Title

Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation

Publication Name

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Abstract

Deep learning models are known to suffer from the problem of catastrophic forgetting when they incrementally learn new classes. Continual learning for semantic segmentation (CSS) is an emerging field in computer vision. We identify a problem in CSS: A model tends to be confused between old and new classes that are visually similar, which makes it forget the old ones. To address this gap, we propose REMINDER - a new CSS framework and a novel class similarity knowledge distillation (CSW-KD) method. Our CSW-KD method distills the knowledge of a previous model on old classes that are similar to the new one. This provides two main benefits: (i) selectively revising old classes that are more likely to be forgotten, and (ii) better learning new classes by relating them with the previously seen classes. Extensive experiments on Pascal-Voc 2012 and ADE20k datasets show that our approach outperforms state-of-the-art methods on standard CSS settings by up to 7.07% and 8.49%, respectively.

Open Access Status

This publication may be available as open access

Volume

2022-June

First Page

16845

Last Page

16854

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/CVPR52688.2022.01636