摘要
Class imbalance presents a persistent bottleneck in machine learning, biasing models toward majority classes and degrading performance on rare events. Active learning partially alleviates this by focusing annotation efforts on underrepresented samples. However, this issue becomes more pronounced in multi-domain learning settings, where both intra-domain imbalances and shifts across domains complicate model training. Current multi-domain active learning techniques typically neglect the interaction of these dual imbalances, resulting in biased selection strategies and limited ability to generalize across domains. To tackle this, we introduce a comprehensive framework featuring a dynamic category-scaling weight mechanism that hierarchically addresses imbalance at the sample and domain levels. This mechanism assesses the relative difficulty of each class to guide effective sampling, while concurrently constructing a progressively refined “Ideal Domain” through the adaptive integration of well-performing domains. This ideal representation further supports a minimax optimization process to allocate annotation budgets both fairly and robustly. Extensive evaluations on various standard datasets confirm that our approach consistently surpasses leading methods in managing complex imbalanced scenarios across multiple domains.