关键词: 语义分割, 全卷积网络, 混合上下文网络, 庭审场景

Abstract: Semantic segmentation is challenging as it requires both object-level information and pixel-level accuracy. Recently, FCN-based systems have great improvement in this area. Unlike classification networks, combining features of different layers plays an important role in these dense prediction models, as these features contain information of different levels. A number of models have been proposed to show how to use these features. However, what is the best architecture to make use of features of different layers is still a question. This paper proposes a module, called mixed context network, and shows that the presented system outperforms most existing semantic segmentation systems by making use of this module. This model performs well in the court scene.

Key words: semantic segmentation, Fully Convolutional Networks（FCN）, mixed context network, court scene