  Domain generalizability is a crucial aspect of a deep learning model since it
determines the capability of the model to perform well on data from unseen
domains. However, research on the domain generalizability of deep learning
models for vision-language tasks remains limited, primarily because of the lack
of required datasets. To address these challenges, we propose VolDoGer:
Vision-Language Dataset for Domain Generalization, a dedicated dataset designed
for domain generalization that addresses three vision-language tasks: image
captioning, visual question answering, and visual entailment. We constructed
VolDoGer by extending LLM-based data annotation techniques to vision-language
tasks, thereby alleviating the burden of recruiting human annotators. We
evaluated the domain generalizability of various models, ranging from
fine-tuned models to a recent multimodal large language model, through
VolDoGer.
