  In this paper, we tackle the challenging task of unsupervised salient object
detection (SOD) by leveraging spectral clustering on self-supervised features.
We make the following contributions: (i) We revisit spectral clustering and
demonstrate its potential to group the pixels of salient objects; (ii) Given
mask proposals from multiple applications of spectral clustering on image
features computed from various self-supervised models, e.g., MoCov2, SwAV,
DINO, we propose a simple but effective winner-takes-all voting mechanism for
selecting the salient masks, leveraging object priors based on framing and
distinctiveness; (iii) Using the selected object segmentation as pseudo
groundtruth masks, we train a salient object detector, dubbed SelfMask, which
outperforms prior approaches on three unsupervised SOD benchmarks. Code is
publicly available at https://github.com/NoelShin/selfmask.
