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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 151160 of 339 papers

TitleStatusHype
Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling0
A Unified Sequence Interface for Vision Tasks0
Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields0
SNAKE: Shape-aware Neural 3D Keypoint FieldCode2
MulT: An End-to-End Multitask Learning TransformerCode1
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localizationCode1
Learning Keypoints from Synthetic Data for Robotic Cloth FoldingCode1
AggPose: Deep Aggregation Vision Transformer for Infant Pose EstimationCode1
GRIT: General Robust Image Task BenchmarkCode1
ViTPose: Simple Vision Transformer Baselines for Human Pose EstimationCode3
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