C²BA: Cross-Domain Consistency and Bidirectional Alignment for Cross-Modal Domain-Incremental Learning

In Cross-Modal Domain-Incremental learning, the primary challenge lies in learning from varying data distributions and maintaining its performance on prior domains. However, existing methods often overlook the importance of shared knowledge across domains and the interaction between modalities is still insufficient. To address these issues, we propose Cross-Domain Consistency and Bidirectional Alignment (C²BA), a novel framework that enhances the model's generalization ability and improves the cross-modal integration in VLMs through two key components. We design a Cross-domain Global Consistency Constraint (CGCC) to stabilize domain-invariant representations during incremental training, preventing excessive shifts of shared distributions toward new domains. In addition, we design a Bidirectional Cross-Modal Attention (BCMA) module, which enables effective interaction between visual and textual features through a bidirectional attention mechanism, thereby reducing cross-modal discrepancies. Experiments on three benchmark datasets demonstrate that our method outperforms state-of-the-art exemplar-free and even exemplar-based approaches, achieving superior generalization and cross-modal interaction.