# Active Learning for Bayesian 3D Hand Pose Estimation

arXiv: https://arxiv.org/abs/2010.00694 · HTML: https://mlsanity.com/paper/2010.00694/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Dense Hand-Object(HO) GraspNet with Full Grasping Taxonomy and Dynamics](https://mlsanity.com/paper/2409.04033/) (2024)
- [VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation](https://mlsanity.com/paper/2210.06028/) (2022)
- [Visual Transformer for Task-aware Active Learning](https://mlsanity.com/paper/2106.03801/) (2021)
- [A Mathematical Analysis of Learning Loss for Active Learning in Regression](https://mlsanity.com/paper/2104.09315/) (2021)
- [Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?](https://mlsanity.com/paper/2104.09493/) (2021)
- [Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training](https://mlsanity.com/paper/2112.13709/) (2021)
- [Modern Augmented Reality: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2202.09450/) (2022)
- [MoBYv2AL: Self-supervised Active Learning for Image Classification](https://mlsanity.com/paper/2301.01531/) (2023)
- [Active Learning for Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2309.08743/) (2023)
- [A Review on Human Pose Estimation](https://mlsanity.com/paper/2110.06877/) (2021)
- [Federated Active Learning for Target Domain Generalisation](https://mlsanity.com/paper/2312.02247/) (2023)
- [Learning Correlation-aware Aleatoric Uncertainty for 3D Hand Pose Estimation](https://mlsanity.com/paper/2509.01242/) (2025)

## Abstract (arXiv)

We propose a Bayesian approximation to a deep learning architecture for 3D hand pose estimation. Through this framework, we explore and analyse the two types of uncertainties that are influenced either by data or by the learning capability. Furthermore, we draw comparisons against the standard estimator over three popular benchmarks. The first contribution lies in outperforming the baseline while in the second part we address the active learning application. We also show that with a newly proposed acquisition function, our Bayesian 3D hand pose estimator obtains lowest errors with the least amount of data. The underlying code is publicly available at https://github.com/razvancaramalau/al_bhpe.