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MovieCuts: A New Dataset and Benchmark for Cut Type Recognition

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13667))

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Abstract

Understanding movies and their structural patterns is a crucial task in decoding the craft of video editing. While previous works have developed tools for general analysis, such as detecting characters or recognizing cinematography properties at the shot level, less effort has been devoted to understanding the most basic video edit, the Cut. This paper introduces the Cut type recognition task, which requires modeling multi-modal information. To ignite research in this new task, we construct a large-scale dataset called MovieCuts, which contains 173, 967 video clips labeled with ten cut types defined by professionals in the movie industry. We benchmark a set of audio-visual approaches, including some dealing with the problem’s multi-modal nature. Our best model achieves \(47.7\%\) mAP, which suggests that the task is challenging and that attaining highly accurate Cut type recognition is an open research problem. Advances in automatic Cut-type recognition can unleash new experiences in the video editing industry, such as movie analysis for education, video re-editing, virtual cinematography, machine-assisted trailer generation, machine-assisted video editing, among others. Our data and code are publicly available: https://github.com/PardoAlejo/MovieCuts.

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Notes

  1. 1.

    MovieClips YouTube Channel is the source of scenes in MovieCuts.

  2. 2.

    EditStock.com.

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Acknowledgements

This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the Visual Computing Center (VCC) funding.

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Authors and Affiliations

  1. King Abdullah University of Science and Technology, KAUST, Thuwal, Saudi Arabia

    Alejandro Pardo, Juan León Alcázar, Ali Thabet & Bernard Ghanem

  2. Adobe Research, San Jose, USA

    Fabian Caba Heilbron

  3. Facebook Reality Labs, Redmond, USA

    Ali Thabet

Authors
  1. Alejandro Pardo
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  2. Fabian Caba Heilbron
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  3. Juan León Alcázar
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  4. Ali Thabet
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  5. Bernard Ghanem
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Corresponding author

Correspondence to Alejandro Pardo .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Pardo, A., Heilbron, F.C., Alcázar, J.L., Thabet, A., Ghanem, B. (2022). MovieCuts: A New Dataset and Benchmark for Cut Type Recognition. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_39

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