2015 AligningBooksandMoviesTowardsSt

• (Zhu et al., 2015) ⇒ Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. (2015). “Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books.” In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV 2015). DOI:10.1109/ICCV.2015.11.

Subject Headings: Took Book Corpus.

Notes

Resource(s)

• Website: https://yknzhu.wixsite.com/mbweb
• Github Repository: https://github.com/fastforwardlabs/skip-thoughts

Pre-Print(s):

• ArXiv: https://arxiv.org/abs/1506.06724
• CVF Open Access Articles : https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf
• MIT Open Access Articles: https://dspace.mit.edu/handle/1721.1/112996

Other Links:

• IEEE: https://ieeexplore.ieee.org/document/7410368
• DBLP: https://dblp.org/rec/conf/iccv/ZhuKZSUTF15
• ACM DL: https://dl.acm.org/doi/10.1109/ICCV.2015.11

Cited By

• Google Scholar: ~ 477 Citations.
• Semantic Scholar: ~ 498 Citations.
• MS Academic: ~ 415 Citations.

Quotes

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 current datasets. To align movies and books we exploit 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.

BibTex

@inproceedings{2015 AligningBooksandMoviesTowardsSt,
  author    = {Yukun Zhu and
               Ryan Kiros and
               [[Richard S. Zemel]] and
               Ruslan Salakhutdinov and
               Raquel Urtasun and
               Antonio Torralba and
               Sanja Fidler},
  title     = {Aligning Books and Movies: Towards Story-Like Visual Explanations
               by Watching Movies and Reading Books},
  booktitle = {2015 {IEEE} International Conference on Computer Vision, {ICCV} 2015,
               Santiago, Chile, December 7-13, 2015},
  pages     = {19--27},
  publisher = {{IEEE} Computer Society},
  year      = {2015},
  url       = {https://doi.org/10.1109/ICCV.2015.11},
  doi       = {10.1109/ICCV.2015.11},
}

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 AligningBooksandMoviesTowardsSt	Raquel Urtasun Ruslan Salakhutdinov Richard S. Zemel Ryan Kiros Yukun Zhu Antonio Torralba Sanja Fidler			Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books						2015