  Fully supervised salient object detection (SOD) methods have made
considerable progress in performance, yet these models rely heavily on
expensive pixel-wise labels. Recently, to achieve a trade-off between labeling
burden and performance, scribble-based SOD methods have attracted increasing
attention. Previous scribble-based models directly implement the SOD task only
based on SOD training data with limited information, it is extremely difficult
for them to understand the image and further achieve a superior SOD task. In
this paper, we propose a simple yet effective framework guided by general
visual representations with rich contextual semantic knowledge for
scribble-based SOD. These general visual representations are generated by
self-supervised learning based on large-scale unlabeled datasets. Our framework
consists of a task-related encoder, a general visual module, and an information
integration module to efficiently combine the general visual representations
with task-related features to perform the SOD task based on understanding the
contextual connections of images. Meanwhile, we propose a novel global semantic
affinity loss to guide the model to perceive the global structure of the
salient objects. Experimental results on five public benchmark datasets
demonstrate that our method, which only utilizes scribble annotations without
introducing any extra label, outperforms the state-of-the-art weakly supervised
SOD methods. Specifically, it outperforms the previous best scribble-based
method on all datasets with an average gain of 5.5% for max f-measure, 5.8% for
mean f-measure, 24% for MAE, and 3.1% for E-measure. Moreover, our method
achieves comparable or even superior performance to the state-of-the-art fully
supervised models.
