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Keypoint Detection

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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.

<span style="color:grey; opacity: 0.6">( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )</span>

논문

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Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets

Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images

Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection Challenge Track Technical Report: Distribution-Aware Coordinate Representation for Human Pose Estimation

Keypoint Detection

논문

Conditional Link Prediction of Category-Implicit Keypoint Detection

UKPGAN: A General Self-Supervised Keypoint Detector

One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks

EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight Transfer

Efficient grouping for keypoint detection

Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets

Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration

Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer

Rotate to Attend: Convolutional Triplet Attention Module

Conditional Negative Sampling for Contrastive Learning of Visual Representations

CoKe: Localized Contrastive Learning for Robust Keypoint Detection

Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild

RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning

Buy Me That Look: An Approach for Recommending Similar Fashion Products

GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes

Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images

Joint Object Contour Points and Semantics for Instance Segmentation

GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision

Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation

Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates

SEKD: Self-Evolving Keypoint Detection and Description

GoodPoint: unsupervised learning of keypoint detection and description

On Mutual Information in Contrastive Learning for Visual Representations

Learning Local Features with Context Aggregation for Visual Localization

Learning Human-Object Interaction Detection using Interaction Points

Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection

High Accuracy Face Geometry Capture using a Smartphone Video

Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection Challenge Track Technical Report: Distribution-Aware Coordinate Representation for Human Pose Estimation

Learning Delicate Local Representations for Multi-Person Pose Estimation

Towards High Performance Human Keypoint Detection

UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision

Neural Outlier Rejection for Self-Supervised Keypoint Learning

SKD: Keypoint Detection for Point Clouds using Saliency Estimation

Self-Supervised 3D Keypoint Learning for Ego-motion Estimation

Dynamic Convolution: Attention over Convolution Kernels

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation