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Books are a rich source of both fine-grained information, how a character, anobject or a scene looks like, as well as high-level semantics, what someone isthinking, feeling and how these states evolve through a story. This paper aimsto align books to their movie releases in order to provide rich descriptiveexplanations for visual content that go semantically far beyond the captionsavailable in current datasets. To align movies and books we exploit a neuralsentence embedding that is trained in an unsupervised way from a large corpusof books, as well as a video-text neural embedding for computing similaritiesbetween movie clips and sentences in the book. We propose a context-aware CNNto combine information from multiple sources. We demonstrate good quantitativeperformance for movie/book alignment and show several qualitative examples thatshowcase the diversity of tasks our model can be used for.

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books