  In this paper, we conduct a comprehensive study on the co-salient object
detection (CoSOD) problem for images. CoSOD is an emerging and rapidly growing
extension of salient object detection (SOD), which aims to detect the
co-occurring salient objects in a group of images. However, existing CoSOD
datasets often have a serious data bias, assuming that each group of images
contains salient objects of similar visual appearances. This bias can lead to
the ideal settings and effectiveness of models trained on existing datasets,
being impaired in real-life situations, where similarities are usually semantic
or conceptual. To tackle this issue, we first introduce a new benchmark, called
CoSOD3k in the wild, which requires a large amount of semantic context, making
it more challenging than existing CoSOD datasets. Our CoSOD3k consists of 3,316
high-quality, elaborately selected images divided into 160 groups with
hierarchical annotations. The images span a wide range of categories, shapes,
object sizes, and backgrounds. Second, we integrate the existing SOD techniques
to build a unified, trainable CoSOD framework, which is long overdue in this
field. Specifically, we propose a novel CoEG-Net that augments our prior model
EGNet with a co-attention projection strategy to enable fast common information
learning. CoEG-Net fully leverages previous large-scale SOD datasets and
significantly improves the model scalability and stability. Third, we
comprehensively summarize 40 cutting-edge algorithms, benchmarking 18 of them
over three challenging CoSOD datasets (iCoSeg, CoSal2015, and our CoSOD3k), and
reporting more detailed (i.e., group-level) performance analysis. Finally, we
discuss the challenges and future works of CoSOD. We hope that our study will
give a strong boost to growth in the CoSOD community. The benchmark toolbox and
results are available on our project page at http://dpfan.net/CoSOD3K/.
