Title: PATCH-MIX TRANSFORMER FOR UNSUPERVISED DOMAIN ADAPTATION: A GAME PERSPECTIVE. Abstract: Endeavors have been recently made to leverage the vision transformer (ViT) for the challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross-attention in ViT for direct domain alignment. However, as the performance of cross-attention highly relies on the quality of pseudo labels for the targeted samples, it becomes less effective when the domain gap becomes larger. We solve this problem from a game theory’s perspective with the model called PMTrans, which bridges the source and the target domains with an intermediate domain. Specifically, we propose a novel ViT-based module called PatchMix that effectively builds up the intermediate domain, i.e., probability distribution, by learning to sample patches from both domains based on the game-theoretical models. In this way, it learns to mix the patches from source and target domains to maximize the cross entropy (CE), while exploiting two semi-supervised mixup losses in the feature and label spaces to minimize CE. As such, we interpret the process of UDA as a min-max CE game with three players, including the feature extractor, classifier, and PatchMix, to find the optimal Nash Equilibria solution. Moreover, we leverage attention maps from ViT to re-weight the label of each patch by its importance, making it possible to obtain more domain-discriminative feature representations. We conduct extensive experiments on four benchmark datasets, and the results show that PMTrans significantly surpasses the ViT-based and CNN-based SoTA methods by +1.4% on Office-31, +3.5% on Office-Home, and +17.7% on DomainNet, respectively.