# Semi-supervised 2D Human Pose Estimation via Adaptive Keypoint Masking

arXiv: https://arxiv.org/abs/2404.14835 · HTML: https://mlsanity.com/paper/2404.14835/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 30 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning](https://mlsanity.com/paper/1606.04586/) (2016)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation](https://mlsanity.com/paper/1406.2984/) (2014)
- [Data Distillation: Towards Omni-Supervised Learning](https://mlsanity.com/paper/1712.04440/) (2017)
- [Compositional Human Pose Regression](https://mlsanity.com/paper/1704.00159/) (2017)
- [Human Pose Estimation with Iterative Error Feedback](https://mlsanity.com/paper/1507.06550/) (2015)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Human Pose Regression by Combining Indirect Part Detection and Contextual Information](https://mlsanity.com/paper/1710.02322/) (2017)
- [Multi-Scale Structure-Aware Network for Human Pose Estimation](https://mlsanity.com/paper/1803.09894/) (2018)
- [ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization](https://mlsanity.com/paper/2208.08631/) (2022)
- [Human Pose Estimation with Spatial Contextual Information](https://mlsanity.com/paper/1901.01760/) (2019)
- [An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation](https://mlsanity.com/paper/2011.12498/) (2020)
- [DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision](https://mlsanity.com/paper/2205.05575/) (2022)

## Abstract (arXiv)

Human pose estimation is a fundamental and challenging task in computer vision. Larger-scale and more accurate keypoint annotations, while helpful for improving the accuracy of supervised pose estimation, are often expensive and difficult to obtain. Semi-supervised pose estimation tries to leverage a large amount of unlabeled data to improve model performance, which can alleviate the problem of insufficient labeled samples. The latest semi-supervised learning usually adopts a strong and weak data augmented teacher-student learning framework to deal with the challenge of "Human postural diversity and its long-tailed distribution". Appropriate data augmentation method is one of the key factors affecting the accuracy and generalization of semi-supervised models. Aiming at the problem that the difference of sample learning is not considered in the fixed keypoint masking augmentation method, this paper proposes an adaptive keypoint masking method, which can fully mine the information in the samples and obtain better estimation performance. In order to further improve the generalization and robustness of the model, this paper proposes a dual-branch data augmentation scheme, which can perform Mixup on samples and features on the basis of adaptive keypoint masking. The effectiveness of the proposed method is verified on COCO and MPII, outperforming the state-of-the-art semi-supervised pose estimation by 5.2% and 0.3%, respectively.