Boosting Semantic Human Matting with Coarse Annotations Article Swipe
Jinlin Liu
,
Yuan Yao
,
Wendi Hou
,
Miaomiao Cui
,
Xuansong Xie
,
Changshui Zhang
,
Xian‐Sheng Hua
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.04955
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.04955
Semantic human matting aims to estimate the per-pixel opacity of the foreground human regions. It is quite challenging and usually requires user interactive trimaps and plenty of high quality annotated data. Annotating such kind of data is labor intensive and requires great skills beyond normal users, especially considering the very detailed hair part of humans. In contrast, coarse annotated human dataset is much easier to acquire and collect from the public dataset. In this paper, we propose to use coarse annotated data coupled with fine annotated data to boost end-to-end semantic human matting without trimaps as extra input. Specifically, we train a mask prediction network to estimate the coarse semantic mask using the hybrid data, and then propose a quality unification network to unify the quality of the previous coarse mask outputs. A matting refinement network takes in the unified mask and the input image to predict the final alpha matte. The collected coarse annotated dataset enriches our dataset significantly, allows generating high quality alpha matte for real images. Experimental results show that the proposed method performs comparably against state-of-the-art methods. Moreover, the proposed method can be used for refining coarse annotated public dataset, as well as semantic segmentation methods, which reduces the cost of annotating high quality human data to a great extent.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2004.04955
- https://arxiv.org/pdf/2004.04955
- OA Status
- green
- Cited By
- 9
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3015539232
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https://openalex.org/W3015539232Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2004.04955Digital Object Identifier
- Title
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Boosting Semantic Human Matting with Coarse AnnotationsWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-04-10Full publication date if available
- Authors
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Jinlin Liu, Yuan Yao, Wendi Hou, Miaomiao Cui, Xuansong Xie, Changshui Zhang, Xian‐Sheng HuaList of authors in order
- Landing page
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https://arxiv.org/abs/2004.04955Publisher landing page
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https://arxiv.org/pdf/2004.04955Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2004.04955Direct OA link when available
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Computer science, Artificial intelligence, Segmentation, Quality (philosophy), Boosting (machine learning), Unification, Pattern recognition (psychology), Data mining, Epistemology, Philosophy, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
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2024: 1, 2022: 1, 2021: 7Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.trimaps | 23, 94 |
| abstract_inverted_index.unified | 139 |
| abstract_inverted_index.usually | 19 |
| abstract_inverted_index.without | 93 |
| abstract_inverted_index.Semantic | 0 |
| abstract_inverted_index.dataset, | 193 |
| abstract_inverted_index.dataset. | 71 |
| abstract_inverted_index.detailed | 50 |
| abstract_inverted_index.enriches | 156 |
| abstract_inverted_index.estimate | 5, 106 |
| abstract_inverted_index.methods, | 199 |
| abstract_inverted_index.methods. | 180 |
| abstract_inverted_index.outputs. | 131 |
| abstract_inverted_index.performs | 176 |
| abstract_inverted_index.previous | 128 |
| abstract_inverted_index.proposed | 174, 183 |
| abstract_inverted_index.refining | 189 |
| abstract_inverted_index.regions. | 13 |
| abstract_inverted_index.requires | 20, 40 |
| abstract_inverted_index.semantic | 90, 109, 197 |
| abstract_inverted_index.Moreover, | 181 |
| abstract_inverted_index.annotated | 29, 58, 80, 85, 154, 191 |
| abstract_inverted_index.collected | 152 |
| abstract_inverted_index.contrast, | 56 |
| abstract_inverted_index.intensive | 38 |
| abstract_inverted_index.per-pixel | 7 |
| abstract_inverted_index.Annotating | 31 |
| abstract_inverted_index.annotating | 205 |
| abstract_inverted_index.comparably | 177 |
| abstract_inverted_index.end-to-end | 89 |
| abstract_inverted_index.especially | 46 |
| abstract_inverted_index.foreground | 11 |
| abstract_inverted_index.generating | 161 |
| abstract_inverted_index.prediction | 103 |
| abstract_inverted_index.refinement | 134 |
| abstract_inverted_index.challenging | 17 |
| abstract_inverted_index.considering | 47 |
| abstract_inverted_index.interactive | 22 |
| abstract_inverted_index.unification | 120 |
| abstract_inverted_index.Experimental | 169 |
| abstract_inverted_index.segmentation | 198 |
| abstract_inverted_index.Specifically, | 98 |
| abstract_inverted_index.significantly, | 159 |
| abstract_inverted_index.state-of-the-art | 179 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile |