Class-Customized Domain Adaptation: Unlock Each Customer-Specific Class With Single Annotation

Model customization mitigates the issues of inadequate performance, resource wastage, and privacy risks associated with using general-purpose models in specialized domains and well-defined tasks. However, achieving customization at a low annotation cost still poses a challenge. Existing domain adaptation research has addressed cases where all customized classes are present in the labeled database, yet scenarios involving customer-specific classes are still unresolved. Therefore, this paper proposes a novel Class-Customized Domain Adaptation (CCDA) method, addressing the latter scenario with just one additional annotation for each customer-specific class. CCDA adopts the classic adaptation training framework and comprises two innovative techniques. Firstly, to ensure the shared class knowledge from the database and the private class knowledge from additional annotations are transferred and propagated to the correct regions within the target domain, we design the partial-feature alignment strategy, based on the mechanical properties of feature alignment. Second, we propose soft-balanced sampling to tackle the long-tail distribution problem in labeled data, preventing the model from overfitting to the labeled samples of customer-specific classes. The effectiveness of CCDA has been validated across 48 tasks simulated on domain adaptation benchmarks and two real-world customization scenarios, consistently showing excellent performance. Additionally, extensive analytical experiments illustrate the contributions of two innovative techniques. The code is available at https://github.com/CHEN-kx/ClassCustomizedDA.