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VNet: a versatile network to train real-time semantic segmentation models on a single GPU

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References

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2018YFA0701500), Strategic Priority Research Program of CAS (Grant No. XDB44000000), Beijing Academy of Artificial Intelligence (BAAI), National Natural Science Foundation of China (Grant No. 61532017), and CARCH Innovation Project (Grant No. CARCH4506).

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Authors and Affiliations

  1. College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

    Wenxing Li

  2. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Wenxing Li, Ning Lin, Mingzhe Zhang, Hang Lu, Xiaoming Chen & Xiaowei Li

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Ning Lin, Hang Lu, Xiaoming Chen & Xiaowei Li

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  1. Wenxing Li
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  2. Ning Lin
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  3. Mingzhe Zhang
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  4. Hang Lu
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  5. Xiaoming Chen
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  6. Xiaowei Li
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Corresponding authors

Correspondence to Hang Lu, Xiaoming Chen or Xiaowei Li.

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Li, W., Lin, N., Zhang, M. et al. VNet: a versatile network to train real-time semantic segmentation models on a single GPU. Sci. China Inf. Sci. 65, 139105 (2022). https://doi.org/10.1007/s11432-020-2971-8

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