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Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 11, 1590(2023)

FRKDNet：feature refine semantic segmentation network based on knowledge distillation

Shi-yi JIANG1, Yang XU1,2、*, Dan-yang LI1, and Run-ze FAN1
Author Affiliations
  • 1College of Big Data and Information Engineering，Guizhou University，Guiyang 550025，China
  • 2Guiyang Aluminum-magnesium Design and Research Institute Co.Ltd.，Guiyang 550009，China
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    [10] SHU C Y, LIU Y F, GAO J F et al. Channel-wise knowledge distillation for dense prediction[C], 5291-5300(2021).

    [11] CORDTS M, OMRAN M, RAMOS S et al. The cityscapes dataset for semantic urban scene understanding[C], 3213-3223(2016).

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    [19] ROMERA E, ALVAREZ J M, BERGASA L M et al. ERFNet: Efficient residual factorized ConvNet for real-time semantic segmentation[J]. IEEE Transactions on Intelligent Transportation Systems, 19, 263-272(2018).

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    [24] ZHAO H S, SHI J P, QI X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

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    Shi-yi JIANG, Yang XU, Dan-yang LI, Run-ze FAN. FRKDNet：feature refine semantic segmentation network based on knowledge distillation[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(11): 1590

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    Paper Information

    Category: Research Articles

    Received: Jan. 10, 2023

    Accepted: --

    Published Online: Nov. 29, 2023

    The Author Email: Yang XU (xuy@gzu.edu.cn)

    DOI:10.37188/CJLCD.2023-0010

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