Causal Intervention-Based Multimodal Class-Incremental Learning Network for 3D Model Classification and Retrieval

Existing class-incremental learning methods have achieved significant progress in unimodal domains, but they face severe challenges of catastrophic forgetting and cross-modal interference when handling multimodal data. We propose a causal intervention-based multimodal class-incremental learning framework to address the stability-plasticity trade-off in 3D model classification and retrieval tasks. By employing backdoor adjustment-based causal intervention, our approach decouples historical task biases from new knowledge acquisition. It introduces an alternating modality freezing strategy to isolate parameter updates between geometric (point cloud) and visual (multi-view) modalities, thereby suppressing cross-modal gradient interference. Furthermore, a dynamic feature anchoring loss is utilized to constrain feature space drift and maintain cross-task consistency. Experimental results on the ModelNet40 and ShapeNet-Core55 datasets demonstrate that our method achieves state-of-the-art performance compared to competitive baselines, confirming the effectiveness of multimodal redundancy in mitigating unimodal degradation. This work provides theoretical support and technical implementation pathways for lifelong learning systems in complex 3D scenarios.