  Co-Salient Object Detection (CoSOD) aims at discovering salient objects that
repeatedly appear in a given query group containing two or more relevant
images. One challenging issue is how to effectively capture co-saliency cues by
modeling and exploiting inter-image relationships. In this paper, we present an
end-to-end collaborative aggregation-and-distribution network (CoADNet) to
capture both salient and repetitive visual patterns from multiple images.
First, we integrate saliency priors into the backbone features to suppress the
redundant background information through an online intra-saliency guidance
structure. After that, we design a two-stage aggregate-and-distribute
architecture to explore group-wise semantic interactions and produce the
co-saliency features. In the first stage, we propose a group-attentional
semantic aggregation module that models inter-image relationships to generate
the group-wise semantic representations. In the second stage, we propose a
gated group distribution module that adaptively distributes the learned group
semantics to different individuals in a dynamic gating mechanism. Finally, we
develop a group consistency preserving decoder tailored for the CoSOD task,
which maintains group constraints during feature decoding to predict more
consistent full-resolution co-saliency maps. The proposed CoADNet is evaluated
on four prevailing CoSOD benchmark datasets, which demonstrates the remarkable
performance improvement over ten state-of-the-art competitors.
