Unsupervised Salient Object Detection with Spectral Cluster Voting
Gyungin Shin, Samuel Albanie, Weidi Xie
Abstract
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, e.g., Mo-Cov2, 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 SELF-MASK, which outperforms prior approaches on three un-supervised SOD benchmarks. Code is publicly available at https://github.com/NoelShin/selfmask.