  Fully-supervised salient object detection (SOD) methods have made great
progress, but such methods often rely on a large number of pixel-level
annotations, which are time-consuming and labour-intensive. In this paper, we
focus on a new weakly-supervised SOD task under hybrid labels, where the
supervision labels include a large number of coarse labels generated by the
traditional unsupervised method and a small number of real labels. To address
the issues of label noise and quantity imbalance in this task, we design a new
pipeline framework with three sophisticated training strategies. In terms of
model framework, we decouple the task into label refinement sub-task and
salient object detection sub-task, which cooperate with each other and train
alternately. Specifically, the R-Net is designed as a two-stream
encoder-decoder model equipped with Blender with Guidance and Aggregation
Mechanisms (BGA), aiming to rectify the coarse labels for more reliable
pseudo-labels, while the S-Net is a replaceable SOD network supervised by the
pseudo labels generated by the current R-Net. Note that, we only need to use
the trained S-Net for testing. Moreover, in order to guarantee the
effectiveness and efficiency of network training, we design three training
strategies, including alternate iteration mechanism, group-wise incremental
mechanism, and credibility verification mechanism. Experiments on five SOD
benchmarks show that our method achieves competitive performance against
weakly-supervised/unsupervised methods both qualitatively and quantitatively.
