In real-world scenarios,few-shot unsupervised domain adaptation(FUDA)faces the dual challenges of limited source supervision and poor target generalization due to the extremely scarce annotated source samples.Existing...In real-world scenarios,few-shot unsupervised domain adaptation(FUDA)faces the dual challenges of limited source supervision and poor target generalization due to the extremely scarce annotated source samples.Existing methods often overlook the restricted learning capacity caused by sparse source labels or fail to effectively utilize the structural information within the target domain to enhance discriminative performance.To address these issues,we propose a novel method,Collaborative Pseudo-label Transfer(CPLT),which jointly improves cross-domain adaptation under few-shot UDA settings.CPLT comprises two key components:a Pseudo-label Guided Source Augmentation(PGSA)mechanism that iteratively selects high-confidence target samples to augment the source domain and strengthen initial representation learning,and a Target-aware Discriminative Modeling(TADM)that leverages pseudo-labeled target data to construct auxiliary classifiers for enhanced inter-class discrimination and reduced misclassification under domain shift.Experiments on three widely used FUDA benchmarks validate the superior performance of CPLT,achieving average accuracy gains of+3.5%on Office-31,+1.4%on Office-Home,and+1.0%on DomainNet over competitive existing methods.展开更多
基金supported by the Science and Technology Project of Qinghai Province(No.2023-QY-208).
文摘In real-world scenarios,few-shot unsupervised domain adaptation(FUDA)faces the dual challenges of limited source supervision and poor target generalization due to the extremely scarce annotated source samples.Existing methods often overlook the restricted learning capacity caused by sparse source labels or fail to effectively utilize the structural information within the target domain to enhance discriminative performance.To address these issues,we propose a novel method,Collaborative Pseudo-label Transfer(CPLT),which jointly improves cross-domain adaptation under few-shot UDA settings.CPLT comprises two key components:a Pseudo-label Guided Source Augmentation(PGSA)mechanism that iteratively selects high-confidence target samples to augment the source domain and strengthen initial representation learning,and a Target-aware Discriminative Modeling(TADM)that leverages pseudo-labeled target data to construct auxiliary classifiers for enhanced inter-class discrimination and reduced misclassification under domain shift.Experiments on three widely used FUDA benchmarks validate the superior performance of CPLT,achieving average accuracy gains of+3.5%on Office-31,+1.4%on Office-Home,and+1.0%on DomainNet over competitive existing methods.
