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BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13689))

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Abstract

Video Object Segmentation (VOS) is fundamental to video understanding. Transformer-based methods show significant performance improvement on semi-supervised VOS. However, existing work faces challenges segmenting visually similar objects in close proximity of each other. In this paper, we propose a novel Bilateral Attention Transformer in Motion-Appearance Neighboring space (BATMAN) for semi-supervised VOS. It captures object motion in the video via a novel optical flow calibration module that fuses the segmentation mask with optical flow estimation to improve within-object optical flow smoothness and reduce noise at object boundaries. This calibrated optical flow is then employed in our novel bilateral attention, which computes the correspondence between the query and reference frames in the neighboring bilateral space considering both motion and appearance. Extensive experiments validate the effectiveness of BATMAN architecture by outperforming all existing state-of-the-art on all four popular VOS benchmarks: Youtube-VOS 2019 (85.0%), Youtube-VOS 2018 (85.3%), DAVIS 2017Val/Test-dev (86.2%/82.2%), and DAVIS 2016 (92.5%).

At Oregon State University, Jialin Yuan and Li Fuxin are supported in part by NSF grant 1911232.

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Author information

Authors and Affiliations

  1. Microsoft, Bellevue, USA

    Ye Yu, Gaurav Mittal & Mei Chen

  2. Oregon State University, Corvallis, USA

    Jialin Yuan & Li Fuxin

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  1. Ye Yu
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  2. Jialin Yuan
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  3. Gaurav Mittal
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  4. Li Fuxin
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  5. Mei Chen
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Correspondence to Ye Yu .

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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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Yu, Y., Yuan, J., Mittal, G., Fuxin, L., Chen, M. (2022). BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation. 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 13689. Springer, Cham. https://doi.org/10.1007/978-3-031-19818-2_35

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