  Compared with laborious pixel-wise dense labeling, it is much easier to label
data by scribbles, which only costs 1$\sim$2 seconds to label one image.
However, using scribble labels to learn salient object detection has not been
explored. In this paper, we propose a weakly-supervised salient object
detection model to learn saliency from such annotations. In doing so, we first
relabel an existing large-scale salient object detection dataset with
scribbles, namely S-DUTS dataset. Since object structure and detail information
is not identified by scribbles, directly training with scribble labels will
lead to saliency maps of poor boundary localization. To mitigate this problem,
we propose an auxiliary edge detection task to localize object edges
explicitly, and a gated structure-aware loss to place constraints on the scope
of structure to be recovered. Moreover, we design a scribble boosting scheme to
iteratively consolidate our scribble annotations, which are then employed as
supervision to learn high-quality saliency maps. As existing saliency
evaluation metrics neglect to measure structure alignment of the predictions,
the saliency map ranking metric may not comply with human perception. We
present a new metric, termed saliency structure measure, to measure the
structure alignment of the predicted saliency maps, which is more consistent
with human perception. Extensive experiments on six benchmark datasets
demonstrate that our method not only outperforms existing
weakly-supervised/unsupervised methods, but also is on par with several
fully-supervised state-of-the-art models. Our code and data is publicly
available at https://github.com/JingZhang617/Scribble_Saliency.
