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The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsuperviseddomain adaptation (UDA) methods that can deal with both input distributionshift and label set variance between the source and target domains. In thisreport, we introduce a universal domain adaptation (UniDA) method byaggregating several popular feature extraction and domain adaptation schemes.First, we utilize VOLO, a Transformer-based architecture with state-of-the-artperformance in several visual tasks, as the backbone to extract effectivefeature representations. Second, we modify the open-set classifier of OVANet torecognize the unknown class with competitive accuracy and robustness. As shownin the leaderboard, our proposed UniDA method ranks the 2nd place with 48.56%ACC and 70.72% AUROC in the VisDA 2021 Challenge.

2nd Place Solution for VisDA 2021 Challenge -- Universally Domain Adaptive Image Recognition