
Story-Like Visual Explanations by Watching Movies and Reading Books Using a CNN - polygot
https://arxiv.org/abs/1506.06724
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polygot
The title is a bit dense; a good example of what it does is available here:
[http://yknzhu.wixsite.com/mbweb](http://yknzhu.wixsite.com/mbweb) (takes in a
movie and its book form, and syncs them up and many other things.)

Abstract:

Books are a rich source of both fine-grained information, how a character, an
object or a scene looks like, as well as high-level semantics, what someone is
thinking, feeling and how these states evolve through a story. This work aims
to align books to their movie releases in order to provide rich descriptive
explanations for visual content that go semantically far beyond the captions
available in current datasets. To align movies and books we propose a neural
sentence embedding that is trained in an unsupervised way from a large corpus
of books, as well as a video-text neural embedding for computing similarities
between movie clips and sentences in the book. We propose a context-aware CNN
to combine information from multiple sources. We demonstrate good quantitative
performance for movie/book alignment and show several qualitative examples
that showcase the diversity of tasks our model can be used for.

