Dense dilation network for saliency detection
Zheng Fang, Tieyong Cao, Jibin Yang, Yibo Xing · 2019
Saliency detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the Dense Dilation Network(DDN) -- a novel saliency model based on Dense Convolutional Network(DenseNet), all layers' feature maps are extracted in the same resolution through a step-wise upsampling procedure which contains dilated convolution filters and deconvolution filters. To derive saliency maps, we propose a fusion dense block which contains dilated convolution filters and 1×1 Conv layers to fuse all low-level and high-level feature maps. DDN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that DDN outperforms the state-of-the-art methods on saliency detection and it requires less parameters and computation time.