Kavli Affiliate: Yi Zhou
| First 5 Authors: Yuxiang Lai, Yi Zhou, Xinghong Liu, Tao Zhou,
| Summary:
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.
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