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

中文翻译：

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通过渐进式监督学习的交互语义分割网络

摘要

语义分割既需要低层细节，又需要高层语义，既不能丢失太多细节，又能保证推理速度。大多数现有的分割方法都利用预训练模型的低级和高级特征。我们通过渐进式监督学习（ISSNet）提出了一种交互语义分割网络。与两组特征的简单融合不同，我们引入了信息交互模块将语义嵌入到图像细节中，它们以交互的方式共同指导特征的响应。我们开发了一个简单而有效的边界细化模块，以提供细化的边界特征来匹配相应的语义。我们在整个训练级别引入渐进式监督学习策略，以显着提高网络性能，而不是架构级别。我们提出的 ISSNet 显示了最佳推理时间。我们对四个数据集进行了广泛的实验，包括 Cityscapes、HazeCityscapes、RainCityscapes 和 CamVid。除了在晴天表现更好之外，提出的 ISSNet 在雨天和雾天也表现良好。我们还进行了消融研究，以证明我们提出的组件的作用。代码位于：https://github.com/Ruini94/ISSNet