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：通用域自适应旨在对齐源域和目标域中相同类别的特征并减小类别间的特征差距。在自适应过程中，目标域私有类别被设定为未知类，因为该类别未包含在源域中。然而，现有方法大多忽略了类别内部的子结构，尤其是当属于同一类别的样本间存在显著概念偏移时。若强制将具有较大概念偏移的样本聚合到同一类别，可能对自适应性能产生负面影响。此外，从可解释性角度来看，将视觉特征差异显著的样本（如战斗机与民用飞机）对齐到同一类别是不合理的。由于此类语义模糊性和标注成本，类别往往难以被精细划分，导致模型难以进行精准的自适应。为解决这些问题，我们提出一种新颖的记忆辅助子原型挖掘（MemSPM）方法，该方法能够学习同一类别内样本间的差异，并在样本间存在显著概念偏移时挖掘子类别。通过这种方式，我们的模型学习到更合理的特征空间，不仅增强了可迁移性，还反映了被标注为同一类别的样本间的固有差异。我们在多种场景（包括UniDA、OSDA和PDA）下评估了MemSPM方法的有效性。在大多数情况下，我们的方法在四个基准数据集上达到了当前最优性能。