  The success of fully supervised saliency detection models depends on a large
number of pixel-wise labeling. In this paper, we work on bounding-box based
weakly-supervised saliency detection to relieve the labeling effort. Given the
bounding box annotation, we observe that pixels inside the bounding box may
contain extensive labeling noise. However, as a large amount of background is
excluded, the foreground bounding box region contains a less complex
background, making it possible to perform handcrafted features-based saliency
detection with only the cropped foreground region. As the conventional
handcrafted features are not representative enough, leading to noisy saliency
maps, we further introduce structure-aware self-supervised loss to regularize
the structure of the prediction. Further, we claim that pixels outside the
bounding box should be background, thus partial cross-entropy loss function can
be used to accurately localize the accurate background region. Experimental
results on six benchmark RGB saliency datasets illustrate the effectiveness of
our model.
