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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
RADA: Robust and Accurate Feature Learning with Domain Adaptation0
Multi-Grained Contrast for Data-Efficient Unsupervised Representation LearningCode1
Scale-Free Image Keypoints Using Differentiable Persistent HomologyCode1
CapeX: Category-Agnostic Pose Estimation from Textual Point ExplanationCode1
Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection -- Towards Precise Fish Morphological Assessment in Aquaculture BreedingCode1
A Self-Supervised Method for Body Part Segmentation and Keypoint Detection of Rat Images0
SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers0
Instance-Adaptive and Geometric-Aware Keypoint Learning for Category-Level 6D Object Pose EstimationCode2
Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion0
A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets0
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