Interaction semantic segmentation network via progressive supervised learning

Abstract

Semantic segmentation requires both low-level details and high-level semantics, without losing too much detail and ensuring the speed of inference. Most existing segmentation approaches leverage low- and high-level features from pre-trained models. We propose an interaction semantic segmentation network via Progressive Supervised Learning (ISSNet). Unlike a simple fusion of two sets of features, we introduce an information interaction module to embed semantics into image details, they jointly guide the response of features in an interactive way. We develop a simple yet effective boundary refinement module to provide refined boundary features for matching corresponding semantic. We introduce a progressive supervised learning strategy throughout the training level to significantly promote network performance, not architecture level. Our proposed ISSNet shows optimal inference time. We perform extensive experiments on four datasets, including Cityscapes, HazeCityscapes, RainCityscapes and CamVid. In addition to performing better in fine weather, proposed ISSNet also performs well on rainy and foggy days. We also conduct ablation study to demonstrate the role of our proposed component. Code is available at: https://github.com/Ruini94/ISSNet