Abstract:

Existing weakly-supervised salient object detection methods often suffer from problems such as false positive,low recall rate,and unclear edges.To address the above issues,a weakly-supervised salient object detection with the multi-scale progressive network is proposed,which divides salient object detection into three sub-tasks:object localization,saliency region improvement and edge refinement.First,the input image is sampled into three images of different scales,which are respectively fed into the three stages of the multi-scale progressive network for learning.Second,in order to better locate the salient objects,a nested shift multi-layer perceptron is proposed in the object localization stage,which can balance the global feature and local feature extraction ability of the network.Finally,according to the characteristic that the structure of saliency maps is not affected by scale changes,a multi-scale self-supervision module and an object consistency loss are designed to build a self-supervision mechanism,so that the network can output a saliency map with complete regions and sharp edges.The proposed method is tested on five datasets,and outperforms the recent weakly-supervised methods in both quantitative and qualitative comparisons,and can reach 89% of the performance of the related fully-supervised methods on the F-measure index.Experimental results show that the proposed algorithm can generate saliency maps with complete saliency regions and sharp edges,and has good robustness.

Key words: image processing, deep learning, multilayer perceptrons, weakly-supervision