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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhu, Yukun Kiros, Ryan Zemel, Rich Salakhutdinov, Ruslan Urtasun, Raquel Torralba, Antonio Fidler, Sanja |
| Copyright Year | 2015 |
| 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. |
| Starting Page | 19 |
| Ending Page | 27 |
| File Size | 2457807 |
| Page Count | 9 |
| File Format | |
| ISSN | 23807504 |
| e-ISBN | 9781467383912 |
| DOI | 10.1109/ICCV.2015.11 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-12-07 |
| Publisher Place | Chile |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Motion pictures Visualization Videos Semantics Grounding Voltage control Roads |
| Content Type | Text |
| Resource Type | Article |
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