Papers › TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and...
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
Yangtao Wang, Xi Shen, Yuan Yuan, Yuming Du, Maomao Li, Shell Xu Hu, James L Crowley, Dominique Vaufreydaz
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Instance Segmentation | COCO val2017 | TokenCut | AP | 2.4 | #5 of 5 | Archive leaderboard | report |
| Unsupervised Instance Segmentation | COCO val2017 | TokenCut | AP50 | 4.8 | #5 of 5 | Archive leaderboard | report |
| Unsupervised Instance Segmentation | COCO val2017 | TokenCut | AP75 | 1.9 | #5 of 5 | Archive leaderboard | report |
| Unsupervised Object Segmentation | FBMS-59 | TokenCut | mIoU | 60.2 | #6 of 7 | Archive leaderboard | report |
| Unsupervised Object Segmentation | SegTrack-v2 | TokenCut | mIoU | 59.6 | #7 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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