  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. As the
subitizing information provides an instant judgement on the number of salient
items, it is naturally related to detecting salient instances and may help
separate instances of the same class while grouping different parts of the same
instance. Inspired by this observation, 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 then
fused to produce a salient instance map. To facilitate the learning process, we
further propose a progressive training scheme to reduce label noise and the
corresponding noise learned by the model, via reciprocating the model with
progressive salient instance prediction and model refreshing. Our extensive
evaluations show that the proposed method plays favorably against carefully
designed baseline methods adapted from related tasks.
