Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/1506.06724
Summary
Novels contain a great deal of visual and psychological detail, but that text is not linked to any imagery. Captions in existing vision-language datasets, by contrast, are short and literal. This work aligns books with their film adaptations at a fine level, matching individual movie shots and subtitle lines to sentences or paragraphs in the source book. The goal is to produce rich, story-level descriptions of video content.
The authors measure text similarity with a sentence encoder trained without supervision on a large new collection of free books (BookCorpus). For visual similarity, they adapt image-sentence embeddings to video and train them on DVS clip descriptions. A context-aware CNN combines these similarity signals with information from neighbouring shots and sentences. The final alignment is posed as an energy minimization that favours matches following a consistent timeline.
The method was evaluated on a newly annotated MovieBook dataset and showed good quantitative alignment performance. The authors also demonstrate book retrieval and story-like captioning of CoCo images. The work matters because it supplies large-scale grounded data for vision-language research, and BookCorpus later became a standard pretraining corpus.
Key points
- MovieBook dataset: 11 movie/book pairs, including Gone Girl, Fight Club and The Green Mile, with 2,070 ground-truth shot-to-sentence correspondences tagged as visual, dialog or audio matches. Annotation took about 90 hours.
- BookCorpus: 11,038 free books by unpublished authors, each longer than 20K words, spanning 16 genres. It contains about 74M sentences, about 985M words and 1.3M unique words, and it was used to train the sentence embedding.
- Visual-text similarity: a video-sentence neural embedding trained on DVS descriptions from the Movie Description dataset.
- Model: a context-aware CNN fuses several similarity measures, and an energy-minimization step enforces timeline consistency.
- Unlike earlier work: previous approaches aligned chapters to scenes or plot synopses to shots using hand-crafted similarity functions. This work aligns at the sentence/paragraph level using learned embeddings.
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Cite
@misc{zhu2015aligning,
title = {{Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books}},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Richard and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
year = {2015},
doi = {10.48550/arxiv.1506.06724},
eprint = {1506.06724},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.1506.06724}
}References
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