Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
Name
Torralba_Aligning books.pdf
Size
8.2 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Zhu, Yukun
Kiros, Ryan
Zemel, Rich
Salakhutdinov, Ruslan
Urtasun, Raquel
Torralba, Antonio
Fidler, Sanja
Date Issued
February 2016
Journal
2015 IEEE International Conference on Computer Vision (ICCV)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhu, Yukun, et al. "Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books." 2015 IEEE International Conference on Computer Vision (ICCV), 7-13 December, 2015, Santiago, Chile, IEEE, 2015, pp. 19–27.
Version
Original manuscript
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 paper 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 the 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICCV.2015.11
