WFNet: A Wider and Finer Network for Salient Object Detection

Multi-layer feature integration has demonstrated its superiority in salient object detection. However, the salient regions generated by most models still suffer from inconsistencies and coarse region boundaries. In this paper, to solve these two problems, we proposed a wider and finer network named WFNet. Firstly, a wider feature enhancement module (WFE) is designed to expand the receptive fields of deep semantic features, which makes the network look wider and more accurate while locating salient regions. Secondly, to improve the regional continuity and reduce background noise, we introduce a finer feature fusion module (F3M) which consists of scale-invariant average pooling and detailed feature integration module with channel-wise attention. Finally, we propose an edge-region complementary strategy (ERC) and an edge-focused loss (EL), which can supplement the diluted deep semantics and let the network pay more attention to boundary pixels of salient objects. Benefit from rich deep semantics and more detailed edge features, WFNet can predict saliency maps with clear boundaries under the guidance of edge-focused loss. Experimental results prove that the proposed method outperforms state-of-the-art methods on five benchmarks without any post-processing.