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| This is a demo of <a href="https://arxiv.org/pdf/2203.12614.pdf">Unsupervised Salient Object Detection with Spectral Cluster Voting</a> (CVPRW 2022).</br> | |
| In the 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 <a href="https://github.com/NoelShin/selfmask">our repo</a>. | |
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