Polite Teacher: Semi-Supervised Instance Segmentation With Mutual Learning and Pseudo-Label Thresholding

Dominik Filipiak*, Andrzej Zapala, Piotr Tempczyk, Anna Fensel, Marek Cygan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We present Polite Teacher, a simple yet effective method for the task of semi-supervised instance segmentation. The proposed architecture relies on the Teacher-Student mutual learning framework. To filter out noisy pseudo-labels, we use confidence thresholding for bounding boxes and mask scoring for masks. The approach has been tested with CenterMask, a single-stage anchor-free detector. Tested on the COCO 2017 val dataset, our architecture significantly (approx. +8 pp. in mask AP) outperforms the baseline at different supervision regimes. To the best of our knowledge, this is one of the first works tackling the problem of semi-supervised instance segmentation and the first one devoted to an anchor-free detector. The code is available: github.com/AI-Clearing/PoliteTeacher.

Original languageEnglish
Pages (from-to)37744-37756
Number of pages13
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024

Keywords

  • anchor-free detection
  • instance segmentation
  • Semi-supervised instance segmentation
  • semi-supervised learning

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