  Existing salient instance detection (SID) methods typically learn from
pixel-level annotated datasets. In this paper, we present the first
weakly-supervised approach to the SID problem. Although weak supervision has
been considered in general saliency detection, it is mainly based on using
class labels for object localization. However, it is non-trivial to use only
class labels to learn instance-aware saliency information, as salient instances
with high semantic affinities may not be easily separated by the labels. We
note that subitizing information provides an instant judgement on the number of
salient items, which naturally relates to detecting salient instances and may
help separate instances of the same class while grouping different parts of the
same instance. Inspired by this insight, we propose to use class and subitizing
labels as weak supervision for the SID problem. We propose a novel
weakly-supervised network with three branches: a Saliency Detection Branch
leveraging class consistency information to locate candidate objects; a
Boundary Detection Branch exploiting class discrepancy information to delineate
object boundaries; and a Centroid Detection Branch using subitizing information
to detect salient instance centroids. This complementary information is further
fused to produce salient instance maps. We conduct extensive experiments to
demonstrate that the proposed method plays favorably against carefully designed
baseline methods adapted from related tasks.
