Colour augmentation for improved semi-supervised semantic segmentation

Geoff French; Michal Mackiewicz

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arXiv · 2110.04487

Colour augmentation for improved semi-supervised semantic segmentation

Geoff French·Michal Mackiewicz

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Abstract

Consistency regularization describes a class of approaches that have yielded state-of-the-art results for semi-supervised classification. While semi-supervised semantic segmentation proved to be more challenging, a number of successful approaches have been recently proposed. Recent work explored the challenges involved in using consistency regularization for segmentation problems. In their self-supervised work Chen et al. found that colour augmentation prevents a classification network from using image colour statistics as a short-cut for self-supervised learning via instance discrimination. Drawing inspiration from this we find that a similar problem impedes semi-supervised semantic segmentation and offer colour augmentation as a solution, improving semi-supervised semantic segmentation performance on challenging photographic imagery.

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Geoff French, Michal Mackiewicz. 2021-10-09. Colour augmentation for improved semi-supervised semantic segmentation. https://arxiv.org/abs/2110.04487

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