Inter-Domain Intra-Class Knowledge Complement for Long-Tail Domain Incremental Learning

Domain Incremental Learning (DIL) aims to learn from a continuous stream of domain-specific data. However, existing DIL approaches predominantly assume class-balanced distributions, overlooking realistic scenarios where data typically follow a long-tailed distribution. To bridge this gap, we investigate the more practical Long-Tailed Domain Incremental Learning (LDIL) problem, which introduces two major challenges: severe catastrophic forgetting of head classes and underrepresentation of tail classes. To address these issues, we propose a novel Inter-Domain Intra-Class Knowledge Complement (DC-Comp) approach that mines discriminative information from previously seen domains to alleviate distributional undersampling in tail classes of new domains and to mitigate the forgetting of head-class knowledge. Specifically, a Discriminativity Modeling Network associated with an Intra-class Balanced Discriminativity Mining mechanism is developed to capture class-specific local features in long-tailed data. In addition, an Inter-domain Discriminativity Reserving and Complementing scheme is introduced to retain these discriminative features and further leverage them to generate pseudo-samples during new domain training, effectively mitigating both distribution imbalance and catastrophic forgetting. Comprehensive experiments conducted under two practical LDIL settings, unified and random cross-domain distributions, demonstrate that DC-Comp achieves state-of-the-art performance. Our source code will be released.