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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 5160 of 339 papers

TitleStatusHype
Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features0
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint DetectionCode1
SKT: Integrating State-Aware Keypoint Trajectories with Vision-Language Models for Robotic Garment Manipulation0
GSplatLoc: Grounding Keypoint Descriptors into 3D Gaussian Splatting for Improved Visual LocalizationCode2
Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia FeedingCode0
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection0
Center Direction Network for Grasping Point Localization on ClothsCode1
Sapiens: Foundation for Human Vision ModelsCode9
Certifying Robustness of Learning-Based Keypoint Detection and Pose Estimation Methods0
SHIC: Shape-Image Correspondences with no Keypoint Supervision0
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