GSCINet: Gradual Shrinkage and Cyclic Interaction Network for Salient Object Detection

Feature Pyramid Network (FPN) has been widely applied in the task of salient object detection (SOD), which has achieved great performance. However, most existing FPN-based SOD methods still have some limitations, such as insufficient guidance due to gradual dilution of semantic information, excessive computation leading to slow inference speed, and low efficiency of training models. In this paper, we design a novel Gradual Shrinkage and Cyclic Interaction Network (GSCINet) for efficient and accurate SOD, consisting of a Multi-Scale Contextual Attention Module (MSCAM) and an Adjacent Feature Shrinkage and Interaction Module (AFSIM). Specifically, the MSCAM aims at efficiently capturing multi-scale and multi-receptive-field contextual attention information through a series of well-designed convolutions and attention weight matrices of different scales to enhance the performance of initial input features. Subsequently, in AFSIM, we propose a gradual shrinkage structure and introduce a circular interaction mechanism to optimize the compressed features with less calculation cost, thereby enabling fast and accurate inference of salient objects. Extensive experimental results demonstrate the high efficiency and superiority of GSCINet against 17 state-of-the-art (SOTA) saliency detection methods under multiple evaluation metrics.