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README.md
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@@ -32,3 +32,18 @@ We propose the Semantic Complex Scenarios Video Object Segmentation (SeCVOS) ben
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Our annotations are licensed under a [CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/). They are available strictly for non-commercial research.
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We uphold the rights of individuals and copyright holders. If you are featured in any of our video annotations or hold copyright to a video and wish to have its annotation removed from our dataset, please reach out to us. Send an email to [email protected] with the subject line beginning with SeCVOS, or raise an issue with the same title format. We commit to reviewing your request promptly and taking suitable action.
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Our annotations are licensed under a [CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/). They are available strictly for non-commercial research.
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We uphold the rights of individuals and copyright holders. If you are featured in any of our video annotations or hold copyright to a video and wish to have its annotation removed from our dataset, please reach out to us. Send an email to [email protected] with the subject line beginning with SeCVOS, or raise an issue with the same title format. We commit to reviewing your request promptly and taking suitable action.
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---
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## Citation
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If you find this project useful in your research, please consider citing:
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```BibTeX
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@article{zhang2025sec,
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title = {SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction},
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author = {Zhixiong Zhang and Shuangrui Ding and Xiaoyi Dong and Yuhang Zang and Yuhang Cao and Dahua Lin and Jiaqi Wang},
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journal = {arXiv preprint arXiv:2507.xxxxx},
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year = {2025},
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url = {https://arxiv.org/abs/2507.xxxxx}
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}
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```
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