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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 2130 of 339 papers

TitleStatusHype
Why Not Replace? Sustaining Long-Term Visual Localization via Handcrafted-Learned Feature Collaboration on CPUCode1
REF-VLM: Triplet-Based Referring Paradigm for Unified Visual DecodingCode1
2.5D U-Net with Depth Reduction for 3D CryoET Object IdentificationCode1
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential InformationCode1
Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment AnythingCode1
Edge Weight Prediction For Category-Agnostic Pose EstimationCode1
OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction on Edge Devices with NPUsCode1
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint DetectionCode1
Center Direction Network for Grasping Point Localization on ClothsCode1
Multi-Grained Contrast for Data-Efficient Unsupervised Representation LearningCode1
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