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

2022-11-07 20:35:51
Dominik Filipiak, Andrzej Zapała, Piotr Tempczyk, Anna Fensel, Marek Cygan
arXiv_AI
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2211.03850

PDF

https://arxiv.org/pdf/2211.03850.pdf

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