Multimodality in Meta-Learning: A Comprehensive Survey
It addresses the problem of data efficiency and generalization in multimodal meta-learning for researchers, but it is incremental as a survey paper.
This survey tackles the lack of comprehensive study on meta-learning's generalization in multimodal tasks by providing an overview of methodologies and applications, including formalizing definitions, proposing a taxonomy, and suggesting future research directions.
Meta-learning has gained wide popularity as a training framework that is more data-efficient than traditional machine learning methods. However, its generalization ability in complex task distributions, such as multimodal tasks, has not been thoroughly studied. Recently, some studies on multimodality-based meta-learning have emerged. This survey provides a comprehensive overview of the multimodality-based meta-learning landscape in terms of the methodologies and applications. We first formalize the definition of meta-learning in multimodality, along with the research challenges in this growing field, such as how to enrich the input in few-shot learning (FSL) or zero-shot learning (ZSL) in multimodal scenarios and how to generalize the models to new tasks. We then propose a new taxonomy to discuss typical meta-learning algorithms in multimodal tasks systematically. We investigate the contributions of related papers and summarize them by our taxonomy. Finally, we propose potential research directions for this promising field.