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home / papers / 2015 / Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

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  • BookCorpus: 11,038 free books by unpublished authors, each longer than 20K words, spanning 16 genres.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.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

814 papers of our library cite it (of the 140673 whose bibliographies we read); 68 of them have a page here, 746 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.1506.06724 · arXiv 1506.06724

@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

38 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. ICLR, 2015. 4 find our card
  2. K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. EMNLP, 2014. 4 find
  3. J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014. 4 find
  4. T. Cour, C. Jordan, E. Miltsakaki, and B. Taskar. Movie/script: Alignment and parsing of video and text transcription. In ECCV, 2008. 2 find
  5. M. Everingham, J. Sivic, and A. Zisserman. “Hello! My name is... Buffy” – Automatic Naming of Characters in TV Video. BMVC, pages 899–908, 2006. 2 find
  6. A. Farhadi, M. Hejrati, M. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth. Every picture tells a story: Generating sentences for images. In ECCV, 2010. 2 find
  7. S. Fidler, A. Sharma, and R. Urtasun. A sentence is worth a thousand pixels. In CVPR, 2013. 2 find
  8. A. Gupta and L. Davis. Beyond nouns: Exploiting prepositions and comparative adjectives for learning visual classifiers. In ECCV, 2008. 1 find
  9. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. 4 find
  10. N. Kalchbrenner and P. Blunsom. Recurrent continuous translation models. In EMNLP, pages 1700–1709, 2013. 4 find
  11. A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In CVPR, 2015. 1, 2 find our card
  12. D. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 5 find
  13. R. Kiros, R. Salakhutdinov, and R. S. Zemel. Unifying visual-semantic embeddings with multimodal neural language models. CoRR, abs/1411.2539, 2014. 1, 2, 3, 5, 9, 10 find our card
  14. R. Kiros, Y. Zhu, R. Salakhutdinov, R. S. Zemel, A. Torralba, R. Urtasun, and S. Fidler. Skip-Thought Vectors. In Arxiv, 2015. 3, 4 find
  15. C. Kong, D. Lin, M. Bansal, R. Urtasun, and S. Fidler. What are you talking about? text-to-image coreference. In CVPR, 2014. 1, 2 find
  16. G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. Berg, and T. Berg. Baby talk: Understanding and generating simple image descriptions. In CVPR, 2011. 2 find
  17. D. Lin, S. Fidler, C. Kong, and R. Urtasun. Visual Semantic Search: Retrieving Videos via Complex Textual Queries. CVPR, pages 2657–2664, 2014. 1, 2 find
  18. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft coco: Common objects in context. In ECCV, pages 740–755. 2014. 1, 19 find our card
  19. X. Lin and D. Parikh. Don’t just listen, use your imagination: Leveraging visual common sense for non-visual tasks. In CVPR, 2015. 1 find
  20. M. Malinowski and M. Fritz. A multi-world approach to question answering about real-world scenes based on uncertain input. In NIPS, 2014. 1 find
  21. J. Mao, W. Xu, Y. Yang, J. Wang, and A. L. Yuille. Explain images with multimodal recurrent neural networks. In arXiv:1410.1090, 2014. 1, 2 — [arXiv:1410.1090](https://arxiv.org/abs/1410.1090) find
  22. T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. 4 find
  23. K. Papineni, S. Roukos, T. Ward, and W. J. Zhu. BLEU: a method for automatic evaluation of machine translation. In ACL, pages 311–318, 2002. 6 find our card
  24. H. Pirsiavash, C. Vondrick, and A. Torralba. Inferring the why in images. arXiv.org, jun 2014. 2 find
  25. V. Ramanathan, A. Joulin, P. Liang, and L. Fei-Fei. Linking People in Videos with “Their” Names Using Coreference Resolution. In ECCV, pages 95–110. 2014. 2 find
  26. V. Ramanathan, P. Liang, and L. Fei-Fei. Video event understanding using natural language descriptions. In ICCV, 2013. 1 find
  27. A. Rohrbach, M. Rohrbach, N. Tandon, and B. Schiele. A dataset for movie description. In CVPR, 2015. 2, 5 find
  28. P. Sankar, C. V. Jawahar, and A. Zisserman. Subtitle-free Movie to Script Alignment. In BMVC, 2009. 2 find
  29. A. Schwing, T. Hazan, M. Pollefeys, and R. Urtasun. Efficient Structured Prediction with Latent Variables for General Graphical Models. In ICML, 2012. 6 find
  30. J. Sivic, M. Everingham, and A. Zisserman. “Who are you?” - Learning person specific classifiers from video. CVPR, pages 1145–1152, 2009. 2 find
  31. I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In NIPS, 2014. 4 find
  32. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014. 5 — [arXiv:1409.4842](https://arxiv.org/abs/1409.4842) find
  33. M. Tapaswi, M. Bauml, and R. Stiefelhagen. Book2Movie: Aligning Video scenes with Book chapters. In CVPR, 2015. 2 find
  34. M. Tapaswi, M. Buml, and R. Stiefelhagen. Aligning Plot Synopses to Videos for Story-based Retrieval. IJMIR, 4:3– 16, 2015. 1, 2, 6 find
  35. S. Venugopalan, H. Xu, J. Donahue, M. Rohrbach, R. J. Mooney, and K. Saenko. Translating Videos to Natural Language Using Deep Recurrent Neural Networks. CoRR abs/1312.6229, cs.CV, 2014. 1, 2 find
  36. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. In arXiv:1411.4555, 2014. 1, 2 find our card
  37. K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. Zemel, and Y. Bengio. Show, attend and tell: Neural image caption generation with visual attention. In arXiv:1502.03044, 2015. 2 find our card
  38. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning Deep Features for Scene Recognition using Places Database. In NIPS, 2014. 5, 7 find