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

TL;DR

This paper investigates whether AI can understand comic book narratives by analyzing a large dataset of panels and introducing tasks that require integrating visual and textual information to infer story continuity.

Contribution

The paper introduces the COMICS dataset and proposes new multimodal tasks to evaluate AI understanding of comic narratives, highlighting current limitations.

Findings

Models underperform humans on narrative prediction tasks

Both text and image modalities are essential for understanding comics

COMICS dataset reveals fundamental challenges in multimodal narrative comprehension

Abstract

Visual narrative is often a combination of explicit information and judicious omissions, relying on the viewer to supply missing details. In comics, most movements in time and space are hidden in the "gutters" between panels. To follow the story, readers logically connect panels together by inferring unseen actions through a process called "closure". While computers can now describe what is explicitly depicted in natural images, in this paper we examine whether they can understand the closure-driven narratives conveyed by stylized artwork and dialogue in comic book panels. We construct a dataset, COMICS, that consists of over 1.2 million panels (120 GB) paired with automatic textbox transcriptions. An in-depth analysis of COMICS demonstrates that neither text nor image alone can tell a comic book story, so a computer must understand both modalities to keep up with the plot. We introduce three cloze-style tasks that ask models to predict narrative and character-centric aspects of a panel given n preceding panels as context. Various deep neural architectures underperform human baselines on these tasks, suggesting that COMICS contains fundamental challenges for both vision and language.