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SeqFormer: Sequential Transformer for Video Instance Segmentation

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 13688))

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Abstract

In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the principle of vision transformer that models instance relationships among video frames. Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video, but attention mechanisms shall be done with each frame independently. To achieve this, SeqFormer locates an instance in each frame and aggregates temporal information to learn a powerful representation of a video-level instance, which is used to predict the mask sequences on each frame dynamically. Instance tracking is achieved naturally without tracking branches or post-processing. On YouTube-VIS, SeqFormer achieves 47.4 AP with a ResNet-50 backbone and 49.0 AP with a ResNet-101 backbone without bells and whistles. Such achievement significantly exceeds the previous state-of-the-art performance by 4.6 and 4.4, respectively. In addition, integrated with the recently-proposed Swin transformer, SeqFormer achieves a much higher AP of 59.3. We hope SeqFormer could be a strong baseline that fosters future research in video instance segmentation, and in the meantime, advances this field with a more robust, accurate, neat model. The code is available athttps://github.com/wjf5203/SeqFormer.

J. Wu and W. Zhang—Work done during an internship at ByteDance.

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Acknowledgment

We thank Xiaoding Yuan for the support and discussions about implementation details. We thank the anonymous reviewers for their efforts and valuable feedback to improve our work.

Author information

Authors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Junfeng Wu, Wenqing Zhang & Xiang Bai

  2. ByteDance Inc., Singapore, Singapore

    Yi Jiang & Song Bai

Authors
  1. Junfeng Wu

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  2. Yi Jiang

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  3. Song Bai

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  4. Wenqing Zhang

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  5. Xiang Bai

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Corresponding author

Correspondence toXiang Bai.

Editor information

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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Wu, J., Jiang, Y., Bai, S., Zhang, W., Bai, X. (2022). SeqFormer: Sequential Transformer for Video Instance 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 13688. Springer, Cham. https://doi.org/10.1007/978-3-031-19815-1_32

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