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Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12363))

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Abstract

To understand movies, humans constantly reason over the dialogues and actions shown in specific scenes and relate them to the overall storyline already seen. Inspired by this behaviour, we design ROLL, a model for knowledge-based video story question answering that leverages three crucial aspects of movie understanding: dialog comprehension, scene reasoning, and storyline recalling. In ROLL, each of these tasks is in charge of extracting rich and diverse information by 1) processing scene dialogues, 2) generating unsupervised video scene descriptions, and 3) obtaining external knowledge in a weakly supervised fashion. To answer a given question correctly, the information generated by each inspired-cognitive task is encoded via Transformers and fused through a modality weighting mechanism, which balances the information from the different sources. Exhaustive evaluation demonstrates the effectiveness of our approach, which yields a new state-of-the-art on two challenging video question answering datasets: KnowIT VQA and TVQA+.

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Notes

  1. 1.

    ‘Do androids dream of electric sheep?’ (Philip K. Dick, 1968).

  2. 2.

    https://www.wikipedia.org/.

  3. 3.

    https://www.imdb.com/.

  4. 4.

    For example, https://bigbangtrans.wordpress.com/.

  5. 5.

    Boy, girl, guy, lady, man, person, player, woman.

  6. 6.

    For example, https://the-big-bang-theory.com/.

  7. 7.

    Generating video plot summaries automatically from the whole video story is a challenging task by itself and out of the scope of this work. However, it is an interesting problem that we aim to study as a our future work.

  8. 8.

    In The Big Bang Theory, the longest summary contains 1,605 words.

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Acknowledgement

This work was supported by a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO), and JSPS KAKENHI Nos. 18H03264 and 20K19822. We also would like to thank the anonymous reviewers for they insightful comments to improve the paper.

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Authors and Affiliations

  1. Osaka University, Suita, Japan

    Noa Garcia & Yuta Nakashima

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  1. Noa Garcia
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  2. Yuta Nakashima
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Correspondence to Noa Garcia .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Garcia, N., Nakashima, Y. (2020). Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12363. Springer, Cham. https://doi.org/10.1007/978-3-030-58523-5_34

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