  Co-Salient Object Detection (CoSOD) is a rapidly growing task, extended from
Salient Object Detection (SOD) and Common Object Segmentation
(Co-Segmentation). It is aimed at detecting the co-occurring salient object in
the given image group. Many effective approaches have been proposed on the
basis of existing datasets. However, there is still no standard and efficient
training set in CoSOD, which makes it chaotic to choose training sets in the
recently proposed CoSOD methods. First, the drawbacks of existing training sets
in CoSOD are analyzed in a comprehensive way, and potential improvements are
provided to solve existing problems to some extent. In particular, in this
thesis, a new CoSOD training set is introduced, named Co-Saliency of ImageNet
(CoSINe) dataset. The proposed CoSINe is the largest number of groups among all
existing CoSOD datasets. The images obtained here span a wide variety in terms
of categories, object sizes, etc. In experiments, models trained on CoSINe can
achieve significantly better performance with fewer images compared to all
existing datasets. Second, to make the most of the proposed CoSINe, a novel
CoSOD approach named Hierarchical Instance-aware COnsensus MinEr (HICOME) is
proposed, which efficiently mines the consensus feature from different feature
levels and discriminates objects of different classes in an object-aware
contrastive way. As extensive experiments show, the proposed HICOME achieves
SoTA performance on all the existing CoSOD test sets. Several useful training
tricks suitable for training CoSOD models are also provided. Third, practical
applications are given using the CoSOD technique to show the effectiveness.
Finally, the remaining challenges and potential improvements of CoSOD are
discussed to inspire related work in the future. The source code, the dataset,
and the online demo will be publicly available at github.com/ZhengPeng7/CoSINe.
