  Our paper introduces a novel two-stage self-supervised approach for detecting
co-occurring salient objects (CoSOD) in image groups without requiring
segmentation annotations. Unlike existing unsupervised methods that rely solely
on patch-level information (e.g. clustering patch descriptors) or on
computation heavy off-the-shelf components for CoSOD, our lightweight model
leverages feature correspondences at both patch and region levels,
significantly improving prediction performance. In the first stage, we train a
self-supervised network that detects co-salient regions by computing local
patch-level feature correspondences across images. We obtain the segmentation
predictions using confidence-based adaptive thresholding. In the next stage, we
refine these intermediate segmentations by eliminating the detected regions
(within each image) whose averaged feature representations are dissimilar to
the foreground feature representation averaged across all the cross-attention
maps (from the previous stage). Extensive experiments on three CoSOD benchmark
datasets show that our self-supervised model outperforms the corresponding
state-of-the-art models by a huge margin (e.g. on the CoCA dataset, our model
has a 13.7% F-measure gain over the SOTA unsupervised CoSOD model). Notably,
our self-supervised model also outperforms several recent fully supervised
CoSOD models on the three test datasets (e.g., on the CoCA dataset, our model
has a 4.6% F-measure gain over a recent supervised CoSOD model).
