MaGGIe ensures feature temporal consistency in videos using bidirectional Conv-GRU. It uses AdamW optimization and curriculum learning on A100 GPUs for robust mattingMaGGIe ensures feature temporal consistency in videos using bidirectional Conv-GRU. It uses AdamW optimization and curriculum learning on A100 GPUs for robust matting
MaGGIe Training Setup: High-Performance Human Instance Matting with A100 GPUs
Table of Links
Abstract and 1. Introduction
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Related Works
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MaGGIe
3.1. Efficient Masked Guided Instance Matting
3.2. Feature-Matte Temporal Consistency
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Instance Matting Datasets
4.1. Image Instance Matting and 4.2. Video Instance Matting
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Experiments
5.1. Pre-training on image data
5.2. Training on video data
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Discussion and References
\ Supplementary Material
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Architecture details
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Image matting
8.1. Dataset generation and preparation
8.2. Training details
8.3. Quantitative details
8.4. More qualitative results on natural images
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Video matting
9.1. Dataset generation
9.2. Training details
9.3. Quantitative details
9.4. More qualitative results
8.2. Training details
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:::info Authors:
(1) Chuong Huynh, University of Maryland, College Park (chuonghm@cs.umd.edu);
(2) Seoung Wug Oh, Adobe Research (seoh,jolee@adobe.com);
(3) Abhinav Shrivastava, University of Maryland, College Park (abhinav@cs.umd.edu);
(4) Joon-Young Lee, Adobe Research (jolee@adobe.com).
:::
:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
:::
\
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