Label propagation with multi-stage inference for visual domain adaptation

Domain adaptation aims at leveraging rich label information in source domain and predicting on a related but different target domain, which makes significant progress in visual recognition. Most existing methods attempt to align domains but have limited considerations on the preservation of data structure, especially for unlabeled target data. In this paper, we present a novel solution for unsupervised domain adaptation based on label propagation. Specifically, we construct the affinity graph separately to capture both within-domain and cross-domain property. Inspired by the overlap exists between two domains, we propose multi-stage inference. Samples shared by two domains are expected to have more reliable labels, then they are picked to assist further predictions. Extensive experiments on three real-world datasets demonstrate that our method is comparable or superior to existing methods, we also provide some discussion about the interpretation of multi-stage inference.