Learning from Variation for Open-Domain Few-Shot Class-Incremental Learning

Few-Shot Class-Incremental Learning (FSCIL) aims to continually acquire novel classes from limited examples while retaining previously learned knowledge. However, existing approaches typically assume that class increments occur within a fixed real-world domain, overlooking domain variations in open-world environments. In this work, we introduce Open-Domain Few-Shot Class-Incremental Learning (OD-FSCIL), where incremental samples may arise from heterogeneous domains whose composition is not predefined. Such domain heterogeneity makes intra-class variations difficult to capture from few-shot observations and complicates joint recognition of all seen classes. To address these challenges, we propose Learning from Variation (LeVa), which leverages the rich intra-class variations of data-rich base classes as prior knowledge for novel classes. LeVa comprises Variation Recovery (VR) and Variation Exploitation (VE). VR transfers historical intra-class variations to novel classes and uses predicted distributional statistics to retain those compatible with each novel class. VE exploits the recovered variations to model class-specific structures around text anchors, enabling more effective joint recognition of both old and new classes. To facilitate evaluation under open-domain conditions, we further construct Hybrid-miniImageNet, a new benchmark covering diverse visual domains. Extensive experiments on Hybrid-miniImageNet, DomainNet, miniImageNet, and CIFAR100 demonstrate the effectiveness of LeVa under both open-domain and standard FSCIL settings.