LAUSR

Vehicle re-identification (Re-ID) methods often fail to achieve robust performance due to insufficient training data and domain diversities. Although state-of-the-art methods apply image-to-image translation or web data to achieve data augmentation, the construct of new datasets will not only introduce noise, but also undergo a mismatch issue with the source domain. Moreover, the label noise of cross-domain data in existing label distribution technologies cannot be alleviated. In this paper, a multi-domain joint learning with inter-domain adaptation label smoothing regularization (IALSR) is proposed using a semi-supervised learning framework. The overall framework consists of two parts. In one part, a multi-domain joint network (MJNet) is proposed to learn multiple vehicle attributes simultaneously. The output of the training model is employed to group several inter-domain subsets, which are regarded as different domains. To adapt to domain diversities, style transfer models are learned for each pair of subsets to generate free and rich data as a novel data augmentation approach. In the other part, IALSR, which preserves self-similarity and domain-transitivity, is designed to smooth the noise of style-transferred data. Upon our basis, we further introduce the web data to verify the superiority of the IALSR. The results of extensive experimental on two large-scale vehicle Re-ID datasets demonstrate that the proposed approach is superior to other state-of-the-art ones.