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E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13695))

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Abstract

Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. The key reason of this phenomenon is the coupled formulation of NeRV, which outputs the spatial and temporal information of video frames directly from the frame index input. In this paper, we propose E-NeRV, which dramatically expedites NeRV by decomposing the image-wise implicit neural representation into separate spatial and temporal context. Under the guidance of this new formulation, our model greatly reduces the redundant model parameters, while retaining the representation ability. We experimentally find that our method can improve the performance to a large extent with fewer parameters, resulting in a more than \(8\times \) faster speed on convergence. Code is available at https://github.com/kyleleey/E-NeRV.

Z. Li and M. Wang—Equal contributions.

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Acknowledgements

We thank all authors and reviewers for the contributions. This work is supported by a Grant from the National Natural Science Foundation of China (No. U21A20484).

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Authors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Zizhang Li, Mengmeng Wang, Huaijin Pi, Kechun Xu, Jianbiao Mei & Yong Liu

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  1. Zizhang Li
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  2. Mengmeng Wang
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  3. Huaijin Pi
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  6. Yong Liu
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Correspondence to Yong Liu .

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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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Li, Z., Wang, M., Pi, H., Xu, K., Mei, J., Liu, Y. (2022). E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context. 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 13695. Springer, Cham. https://doi.org/10.1007/978-3-031-19833-5_16

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