Knowledge Transfer Based Spatial Embedding Network for Plant Leaf Instance Segmentation

Joo Yeon Jung, Sang Ho Lee, Jong Ok Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper proposes a method to segment plant leaves using knowledge distillation. Unlike the existing knowledge distillation method aimed at lightening the model, we use knowledge distillation to achieve good performance even with a small amount of dataset. Plants have many leaves, and each leaf is very small. Therefore, the leaf instance segmentation is performed based on spatial embedding. The teacher network is trained with a large dataset and then distills its segmentation knowledge into the student network. Two types of knowledge are distilled from the teacher network: attention distillation and region affinity distillation. The results of the experiment demonstrate that better instance segmentation can be achieved when knowledge distillation is used.

Original languageEnglish
Pages (from-to)162-170
Number of pages9
JournalIEIE Transactions on Smart Processing and Computing
Issue number2
Publication statusPublished - 2023

Bibliographical note

Funding Information:
This work is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A4A4079705).

Publisher Copyright:
Copyrights © 2023 The Institute of Electronics and Information Engineers.


  • Knowledge distillation
  • Leaf instance segmentation
  • Spatial embedding

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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