A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

Abstract

Recently, significant progress has been made on semantic segmentation. However, the success of supervised semantic segmentation typically relies on a large amount of labelled data, which is time-consuming and costly to obtain. Inspired by the success of semi-supervised learning methods in image classification, here we propose a simple yet effective semi-supervised learning framework for semantic segmentation. We demonstrate that the devil is in the details: a set of simple design and training techniques can collectively improve the performance of semi-supervised semantic segmentation significantly. Previous works [3, 27] fail to employ strong augmentation in pseudo label learning efficiently, as the large distribution change caused by strong augmentation harms the batch normalisation statistics. We design a new batch normalisation, namely distribution-specific batch normalisation (DSBN) to address this problem and demonstrate the importance of strong augmentation for semantic segmentation. Moreover, we design a self correction loss which is effective in noise resistance. We conduct a series of ablation studies to show the effectiveness of each component. Our method achieves state-of-the-art results in the semi-supervised settings on the Cityscapes and Pascal VOC datasets.

arXiv commentAccepted to Proc. Int. Conf. Computer Vision (ICCV) 2021

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Cited by in corpus (4)

• ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
• Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
• Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
• Robust Mutual Learning for Semi-supervised Semantic Segmentation