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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 6170 of 339 papers

TitleStatusHype
Greedy Offset-Guided Keypoint Grouping for Human Pose EstimationCode1
Multi-Grained Contrast for Data-Efficient Unsupervised Representation LearningCode1
Keypoint CommunitiesCode1
Learning Keypoints from Synthetic Data for Robotic Cloth FoldingCode1
Nonlinear optical encoding enabled by recurrent linear scatteringCode1
A lightweight 3D dense facial landmark estimation model from position map dataCode1
Dense Interspecies Face EmbeddingCode1
Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection -- Towards Precise Fish Morphological Assessment in Aquaculture BreedingCode1
Deep High-Resolution Representation Learning for Human Pose EstimationCode1
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D FeaturesCode1
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