[2410.05352] Recent Advances of Multimodal Continual Learning: A Comprehensive Survey

Computer Science > Machine Learning arXiv:2410.05352 (cs) [Submitted on 7 Oct 2024 (v1), last revised 28 Mar 2026 (this version, v3)] Title:Recent Advances of Multimodal Continual Learning: A Comprehensive Survey Authors:Dianzhi Yu, Xinni Zhang, Yankai Chen, Aiwei Liu, Yifei Zhang, Philip S. Yu, Irwin King View a PDF of the paper titled Recent Advances of Multimodal Continual Learning: A Comprehensive Survey, by Dianzhi Yu and 6 other authors View PDF Abstract:Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As models have evolved from small to large pre-trained architectures, and from supporting unimodal to multimodal data, multimodal continual learning (MMCL) methods have recently emerged. The primary complexity of MMCL is that it extends beyond a simple stacking of unimodal CL methods. Such straightforward approaches often suffer from multimodal catastrophic forgetting, yielding unsatisfactory performance. In addition, MMCL introduces new challenges that unimodal CL methods fail to adequately address, including modality imbalance, complex modality interaction, high computational costs, and degradation of pre-trained zero-shot capability of multimodal backbones. In this work, we present the first comprehensive survey on MMCL. We provide essential background knowledge and MMCL settings, as well as a structured taxonomy of MMCL methods. We categorize MMC...