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FlowFormer: A Transformer Architecture for Optical Flow

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13677))

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Abstract

We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow. FlowFormer tokenizes the 4D cost volume built from an image pair, encodes the cost tokens into a cost memory with alternate-group transformer (AGT) layers in a novel latent space, and decodes the cost memory via a recurrent transformer decoder with dynamic positional cost queries. On the Sintel benchmark, FlowFormer achieves 1.144 and 2.183 average end-ponit-error (AEPE) on the clean and final pass, a 17.6% and 11.6% error reduction from the best published result (1.388 and 2.47). Besides, FlowFormer also achieves strong generalization performance. Without being trained on Sintel, FlowFormer achieves 0.95 AEPE on the Sintel training set clean pass, outperforming the best published result (1.29) by 26.9%.

Z. Huang and X. Shi—Assert equal contributions.

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Acknowledgements

Hongsheng Li is also a Principal Investigator of Centre for Perceptual and Interactive Intelligence Limited (CPII). This work is supported in part by CPII, in part by the General Research Fund through the Research Grants Council of Hong Kong under Grants (Nos. 14204021, 14207319), in part by CUHK Strategic Fund.

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Authors and Affiliations

  1. Multimedia Laboratory, The Chinese University of Hong Kong, Shatin, Hong Kong

    Zhaoyang Huang, Xiaoyu Shi & Hongsheng Li

  2. Samsung Telecommunication Research, Suwon, South Korea

    Chao Zhang & Qiang Wang

  3. NVIDIA AI Technology Center, Shanghai, China

    Zhaoyang Huang, Xiaoyu Shi & Ka Chun Cheung

  4. SenseTime Research, Shanghai, China

    Hongwei Qin & Jifeng Dai

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  1. Zhaoyang Huang
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  2. Xiaoyu Shi
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  3. Chao Zhang
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  6. Hongwei Qin
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  8. Hongsheng Li
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Correspondence to Hongsheng Li .

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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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Huang, Z. et al. (2022). FlowFormer: A Transformer Architecture for Optical Flow. 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 13677. Springer, Cham. https://doi.org/10.1007/978-3-031-19790-1_40

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