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Despite the success of deep learning methods for semantic segmentation,few-shot semantic segmentation remains a challenging task due to the limitedtraining data and the generalisation requirement for unseen classes. Whilerecent progress has been particularly encouraging, we discover that existingmethods tend to have poor performance in terms of meanIoU when query imagescontain other semantic classes besides the target class. To address this issue,we propose a novel self-supervised task that generates random pseudo-classes inthe background of the query images, providing extra training data that wouldotherwise be unavailable when predicting individual target classes. To thatend, we adopted superpixel segmentation for generating the pseudo-classes. Withthis extra supervision, we improved the meanIoU performance of thestate-of-the-art method by 2.5% and 5.1% on the one-shot tasks, as well as 6.7%and 4.4% on the five-shot tasks, on the PASCAL-5i and COCO benchmarks,respectively.

Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes