Salient object detection via light-weight multi-path cascaded networks

Abstract

Recently, deep learning-based saliency detection has achieved fantastic performance over conventional works. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. It is difficult for a network to learn saliency at the boundaries of salient regions. In this paper, a Light-Weight Multi-Path Cascaded Network (LMCN) is proposed for image saliency detection – an encoder-decoder architecture that explicitly exploits all the information available along the down-sampling process to enable full-resolution prediction using long-range residual connections. The Res2Net is adopted as a backbone network to better extract multi-level and multi-scale feature maps by constructing hierarchical residual-like connections within one single residual block. Also, the network is lightweighted by the bottleneck structure and depth-wise convolution, which ultimately reduces the number of parameters by more than 50% while maintaining the same performance. Comprehensive experiments are carried out and new state-of-the-art results are set in eight public datasets. Experiment results show that the proposed approach substantially improves previous state-of-the-art performances.