Deep Context Convolutional Neural Networks for Semantic Segmentation

Recent years have witnessed the great progress for semantic segmentation using deep convolutional neural networks (DCNNs). This paper presents a novel fully convolutional network for semantic segmentation using multi-scale contextual convolutional features. Since objects in natural images tend to be with various scales and aspect ratios, capturing the rich contextual information is very critical for dense pixel prediction. On the other hand, with going deeper of the convolutional layers, the convolutional feature maps of traditional DCNNs gradually become coarser, which may be harmful for semantic segmentation. According to these observations, we attempt to design a deep context convolutional network (DCCNet), which combines the feature maps from different levels of network in a holistic manner for semantic segmentation. The proposed network allows us to fully exploit local and global contextual information, ranging from an entire scene, though sub-regions, to every single pixel, to perform pixel label estimation. The experimental results demonstrate that our DCCNet (without any postprocessing) outperforms state-of-the-art methods on PASCAL VOC 2012, which is the most popular and challenging semantic segmentation dataset.