Perceptual Artifacts Localization for Image Synthesis Tasks Article Swipe
Lingzhi Zhang
,
Zhengjie Xu
,
Connelly Barnes
,
Yuqian Zhou
,
Qing Liu
,
He Zhang
,
Sohrab Amirghodsi
,
Zhe Lin
,
Eli Shechtman
,
Jianbo Shi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.05590
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.05590
Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks. However, these generated images often exhibit perceptual artifacts in specific regions, necessitating manual correction. In this study, we present a comprehensive empirical examination of Perceptual Artifacts Localization (PAL) spanning diverse image synthesis endeavors. We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels across ten synthesis tasks. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. Additionally, we illustrate its proficiency in adapting to previously unseen models using minimal training samples. We further propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images. Through our experimental analyses, we elucidate several practical downstream applications, such as automated artifact rectification, non-referential image quality evaluation, and abnormal region detection in images. The dataset and code are released.
Related Topics
Concepts
Computer science
Artificial intelligence
Pipeline (software)
Artifact (error)
Inpainting
Perception
Computer vision
Zoom
Segmentation
Code (set theory)
Image (mathematics)
Pattern recognition (psychology)
Set (abstract data type)
Neuroscience
Lens (geology)
Programming language
Engineering
Petroleum engineering
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.05590
- https://arxiv.org/pdf/2310.05590
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387560208
All OpenAlex metadata
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https://openalex.org/W4387560208Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.05590Digital Object Identifier
- Title
-
Perceptual Artifacts Localization for Image Synthesis TasksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-09Full publication date if available
- Authors
-
Lingzhi Zhang, Zhengjie Xu, Connelly Barnes, Yuqian Zhou, Qing Liu, He Zhang, Sohrab Amirghodsi, Zhe Lin, Eli Shechtman, Jianbo ShiList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.05590Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.05590Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2310.05590Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Pipeline (software), Artifact (error), Inpainting, Perception, Computer vision, Zoom, Segmentation, Code (set theory), Image (mathematics), Pattern recognition (psychology), Set (abstract data type), Neuroscience, Lens (geology), Programming language, Engineering, Petroleum engineering, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.images | 12, 19 |
| abstract_inverted_index.labels | 64 |
| abstract_inverted_index.manual | 28 |
| abstract_inverted_index.model, | 71 |
| abstract_inverted_index.models | 5, 95 |
| abstract_inverted_index.region | 138 |
| abstract_inverted_index.study, | 32 |
| abstract_inverted_index.tasks. | 15, 68, 84 |
| abstract_inverted_index.unseen | 94 |
| abstract_inverted_index.Through | 117 |
| abstract_inverted_index.dataset | 53, 143 |
| abstract_inverted_index.diverse | 45 |
| abstract_inverted_index.exhibit | 21 |
| abstract_inverted_index.further | 101 |
| abstract_inverted_index.images, | 57 |
| abstract_inverted_index.images. | 116, 141 |
| abstract_inverted_index.minimal | 97 |
| abstract_inverted_index.present | 34 |
| abstract_inverted_index.propose | 102 |
| abstract_inverted_index.quality | 134 |
| abstract_inverted_index.several | 123 |
| abstract_inverted_index.trained | 72 |
| abstract_inverted_index.various | 14 |
| abstract_inverted_index.zoom-in | 105 |
| abstract_inverted_index.However, | 16 |
| abstract_inverted_index.abnormal | 137 |
| abstract_inverted_index.adapting | 91 |
| abstract_inverted_index.artifact | 63, 130 |
| abstract_inverted_index.creation | 9 |
| abstract_inverted_index.dataset, | 76 |
| abstract_inverted_index.pipeline | 107 |
| abstract_inverted_index.proposed | 75 |
| abstract_inverted_index.regions, | 26 |
| abstract_inverted_index.samples. | 99 |
| abstract_inverted_index.spanning | 44 |
| abstract_inverted_index.specific | 25 |
| abstract_inverted_index.training | 98 |
| abstract_inverted_index.Artifacts | 41 |
| abstract_inverted_index.analyses, | 120 |
| abstract_inverted_index.annotated | 59 |
| abstract_inverted_index.artifacts | 23, 79, 112 |
| abstract_inverted_index.automated | 129 |
| abstract_inverted_index.detection | 139 |
| abstract_inverted_index.elucidate | 122 |
| abstract_inverted_index.empirical | 37 |
| abstract_inverted_index.generated | 18, 56, 115 |
| abstract_inverted_index.introduce | 50 |
| abstract_inverted_index.localizes | 78 |
| abstract_inverted_index.per-pixel | 61 |
| abstract_inverted_index.practical | 124 |
| abstract_inverted_index.rectifies | 110 |
| abstract_inverted_index.released. | 147 |
| abstract_inverted_index.synthesis | 47, 67 |
| abstract_inverted_index.Perceptual | 40 |
| abstract_inverted_index.comprising | 54 |
| abstract_inverted_index.downstream | 125 |
| abstract_inverted_index.endeavors. | 48 |
| abstract_inverted_index.generative | 4 |
| abstract_inverted_index.illustrate | 87 |
| abstract_inverted_index.innovative | 104 |
| abstract_inverted_index.inpainting | 106 |
| abstract_inverted_index.perceptual | 22, 62, 111 |
| abstract_inverted_index.previously | 93 |
| abstract_inverted_index.seamlessly | 109 |
| abstract_inverted_index.correction. | 29 |
| abstract_inverted_index.effectively | 77 |
| abstract_inverted_index.evaluation, | 135 |
| abstract_inverted_index.examination | 38 |
| abstract_inverted_index.facilitated | 7 |
| abstract_inverted_index.proficiency | 89 |
| abstract_inverted_index.Localization | 42 |
| abstract_inverted_index.advancements | 1 |
| abstract_inverted_index.experimental | 119 |
| abstract_inverted_index.segmentation | 70 |
| abstract_inverted_index.Additionally, | 85 |
| abstract_inverted_index.applications, | 126 |
| abstract_inverted_index.comprehensive | 36 |
| abstract_inverted_index.necessitating | 27 |
| abstract_inverted_index.rectification, | 131 |
| abstract_inverted_index.non-referential | 132 |
| abstract_inverted_index.photo-realistic | 11 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile |