CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection

Qijian Zhang, Runmin Cong, Junhui Hou, Chongyi Li, Yao Zhao

Abstract:

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.