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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 291300 of 339 papers

TitleStatusHype
MONET: Multiview Semi-supervised Keypoint Detection via Epipolar DivergenceCode0
Pose2Seg: Detection Free Human Instance SegmentationCode0
MK-Pose: Category-Level Object Pose Estimation via Multimodal-Based Keypoint LearningCode0
PoseFix: Model-agnostic General Human Pose Refinement NetworkCode0
Pose Neural Fabrics SearchCode0
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint DetectionCode0
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description ExtractionCode0
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB ImagesCode0
Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia FeedingCode0
Single-Stage Multi-Person Pose MachinesCode0
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