Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
<|reference_start|>Recent Advances of Multimodal Continual Learning: A Comprehensive Survey: Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As machine learning 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 challenge of MMCL is that it goes beyond a simple stacking of unimodal CL methods, as such straightforward approaches often yield unsatisfactory performance. 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 existing MMCL methods into four categories, i.e., regularization-based, architecture-based, replay-based, and prompt-based methods, explaining their methodologies and highlighting their key innovations. Additionally, to prompt further research in this field, we summarize open MMCL datasets and benchmarks, and discuss several promising future directions for investigation and development. We have also created a GitHub repository for indexing relevant MMCL papers and open resources available at https://github.com/LucyDYu/Awesome-Multimodal-Continual-Learning.<|reference_end|>
@article{yu2024recent,
    title={Recent Advances of Multimodal Continual Learning: A Comprehensive Survey},
    author={Dianzhi Yu, Xinni Zhang, Yankai Chen, Aiwei Liu, Yifei Zhang, Philip
  S. Yu, Irwin King},
    journal={arXiv preprint arXiv:2410.05352},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05352},
    primaryClass={cs.LG cs.AI}
}