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    Real-time Semantic Segmentation with Fast Attention

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    Type
    Article
    Authors
    Hu, Ping
    Perazzi, Federico
    Heilbron, Fabian Caba
    Wang, Oliver
    Lin, Zhe
    Saenko, Kate
    Sclaroff, Stan
    KAUST Department
    King Abdullah University of Science and Technology, KAUST, Saudi Arabia.
    Date
    2020
    Submitted Date
    2020-08-25
    Permanent link to this record
    http://hdl.handle.net/10754/666065
    
    Metadata
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    Abstract
    Accurate semantic segmentation requires rich contextual cues (large receptive fields) and fine spatial details (high resolution), both of which incur high computational costs. In this paper, we propose a novel architecture that addresses both challenges and achieves state-of-the-art performance for semantic segmentation of high-resolution images and videos in real-time. The proposed architecture relies on our fast attention, which is a simple modification of the popular self-attention mechanism and captures the same rich contextual information at a small fraction of the computational cost, by changing the order of operations. Moreover, to efficiently process high-resolution input, we apply an additional spatial reduction to intermediate feature stages of the network with minimal loss in accuracy thanks to the use of the fast attention module to fuse features. We validate our method with a series of experiments, and show that results on multiple datasets demonstrate superior performance with better accuracy and speed compared to existing approaches for real-time semantic segmentation. On Cityscapes, our network achieves 74.4% mIoU at 72 FPS and 75.5% mIoU at 58 FPS on a single Titan X GPU, which is ~50% faster than the state-of-the-art while retaining the same accuracy.
    Citation
    Hu, P., Perazzi, F., Heilbron, F. C., Wang, O., Lin, Z., Saenko, K., & Sclaroff, S. (2020). Real-time Semantic Segmentation with Fast Attention. IEEE Robotics and Automation Letters, 1–1. doi:10.1109/lra.2020.3039744
    Publisher
    IEEE
    Journal
    IEEE Robotics and Automation Letters
    DOI
    10.1109/LRA.2020.3039744
    Additional Links
    https://ieeexplore.ieee.org/document/9265219/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9265219
    ae974a485f413a2113503eed53cd6c53
    10.1109/LRA.2020.3039744
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