Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private category is set as the unknown class during the adaptation process, as it is not included in the source domain. However, most existing methods overlook the intra-class structure within a category, especially in cases where there exists significant concept shift between the samples belonging to the same category. When samples with large concept shift are forced to be pushed together, it may negatively affect the adaptation performance. Moreover, from the interpretability aspect, it is unreasonable to align visual features with significant differences, such as fighter jets and civil aircraft, into the same category. Unfortunately, due to such semantic ambiguity and annotation cost, categories are not always classified in detail, making it difficult for the model to perform precise adaptation. To address these issues, we propose a novel Memory-Assisted Sub-Prototype Mining (MemSPM) method that can learn the differences between samples belonging to the same category and mine sub-classes when there exists significant concept shift between them. By doing so, our model learns a more reasonable feature space that enhances the transferability and reflects the inherent differences among samples annotated as the same category. We evaluate the effectiveness of our MemSPM method over multiple scenarios, including UniDA, OSDA, and PDA. Our method achieves state-of-the-art performance on four benchmarks in most cases.

翻译：通用域自适应的目标是使源域和目标域中相同类别的样本对齐，并缩小其特征差异。在自适应过程中，目标域中的私有类别被视为未知类，因为这些类别在源域中并未包含。然而，现有方法大多忽略了类别内部的子结构，特别是当属于同一类别的样本之间存在显著概念偏移时更是如此。当具有较大概念偏移的样本被强制聚合在一起时，可能会对自适应性能产生负面影响。此外，从可解释性角度而言，将存在显著差异的视觉特征（如战斗机和民用飞机）归为同一类别进行对齐是不合理的。不幸的是，由于这种语义模糊性和标注成本，类别并不总是被详细划分，这使得模型难以进行精确的自适应。为解决这些问题，我们提出了一种新颖的记忆辅助子原型挖掘（MemSPM）方法，该方法能够学习属于同一类别样本之间的差异，并在样本之间存在显著概念偏移时自动挖掘子类。通过这种方式，我们的模型学习到一个更合理的特征空间，既增强了可迁移性，又反映了被标注为同一类别的样本之间的内在差异。我们在多种场景下评估了MemSPM方法的有效性，包括UniDA、OSDA和PDA。在四个基准数据集上的大多数情况下，我们的方法均取得了最先进的性能。