Keypoint Detection
151 papers with code • 7 benchmarks • 11 datasets
Keypoint Detection involves simultaneously detecting people and localizing their keypoints. Keypoints are the same thing as interest points. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. They are invariant to image rotation, shrinkage, translation, distortion, and so on.
( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )
Benchmarks
These leaderboards are used to track progress in Keypoint Detection
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Libraries
Use these libraries to find Keypoint Detection models and implementationsDatasets
MS COCO
MPII
PASCAL3D+
OCHuman
KeypointNet
ApolloCar3D
GRIT
AwA Pose
TAMPAR
RadioGalaxyNET Dataset
Latest papers with no code
Semantic Image Attack for Visual Model Diagnosis
Rather than relying on a carefully designed test set to assess ML models' failures, fairness, or robustness, this paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images to allow model diagnosis, interpretability, and robustness.
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty
Therefore, we propose ShaRPy, the first RGB-D Shape Reconstruction and hand Pose tracking system, which provides uncertainty estimates of the computed pose, e. g., when a finger is hidden or its estimate is inconsistent with the observations in the input, to guide clinical decision-making.
PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection
In this paper, we propose a new scheme for multi-task satellite imagery road extraction, Patch-wise Road Keypoints Detection (PaRK-Detect).
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training
Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios.
Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection
In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images.
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models
We propose a new method for object pose estimation without CAD models.
Towards High Performance One-Stage Human Pose Estimation
Making top-down human pose estimation method present both good performance and high efficiency is appealing.
Cross-Domain 3D Hand Pose Estimation With Dual Modalities
To solve this problem, we present a framework for cross-domain semi-supervised hand pose estimation and target the challenging scenario of learning models from labelled multi-modal synthetic data and unlabelled real-world data.
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action
Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.
SiLK: Simple Learned Keypoints
Keypoint detection & descriptors are foundational technologies for computer vision tasks like image matching, 3D reconstruction and visual odometry.
