  Co-Salient Object Detection (CoSOD) aims at detecting common salient objects
within a group of relevant source images. Most of the latest works employ the
attention mechanism for finding common objects. To achieve accurate CoSOD
results with high-quality maps and high efficiency, we propose a novel
Memory-aided Contrastive Consensus Learning (MCCL) framework, which is capable
of effectively detecting co-salient objects in real time (~150 fps). To learn
better group consensus, we propose the Group Consensus Aggregation Module
(GCAM) to abstract the common features of each image group; meanwhile, to make
the consensus representation more discriminative, we introduce the Memory-based
Contrastive Module (MCM), which saves and updates the consensus of images from
different groups in a queue of memories. Finally, to improve the quality and
integrity of the predicted maps, we develop an Adversarial Integrity Learning
(AIL) strategy to make the segmented regions more likely composed of complete
objects with less surrounding noise. Extensive experiments on all the latest
CoSOD benchmarks demonstrate that our lite MCCL outperforms 13 cutting-edge
models, achieving the new state of the art (~5.9% and ~6.2% improvement in
S-measure on CoSOD3k and CoSal2015, respectively). Our source codes, saliency
maps, and online demos are publicly available at
https://github.com/ZhengPeng7/MCCL.
