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Hierarchical Visual-Textual Graph for Temporal Activity Localization via Language

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12365))

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Abstract

Temporal Activity Localization via Language (TALL) in video is a recently proposed challenging vision task, and tackling it requires fine-grained understanding of the video content, however, this is overlooked by most of the existing works. In this paper, we propose a novel TALL method which builds a Hierarchical Visual-Textual Graph to model interactions between the objects and words as well as among the objects to jointly understand the video contents and the language. We also design a convolutional network with cross-channel communication mechanism to further encourage the information passing between the visual and textual modalities. Finally, we propose a loss function that enforces alignment of the visual representation of the localized activity and the sentence representation, so that the model can predict more accurate temporal boundaries. We evaluated our proposed method on two popular benchmark datasets: Charades-STA and ActivityNet Captions, and achieved state-of-the-art performances on both datasets. Code is available at https://github.com/forwchen/HVTG.

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Notes

  1. 1.

    Due to the space limit, more experiments are placed in the Supplementary Material.

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Authors and Affiliations

  1. Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

    Shaoxiang Chen & Yu-Gang Jiang

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  1. Shaoxiang Chen
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  2. Yu-Gang Jiang
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Correspondence to Yu-Gang Jiang .

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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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Chen, S., Jiang, YG. (2020). Hierarchical Visual-Textual Graph for Temporal Activity Localization via Language. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_36

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  • DOI: https://doi.org/10.1007/978-3-030-58565-5_36

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  • Online ISBN: 978-3-030-58565-5

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