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 across various self-supervised features, eg, MoCov2, SwAV, and DINO;(ii) Given mask proposals from multiple applications of spectral clustering on image features computed from different self-supervised models, 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, termed SelfMask, which outperforms prior approaches on three unsupervised SOD benchmarks. Code is publicly available at https://github. com/NoelShin/selfmask.