论文标题
贝叶斯3D手姿势估计的主动学习
Active Learning for Bayesian 3D Hand Pose Estimation
论文作者
论文摘要
我们建议对3D手姿势估计的深度学习体系结构进行贝叶斯近似。通过此框架，我们探索和分析受数据或学习能力影响的两种类型的不确定性。此外，我们在三个流行的基准测试中与标准估计器进行了比较。第一个贡献在于优于基线，而在第二部分中，我们解决了主动学习应用程序。我们还表明，通过新提出的采集函数，我们的贝叶斯3D手姿势估计器以最少的数据获得了最低的错误。基础代码可在https://github.com/razvancaramalau/al_bhpe上公开获得。
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.
