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pipeline_tag: image-segmentation
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arxiv: 2404.16035
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license: cc-by-4.0
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**[Chuong Huynh](https://hmchuong.github.io/), [Seoung Wug Oh](https://sites.google.com/view/seoungwugoh/), [Abhinav Shrivastava](https://www.cs.umd.edu/~abhinav/), [Joon-Young Lee](https://joonyoung-cv.github.io/)**
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Work is a part of Summer Internship 2023 at [Adobe Research](https://research.adobe.com/)
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pipeline_tag: image-segmentation
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license: cc-by-4.0
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---
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**[Chuong Huynh](https://hmchuong.github.io/), [Seoung Wug Oh](https://sites.google.com/view/seoungwugoh/), [Abhinav Shrivastava](https://www.cs.umd.edu/~abhinav/), [Joon-Young Lee](https://joonyoung-cv.github.io/)**
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Work is a part of Summer Internship 2023 at [Adobe Research](https://research.adobe.com/)
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## Citation
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If you find MaGGIe useful in your research, please cite the following paper:
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```latex
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@article{chuonghm_maggie,
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author = {Chuong Huynh and Seoung Wug Oh and and Abhinav Shrivastava and Joon-Young Lee},
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title = {MaGGIe: Masked Guided Gradual Human Instance Matting},
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journal = {arXiv:2404.16035},
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year = {2024}
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}
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```
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