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

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...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Filipiak, Dominik, ZapaLa, Andrzej, Tempczyk, Piotr, Fensel, Anna, Cygan, Marek
Format: Article
Language:English
Subjects:
anchor-free detection
Instance segmentation
Labels
Learning
Semi-supervised instance segmentation
semi-supervised learning
Teachers
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Summary: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 [will be released after the publication].
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3374073