neural Keypoint Detection and Description on the EdgeCode0
CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild0
Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a DatasetCode0
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