Semi-Supervised Human Pose Estimation
3 papers with code • 2 benchmarks • 1 datasets
Semi-supervised human pose estimation aims to leverage the unlabelled data along with labeled data to improve the model performance.
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Human Pose Estimation
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PoPArt
Most implemented papers
An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation
xierc/Semi_Human_Pose
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ICCV 2021
The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations.
Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room
Second, to address the domain shift and the lack of annotations, we propose a novel unsupervised domain adaptation method, called AdaptOR, to adapt a model from an in-the-wild labeled source domain to a statistically different unlabeled target domain.
Semi-supervised Human Pose Estimation in Art-historical Images
tibhannover/iart-semi-pose
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In contrast to previous work that attempts to bridge the domain gap with pre-trained models or through style transfer, we suggest semi-supervised learning for both object and keypoint detection.
