Self-progress aggregate learning for weakly supervised salient object detection

Abstract

The task of salient object detection aims to find the most salient object from the samples. In the field of weakly supervised learning, the existing weakly supervised salient object detection (WSSOD) methods often fail to utilize the limited label information (such as self-affinity or edge features, and scale transform) for learning. Therefore, this paper proposes a self-progress aggregate learning method named SPAL. First, the feature optimization scheme of the edge information module is put forward based on analysis of the problems existing in the current convolutional neural network for detection of the edge information of the object. Obviously, the salient object has a low requirement for high-level information, In particular, in order to improve the utilization rate of the network structure without increasing its complexity, the affinity global context is design in view of the particularity of the structure of a salient object. The structure of a salient object not only depends on the deep-level semantic feature information to a certain extent, but also has a certain guiding effect on the object position and edge information. Second, high-level affinity information is used to complement the slight-level edge information globally, and the scale attention module is adopted to guide the network to adapt the multi-scale reinforcement feature learning ability for the salient object regions. Our method SPAL achieved better experimental results than the other competitive models for comparison on five benchmark data sets (i.e. for DUTS-TEST, compared with CCFNet our method achieved an improvement of 0.6% for mean absolute error (MAE), 5.1% for Fb, and 1.1% for Eℰ ), which demonstrates the effectiveness of our proposed method.