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- ---
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- tags:
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- - pytorch_model_hub_mixin
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- - model_hub_mixin
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- - arXiv:2404.16035
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- datasets:
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- - chuonghm/MaGGIe-HIM
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- metrics:
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- - mse
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- - sad
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- - mad
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- - conn
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- - grad
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- pipeline_tag: image-segmentation
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- license: cc-by-4.0
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- arXiv: 2404.16035
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- ---
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  # MaGGIe: Mask Guided Gradual Human Instance Matting
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@@ -29,12 +27,13 @@ arXiv: 2404.16035
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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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- ```
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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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  ```
 
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+ ---
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+ tags:
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+ - pytorch_model_hub_mixin
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+ - model_hub_mixin
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+ datasets:
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+ - chuonghm/MaGGIe-HIM
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+ metrics:
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+ - mse
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+ - sad
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+ - mad
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+ - conn
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+ - grad
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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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  # MaGGIe: Mask Guided Gradual Human Instance Matting
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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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+
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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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  ```