LANGUAGE-AWARE COMPLEMENTARY DECOMPOSITION FOR EXEMPLAR-FREE DOMAIN- INCREMENTAL LEARNING

Exemplar-free domain-incremental learning (DIL) requires models to continually adapt to new domains while retaining previously acquired knowledge without storing historical images. Although parameter isolation mitigates forgetting by preserving domain-specific knowledge in separate lightweight modules, effective inference still requires reliably identifying which knowledge should be invoked when domain identities are unavailable at test time. This is particularly challenging because class semantics and domain variations are entangled in visual representations, causing the evidence useful for recognition and domain selection to interfere with each other. Motivated by the insight that language semantics provide a natural reference for organizing these two types of information, we propose LaCoD, a Language-Aware Complementary Decomposition framework for exemplar-free DIL. LaCoD constructs a semantic subspace from frozen CLIP text embeddings and decomposes visual features into a language-aligned semantic component and a complementary residual. The residual retains domain-sensitive cues with reduced class-semantic interference and supports domain routing, which in turn selects suitable domain-specific predictors. The semantic component preserves domain-agnostic class evidence to complement the routed prediction. In this way, LaCoD assigns complementary roles to the two components, enabling more reliable knowledge invocation and robust classification. Extensive experiments on four DIL benchmarks demonstrate the effectiveness of LaCoD, consistently outperforming prior methods.