Completing Saliency from Details

The salient object detection (SOD) models based on the UNet or FCN structure have reached a significant milestone, and the addition of edge constraints to the SOD model has progressively become a common practice in current methods. Despite these methods producing excellent results, they still lack sufficient confidence in places with sharp edges of the objects owing to sample imbalance. In addition, compressing the encoded features to lower dimensions to decrease the computational cost, as a commonly used method, would unavoidably diminish the model’s precision. To overcome the aforementioned issues, we propose a feature mutual feedback network (FMFNet) for the SOD task in which the semantic supplement module (SSM) integrates diverse feature information through different receptive fields to preserve important features. In addition, we provide a novel details map, which can better serve as an edge map to aid the model in learning the hard edge regions, resulting in more complete saliency maps. Multiple experiments on five benchmark datasets indicate the effectiveness, robustness, and superiority of the proposed model and details map.