TF-SOD: a novel transformer framework for salient object detection

Most of existing salient object detection models are based on fully convolutional network (FCN), which learn multi-scale/level semantic information through convolutional layers to obtain high-quality predicted saliency maps. However, convolution is locally interactive, it is difficult to capture remote dependencies, and FCN-based methods suffer from coarse object boundaries. In this paper, to solve these problems, we propose a novel transformer framework for salient object detection (named TF-SOD), which mainly consists of the encoder part of the FCN, fusion module (FM), transformer module (TM) and feature decoder module (FDM). Specifically, FM is a bridge connecting the encoder and TM and provides some foresight for the non-local interaction of TM. Besides, FDM can efficiently decode the non-local features output by TM and achieve deep fusion with local features. This architecture enables the network to achieve a close integration of local and non-local interactions, making information complementary to each other, deeply mining the associated information between features. Furthermore, we also propose a novel edge reinforcement learning strategy, which can effectively suppress edge blurring from local and global aspects by means of powerful network architecture. Extensive experiments using five datasets demonstrate that the proposed method outperforms 19 state-of-the-art methods