A Novel Multiple-View Adversarial Learning Network for Unsupervised Domain Adaptation Action Recognition

Abstract-domain adaptation action recognition is a hot research topic in machine learning and some effective approaches have been proposed. However, samples in the target domain with label information are often required by these approaches. Moreover, domain-invariant discriminative feature learning, feature fusion, and classifier module learning have not been explored in an end-to-end framework. Thus, in this study, we propose a novel end-to-end multiple-view adversarial learning network (MAN) for unsupervised domain adaptation action recognition in which the fusion of RGB and optical-flow features, domain-invariant discrimination feature learning, and action recognition is conducted in a unified framework. Specifically, a robust spatiotemporal feature extraction network, including a spatial transform network and an adaptive intrachannel weight network, is proposed to improve the scale invariance and robustness of the method. Then, a self-attention mechanism fusion module is designed to adaptively fuse the RGB and optical-flow features. Moreover, a multiview adversarial learning loss is developed to obtain domain-invariant discriminative features. In addition, three benchmark datasets are constructed for unsupervised domain adaptation action recognition, for which all actions and samples are carefully collected from public action datasets, and their action categories are hierarchically augmented, which can guide how to extend existing action datasets. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate that our proposed MAN can outperform several state-of-the-art unsupervised domain adaptation action recognition approaches. When the SDAI Action II-6 and SDAI Action II-11 datasets are used, MAN can achieve 3.7% ( H → U ) and 6.1% ( H → U ) improvements over the temporal attentive adversarial adaptation network (published in ICCV 2019) module, respectively. As an added contribution, the SDAI Action II-6, SDAI Action II-11, and SDAI Action II-16 datasets will be released to facilitate future research on domain adaptation action recognition.