MCIGLE: Multimodal Exemplar-Free Class-Incremental Graph Learning

Exemplar-free class-incremental learning enables models to learn new classes over time without storing data from old ones. As mul-timodal graph-structured data becomes increasingly prevalent, existing methods struggle with challenges like catastrophic forgetting, distribution bias, memory limits, and weak generalization. We propose MCIGLE, a novel framework that addresses these issues by extracting and aligning multimodal graph features and applying Concatenated Recursive Least Squares for effective knowledge retention.