  In this paper, we describe a graph-based algorithm that uses the features
obtained by a self-supervised transformer to detect and segment salient objects
in images and videos. With this approach, the image patches that compose an
image or video are organised into a fully connected graph, where the edge
between each pair of patches is labeled with a similarity score between patches
using features learned by the transformer. Detection and segmentation of
salient objects is then formulated as a graph-cut problem and solved using the
classical Normalized Cut algorithm. Despite the simplicity of this approach, it
achieves state-of-the-art results on several common image and video detection
and segmentation tasks. For unsupervised object discovery, this approach
outperforms the competing approaches by a margin of 6.1%, 5.7%, and 2.6%,
respectively, when tested with the VOC07, VOC12, and COCO20K datasets. For the
unsupervised saliency detection task in images, this method improves the score
for Intersection over Union (IoU) by 4.4%, 5.6% and 5.2%. When tested with the
ECSSD, DUTS, and DUT-OMRON datasets, respectively, compared to current
state-of-the-art techniques. This method also achieves competitive results for
unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS
datasets.
