  Given a group of images, co-salient object detection (CoSOD) aims to
highlight the common salient object in each image. There are two factors
closely related to the success of this task, namely consensus extraction, and
the dispersion of consensus to each image. Most previous works represent the
group consensus using local features, while we instead utilize a hierarchical
Transformer module for extracting semantic-level consensus. Therefore, it can
obtain a more comprehensive representation of the common object category, and
exclude interference from other objects that share local similarities with the
target object. In addition, we propose a Transformer-based dispersion module
that takes into account the variation of the co-salient object in different
scenes. It distributes the consensus to the image feature maps in an
image-specific way while making full use of interactions within the group.
These two modules are integrated with a ViT encoder and an FPN-like decoder to
form an end-to-end trainable network, without additional branch and auxiliary
loss. The proposed method is evaluated on three commonly used CoSOD datasets
and achieves state-of-the-art performance.
