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：通用域适应旨在对齐源域和目标域中相同类别的类分布并减少特征差距。在适应过程中，目标域私有类别被设为未知类，因其未包含在源域中。然而，现有大多数方法忽略了类别内的细粒度结构，尤其在属于同一类别的样本之间存在显著概念偏移时。当具有较大概念偏移的样本被迫聚合时，可能对适应性能产生负面影响。此外，从可解释性角度看，将存在显著差异的视觉特征（如战斗机和民用飞机）对齐至同一类别是不合理的。遗憾的是，由于这种语义歧义性和标注成本，类别并非总能被精细划分，这使得模型难以实现精确的域适应。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，该方法能够学习同一类别内样本之间的差异，并在样本间存在显著概念偏移时挖掘子类。通过这种方式，我们的模型学习到更合理的特征空间，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。在四个基准测试中，我们的方法在大多数情况下达到了最先进的性能。