Urban scene semantic segmentation with insufficient labeled data

Abstract

Semantic segmentation of urban scenes is an enabling factor for a wide range of applications. With the development of deep learning in recent years, semantic segmentation tasks using high-capacity models have achieved considerable successes on large datasets. However, the pixel-level annotation process, especially for urban scene images with various objects, is tedious and labor intensive. Meanwhile, the scale of the unlabeled data, which is currently easy to collect, is often much larger than labeled data. Thus, using the abundant unlabeled data to make up the loss of the segmentation model from insufficient labeled data is of great interest. In this paper, we propose a semi-supervised method based on reinforcement learning to capture the contextual information from the unlabeled data to improve the model trained on the small scale labeled data. Both quantitative and qualitative experiments have shown the effectiveness of the proposed method.