Weakly Supervised Salient Object Detection Based on Image Semantics

To reduce the dependence of salient object detection on pixel-level labels,we propose a weakly supervised salient object detection method based on image semantics.Using the combined model of the fish network and the attention mechanism,on the basis of the image semantic heat map,the weak labels were trained and updated by cosine similarity.At the same time,we used a training induction strategy.In the initial stage of network training,a simple dataset was used to induce the entire network to make it have certain capabilities.Then,after continuously increasing the complexity of the dataset,the network’s ability to extract features became stronger and stronger.Experiments were conducted on four saliency detection data sets,and compared with traditional supervision methods.The experimental results show that the F-MAX value of this method is increased by 0.03‒0.08 on each data set on average,and the MAE is reduced by 0.02‒0.05.Under the weak supervision label,this method can more accurately extract the salient features.