Semantic Image Segmentation With Propagating Deep Aggregation

Jian Ji, Sitong Li, Jian Xiong, Peng Chen, Qiguang Miao

Abstract: In this article, we propose a new architecture of feature aggregation, which is designed to deal with the problem that the information of each convolutional layer cannot be used reasonably and the shallow layer information is lost in the process of transmission. In this method, propagating deep aggregation (PDA) is transplanted into the DeepLab-ASPP model to generate a new model combining structural continuity with feature aggregation. This new model can be divided into several stages according to the output resolution. In these stages, more possible feature fusion combinations can be realized, and the result from aggregation propagated among stages can be optimized and updated to get the best results. We demonstrate the effectiveness of the proposed model on the PASCAL VOC 2012 data set and the PASCAL-Context data set. Our method achieves state-of-the-art performance on two public benchmarks and significantly outperforms the previous results, 78.8% (versus 62.4%) on the PASCAL VOC 2012 data set and 46.2% (versus 37.8%) on the PASCAL-Context data set.