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

Dominik Filipiak, Andrzej Zapała, Piotr Tempczyk, Anna Fensel, Marek Cygan

Keywords: semi-supervised learning, instance segmentation, semi-supervised instance segmentation

TL;DR: We present Polite Teacher, a mutual learning framework with pseudo-label thresholding for single-stage semi-supervised instance segmentation.

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.

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Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/polite-teacher-semi-supervised-instance/code)