WUSL–SOD: Joint weakly supervised, unsupervised and supervised learning for salient object detection

Deep learning methods for salient object detection (SOD) have been studied actively and promisingly. However, it is still challenging for the studies with two aspects. The first one is a single type of label from the network to convey limit information, which leads to the poor generalization ability of the network. The second one is the difficulty to improve the accuracy and detect details of target. To address these challenges, we develop a novel approach via joint weakly supervised, unsupervised and supervised learning for SOD (WUSL–SOD), which differs from existing methods just based on ground-truth or other sparse labels. Specifically, to optimize the objective of the image, the unsupervised learning module (ULM) is designed to generate coarse saliency feature and suppress background noises via attention guiding mechanism. Then, we propose the weakly supervised learning module (WLM) based on scribbles for producing relatively accurate saliency feature. Note that this structure is used to enhance the details and remedy the deficiency of scribbles in WLM. For further refining information from the ULM and WLM, we propose a supervised learning module (SLM), which is not only applied to process and refine information from the ULM and WLM, but also enhance the image details and capture the entire target area. Furthermore, we also exchange information between the SLM and the WLM to obtain more accurate saliency maps. Extensive experiments on five datasets demonstrate that the proposed approach can effectively outperform the state-of-the-art approaches and achieve real-time.