Corpus ID: 3924215

ShuffleSeg: Real-time Semantic Segmentation Network

@article{Gamal2018ShuffleSegRS,
  title={ShuffleSeg: Real-time Semantic Segmentation Network},
  author={M. Gamal and M. Siam and Moemen Abdel-Razek},
  journal={ArXiv},
  year={2018},
  volume={abs/1803.03816}
}
  • M. Gamal, M. Siam, Moemen Abdel-Razek
  • Published 2018
  • Computer Science
  • ArXiv
  • Real-time semantic segmentation is of significant importance for mobile and robotics related applications. [...] Key Method An ablation study of different decoding methods is compared including Skip architecture, UNet, and Dilation Frontend. Interesting insights on the speed and accuracy tradeoff is discussed.Expand Abstract
    27 Citations

    Figures, Tables, and Topics from this paper

    An Efficient Semantic Segmentation Method using Pyramid ShuffleNet V2 with Vortex Pooling
    • 2
    EHANet: Efficient Hybrid Attention Network towards Real-time Semantic Segmentation
    Small Object Augmentation of Urban Scenes for Real-Time Semantic Segmentation
    • 3
    • Highly Influenced
    RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving
    • 3
    • PDF
    Semantic Segmentation , Urban Navigation , and Research Directions
    • PDF
    Scene understanding through semantic image segmentation in augmented reality
    • Highly Influenced
    • PDF

    References

    SHOWING 1-10 OF 21 REFERENCES
    ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
    • 853
    • Highly Influential
    • PDF
    LinkNet: Exploiting encoder representations for efficient semantic segmentation
    • 313
    • PDF
    The Cityscapes Dataset for Semantic Urban Scene Understanding
    • 3,854
    • PDF
    SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
    • 5,475
    • Highly Influential
    • PDF
    SegICP-DSR: Dense Semantic Scene Reconstruction and Registration
    • 7
    • PDF
    Multi-Scale Context Aggregation by Dilated Convolutions
    • 3,883
    • Highly Influential
    • PDF
    U-Net: Convolutional Networks for Biomedical Image Segmentation
    • 19,186
    • Highly Influential
    • PDF
    Going deeper with convolutions
    • 22,341
    • PDF
    XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
    • 1,687
    • PDF
    MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
    • 6,280
    • PDF