  Manga is a popular medium that combines stylized drawings and text to convey
stories. As manga panels differ from natural images, computational systems
traditionally had to be designed specifically for manga. Recently, the adaptive
nature of modern large multimodal models (LMMs) shows possibilities for more
general approaches. To provide an analysis of the current capability of LMMs
for manga understanding tasks and identifying areas for their improvement, we
design and evaluate MangaUB, a novel manga understanding benchmark for LMMs.
MangaUB is designed to assess the recognition and understanding of content
shown in a single panel as well as conveyed across multiple panels, allowing
for a fine-grained analysis of a model's various capabilities required for
manga understanding. Our results show strong performance on the recognition of
image content, while understanding the emotion and information conveyed across
multiple panels is still challenging, highlighting future work towards LMMs for
manga understanding.
