Multi-class Multi-annotator Active Learning with Robust Gaussian Process for Visual Recognition

Chengjiang Long, G. Hua
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引用次数: 69

Abstract

Active learning is an effective way to relieve the tedious work of manual annotation in many applications of visual recognition. However, less research attention has been focused on multi-class active learning. In this paper, we propose a novel Gaussian process classifier model with multiple annotators for multi-class visual recognition. Expectation propagation (EP) is adopted for efficient approximate Bayesian inference of our probabilistic model for classification. Based on the EP approximation inference, a generalized Expectation Maximization (GEM) algorithm is derived to estimate both the parameters for instances and the quality of each individual annotator. Also, we incorporate the idea of reinforcement learning to actively select both the informative samples and the high-quality annotators, which better explores the trade-off between exploitation and exploration. The experiments clearly demonstrate the efficacy of the proposed model.
基于鲁棒高斯过程的多类多标注器主动学习视觉识别
在视觉识别的许多应用中,主动学习是一种有效的方法,可以减轻手工标注的繁琐工作。然而,对多班级主动学习的研究较少。本文提出了一种具有多标注器的高斯过程分类器模型,用于多类视觉识别。采用期望传播(EP)对分类概率模型进行有效的近似贝叶斯推理。在EP近似推理的基础上,推导出一种广义期望最大化(GEM)算法来估计实例的参数和每个注释器的质量。此外,我们还结合了强化学习的思想来主动选择信息丰富的样本和高质量的注释器,从而更好地探索了开发和探索之间的权衡。实验清楚地证明了该模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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