  Co-saliency detection (CoSOD) aims at discovering the repetitive salient
objects from multiple images. Two primary challenges are group semantics
extraction and noise object suppression. In this paper, we present a unified
Two-stage grOup semantics PropagatIon and Contrastive learning NETwork
(TopicNet) for CoSOD. TopicNet can be decomposed into two substructures,
including a two-stage group semantics propagation module (TGSP) to address the
first challenge and a contrastive learning module (CLM) to address the second
challenge. Concretely, for TGSP, we design an image-to-group propagation module
(IGP) to capture the consensus representation of intra-group similar features
and a group-to-pixel propagation module (GPP) to build the relevancy of
consensus representation. For CLM, with the design of positive samples, the
semantic consistency is enhanced. With the design of negative samples, the
noise objects are suppressed. Experimental results on three prevailing
benchmarks reveal that TopicNet outperforms other competitors in terms of
various evaluation metrics.
