The goal of co-salient object detection (CoSOD) is to discover salient
objects that commonly appear in a query group containing two or more relevant
images. Therefore, how to effectively extract inter-image correspondence is
crucial for the CoSOD task. In this paper, we propose a global-and-local
collaborative learning architecture, which includes a global correspondence
modeling (GCM) and a local correspondence modeling (LCM) to capture
comprehensive inter-image corresponding relationship among different images
from the global and local perspectives. Firstly, we treat different images as
different time slices and use 3D convolution to integrate all intra features
intuitively, which can more fully extract the global group semantics. Secondly,
we design a pairwise correlation transformation (PCT) to explore similarity
correspondence between pairwise images and combine the multiple local pairwise
correspondences to generate the local inter-image relationship. Thirdly, the
inter-image relationships of the GCM and LCM are integrated through a
global-and-local correspondence aggregation (GLA) module to explore more
comprehensive inter-image collaboration cues. Finally, the intra- and
inter-features are adaptively integrated by an intra-and-inter weighting fusion
(AEWF) module to learn co-saliency features and predict the co-saliency map.
The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets,
demonstrating that our model trained on a small dataset (about 3k images) still
outperforms eleven state-of-the-art competitors trained on some large datasets
(about 8k-200k images).

本文提出了一种全局和局部协同学习架构（GLNet），包括全局对应建模和局部对应建模，以从全局和局部角度捕获不同图像之间的全面对应关系来学习共同显著对象检测（CoSOD）任务。在三个普及的 CoSOD 基准数据集上进行评估，证明了我们的模型在小数据集（约 3k 张图像）上训练仍然优于一些大数据集上训练的十一个最先进的竞争对手。