ベイズ推定による3D手姿勢推定のための能動学習
Active Learning for Bayesian 3D Hand Pose Estimation
3D手姿勢推定の深層学習にベイズ近似を導入し、不確実性を分析するとともに、新しい獲得関数により少ないデータで高精度を実現する能動学習手法を提案した。
著者: Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
