In this work, a supervised probabilistic approach is proposed that integrates the learning using privileged information (LUPI) paradigm into a hidden conditional random field (HCRF) model, called HCRF+, for human action recognition. The proposed model employs a self-training technique for automatic estimation of the regularization parameters of the objective function. Moreover, the method provides robustness to outliers by modeling the conditional distribution of the privileged information by a Student’s t-density function, which is naturally integrated into the HCRF+ framework. The proposed method was evaluated using different forms of privileged information on four publicly available datasets. The experimental results demonstrate its effectiveness concerning the state of the art in the LUPI framework using both hand-crafted and deep learning-based features extracted from a convolutional neural network.

在这项工作中，提出了一种监督概率方法，该方法将使用特权信息（LUPI）范式的学习整合到一个称为HCRF +的隐藏条件随机场（HCRF）模型中，用于人类动作识别。提出的模型采用一种自训练技术来自动估计目标函数的正则化参数。此外，该方法通过用学生的t建模特权信息的条件分布来为异常值提供鲁棒性-密度函数，它自然地集成到HCRF +框架中。在四个公开可用的数据集上使用不同形式的特权信息对提出的方法进行了评估。实验结果证明了从卷积神经网络提取的手工和基于深度学习的特征在LUPI框架中与现有技术有关的有效性。