  Most previous co-salient object detection works mainly focus on extracting
co-salient cues via mining the consistency relations across images while
ignoring explicit exploration of background regions. In this paper, we propose
a Discriminative co-saliency and background Mining Transformer framework (DMT)
based on several economical multi-grained correlation modules to explicitly
mine both co-saliency and background information and effectively model their
discrimination. Specifically, we first propose a region-to-region correlation
module for introducing inter-image relations to pixel-wise segmentation
features while maintaining computational efficiency. Then, we use two types of
pre-defined tokens to mine co-saliency and background information via our
proposed contrast-induced pixel-to-token correlation and co-saliency
token-to-token correlation modules. We also design a token-guided feature
refinement module to enhance the discriminability of the segmentation features
under the guidance of the learned tokens. We perform iterative mutual promotion
for the segmentation feature extraction and token construction. Experimental
results on three benchmark datasets demonstrate the effectiveness of our
proposed method. The source code is available at:
https://github.com/dragonlee258079/DMT.
