  Multi-modal learning has emerged as an increasingly promising avenue in
vision recognition, driving innovations across diverse domains ranging from
media and education to healthcare and transportation. Despite its success, the
robustness of multi-modal learning for visual recognition is often challenged
by the unavailability of a subset of modalities, especially the visual
modality. Conventional approaches to mitigate missing modalities in multi-modal
learning rely heavily on algorithms and modality fusion schemes. In contrast,
this paper explores the use of text-to-image models to assist multi-modal
learning. Specifically, we propose a simple but effective multi-modal learning
framework GTI-MM to enhance the data efficiency and model robustness against
missing visual modality by imputing the missing data with generative
transformers. Using multiple multi-modal datasets with visual recognition
tasks, we present a comprehensive analysis of diverse conditions involving
missing visual modality in data, including model training. Our findings reveal
that synthetic images benefit training data efficiency with visual data missing
in training and improve model robustness with visual data missing involving
training and testing. Moreover, we demonstrate GTI-MM is effective with lower
generation quantity and simple prompt techniques.
