Corpus ID: 150373855

EdgeSegNet: A Compact Network for Semantic Segmentation

@article{Lin2019EdgeSegNetAC,
  title={EdgeSegNet: A Compact Network for Semantic Segmentation},
  author={Z. Lin and Brendan Chwyl and A. Wong},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.04222}
}
In this study, we introduce EdgeSegNet, a compact deep convolutional neural network for the task of semantic segmentation. A human-machine collaborative design strategy is leveraged to create EdgeSegNet, where principled network design prototyping is coupled with machine-driven design exploration to create networks with customized module-level macroarchitecture and microarchitecture designs tailored for the task. Experimental results showed that EdgeSegNet can achieve semantic segmentation… Expand
Highly Efficient Deep Intelligence via Multi-Parent Evolutionary Synthesis of Deep Neural Networks
Benchmarking Jetson Platform for 3D Point-Cloud and Hyper-Spectral Image Classification
  • Shan Ullah, Deok-Hwan Kim
  • Computer Science
  • 2020 IEEE International Conference on Big Data and Smart Computing (BigComp)
  • 2020
Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU

References

SHOWING 1-10 OF 31 REFERENCES
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Understanding Convolution for Semantic Segmentation
Rethinking Atrous Convolution for Semantic Image Segmentation
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Pyramid Scene Parsing Network
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design
  • A. Wong, Z. Lin, Brendan Chwyl
  • Computer Science
  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2019
MnasNet: Platform-Aware Neural Architecture Search for Mobile
...
1
2
3
4
...