Densely connected deconvolutional network for semantic segmentation

Jun Fu, Jing Liu, Yuhang Wang, Hanqing Lu

Abstract: Recent progress in semantic segmentation has been driven by improving the spatial resolution under Fully Convolutional Networks(FCNs). To address this problem, we propose a Densely Connected Deconvolutional Network (DCDN) for semantic segmentation. In DCDN, multiple shallow deconvolutional networks, which are called as DCDN units, are stacked one by one to make the structure deeper and guarantee the fine recovery of localization information, meanwhile, the inter-unit and intra-unit dense connections are designed to make the network easy to train since the connections improve the flow of information and gradients throughout the network. Besides, the intermediate supervisions are applied to each DCDN unit to ensure the fast convergence. Extensive experiments on two urban scene datasets, i.e., CamVid and GATECH, demonstrate that the proposed model achieves better performance than some state-of-the-art methods without using any post-processing, pretrained model, nor temporal information, whilst requiring less parameters.