FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916744171356160 |
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| author | Sari, Alp Eren Favaro, Paolo |
| author_facet | Sari, Alp Eren Favaro, Paolo |
| contents | We propose FlowCut, a simple and capable method for unsupervised video instance segmentation consisting of a three-stage framework to construct a high-quality video dataset with pseudo labels. To our knowledge, our work is the first attempt to curate a video dataset with pseudo-labels for unsupervised video instance segmentation. In the first stage, we generate pseudo-instance masks by exploiting the affinities of features from both images and optical flows. In the second stage, we construct short video segments containing high-quality, consistent pseudo-instance masks by temporally matching them across the frames. In the third stage, we use the YouTubeVIS-2021 video dataset to extract our training instance segmentation set, and then train a video segmentation model. FlowCut achieves state-of-the-art performance on the YouTubeVIS-2019, YouTubeVIS-2021, DAVIS-2017, and DAVIS-2017 Motion benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching Sari, Alp Eren Favaro, Paolo Computer Vision and Pattern Recognition We propose FlowCut, a simple and capable method for unsupervised video instance segmentation consisting of a three-stage framework to construct a high-quality video dataset with pseudo labels. To our knowledge, our work is the first attempt to curate a video dataset with pseudo-labels for unsupervised video instance segmentation. In the first stage, we generate pseudo-instance masks by exploiting the affinities of features from both images and optical flows. In the second stage, we construct short video segments containing high-quality, consistent pseudo-instance masks by temporally matching them across the frames. In the third stage, we use the YouTubeVIS-2021 video dataset to extract our training instance segmentation set, and then train a video segmentation model. FlowCut achieves state-of-the-art performance on the YouTubeVIS-2019, YouTubeVIS-2021, DAVIS-2017, and DAVIS-2017 Motion benchmarks. |
| title | FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.13174 |