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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 231240 of 339 papers

TitleStatusHype
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types0
Fast keypoint detection in video sequences0
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings0
FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images0
FPC-Net: Revisiting SuperPoint with Descriptor-Free Keypoint Detection via Feature Pyramids and Consistency-Based Implicit Matching0
From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection0
From Web Data to Real Fields: Low-Cost Unsupervised Domain Adaptation for Agricultural Robots0
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes0
GKNet: grasp keypoint network for grasp candidates detection0
HandsOff: Labeled Dataset Generation With No Additional Human Annotations0
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