Uncertainty Feature Learning With Personalized Class Description for Visual Domain Adaptation

Unsupervised domain adaptation (UDA) is widely utilized to accomplish the visual object recognition task in the field of consumer electronics, aiming at reducing labeling costs in new scenarios. To address the error accumulation problem caused by unreliable target pseudo labels, existing feature-based UDA methods utilize the predicted posterior probability of target sample to model the uncertainty relationship between samples. However, due to differences in data characteristics of each class, unified uncertainty modeling may lead to information loss in some classes due to excessive fuzziness. In view of this, this paper proposes a uncertainty feature learning method with personalized class description for UDA (UFL-PCD) to accomplish the visual object recognition task. Firstly, to learn a more robust discriminative common feature subspace, the proposed method performs more flexible uncertainty modeling of relationships between intra-domain and cross-domain samples based on the personalized uncertainty description of each class. Secondly, to alleviate the error accumulation problem, a hybrid label filtering strategy is proposed to determine the credible target pseudo labels based on fuzzy membership degrees and adaptive rough approximations. Furthermore, a consensus prototype-driven prediction model is designed to predict target data by comprehensively considering the guidance information of the source domain and the intrinsic structural information of the target domain. Experiments conducted on four benchmark datasets have demonstrated that UFL-PCD can construct a common feature subspace with strong stability and adaptability through uncertainty feature learning.