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Visual narrative is often a combination of explicit information and judiciousomissions, relying on the viewer to supply missing details. In comics, mostmovements in time and space are hidden in the "gutters" between panels. Tofollow the story, readers logically connect panels together by inferring unseenactions through a process called "closure". While computers can now describewhat is explicitly depicted in natural images, in this paper we examine whetherthey can understand the closure-driven narratives conveyed by stylized artworkand dialogue in comic book panels. We construct a dataset, COMICS, thatconsists of over 1.2 million panels (120 GB) paired with automatic textboxtranscriptions. An in-depth analysis of COMICS demonstrates that neither textnor image alone can tell a comic book story, so a computer must understand bothmodalities to keep up with the plot. We introduce three cloze-style tasks thatask models to predict narrative and character-centric aspects of a panel givenn preceding panels as context. Various deep neural architectures underperformhuman baselines on these tasks, suggesting that COMICS contains fundamentalchallenges for both vision and language.

The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives