UniGS: Unified Representation for Image Generation and Segmentation Article Swipe
Lu Qi
,
Lehan Yang
,
Weidong Guo
,
Yu Xu
,
Bo Du
,
Varun Jampani
,
Ming–Hsuan Yang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.01985
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.01985
This paper introduces a novel unified representation of diffusion models for image generation and segmentation. Specifically, we use a colormap to represent entity-level masks, addressing the challenge of varying entity numbers while aligning the representation closely with the image RGB domain. Two novel modules, including the location-aware color palette and progressive dichotomy module, are proposed to support our mask representation. On the one hand, a location-aware palette guarantees the colors' consistency to entities' locations. On the other hand, the progressive dichotomy module can efficiently decode the synthesized colormap to high-quality entity-level masks in a depth-first binary search without knowing the cluster numbers. To tackle the issue of lacking large-scale segmentation training data, we employ an inpainting pipeline and then improve the flexibility of diffusion models across various tasks, including inpainting, image synthesis, referring segmentation, and entity segmentation. Comprehensive experiments validate the efficiency of our approach, demonstrating comparable segmentation mask quality to state-of-the-art and adaptability to multiple tasks. The code will be released at \href{https://github.com/qqlu/Entity}{https://github.com/qqlu/Entity}.
Related Topics
Concepts
Computer science
Inpainting
Segmentation
Palette (painting)
Artificial intelligence
Representation (politics)
Pipeline (software)
Benchmark (surveying)
RGB color model
Code (set theory)
Image (mathematics)
Computer vision
Pattern recognition (psychology)
Set (abstract data type)
Geodesy
Geography
Operating system
Law
Politics
Programming language
Political science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.01985
- https://arxiv.org/pdf/2312.01985
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389364325
All OpenAlex metadata
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https://openalex.org/W4389364325Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.01985Digital Object Identifier
- Title
-
UniGS: Unified Representation for Image Generation and SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-04Full publication date if available
- Authors
-
Lu Qi, Lehan Yang, Weidong Guo, Yu Xu, Bo Du, Varun Jampani, Ming–Hsuan YangList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.01985Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.01985Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.01985Direct OA link when available
- Concepts
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Computer science, Inpainting, Segmentation, Palette (painting), Artificial intelligence, Representation (politics), Pipeline (software), Benchmark (surveying), RGB color model, Code (set theory), Image (mathematics), Computer vision, Pattern recognition (psychology), Set (abstract data type), Geodesy, Geography, Operating system, Law, Politics, Programming language, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.while | 31 |
| abstract_inverted_index.across | 125 |
| abstract_inverted_index.binary | 95 |
| abstract_inverted_index.decode | 84 |
| abstract_inverted_index.employ | 113 |
| abstract_inverted_index.entity | 29, 135 |
| abstract_inverted_index.masks, | 23 |
| abstract_inverted_index.models | 9, 124 |
| abstract_inverted_index.module | 81 |
| abstract_inverted_index.search | 96 |
| abstract_inverted_index.tackle | 103 |
| abstract_inverted_index.tasks, | 127 |
| abstract_inverted_index.tasks. | 156 |
| abstract_inverted_index.closely | 35 |
| abstract_inverted_index.cluster | 100 |
| abstract_inverted_index.colors' | 69 |
| abstract_inverted_index.domain. | 40 |
| abstract_inverted_index.improve | 119 |
| abstract_inverted_index.knowing | 98 |
| abstract_inverted_index.lacking | 107 |
| abstract_inverted_index.module, | 52 |
| abstract_inverted_index.numbers | 30 |
| abstract_inverted_index.palette | 48, 66 |
| abstract_inverted_index.quality | 149 |
| abstract_inverted_index.support | 56 |
| abstract_inverted_index.unified | 5 |
| abstract_inverted_index.various | 126 |
| abstract_inverted_index.varying | 28 |
| abstract_inverted_index.without | 97 |
| abstract_inverted_index.aligning | 32 |
| abstract_inverted_index.colormap | 19, 87 |
| abstract_inverted_index.modules, | 43 |
| abstract_inverted_index.multiple | 155 |
| abstract_inverted_index.numbers. | 101 |
| abstract_inverted_index.pipeline | 116 |
| abstract_inverted_index.proposed | 54 |
| abstract_inverted_index.released | 161 |
| abstract_inverted_index.training | 110 |
| abstract_inverted_index.validate | 139 |
| abstract_inverted_index.approach, | 144 |
| abstract_inverted_index.challenge | 26 |
| abstract_inverted_index.dichotomy | 51, 80 |
| abstract_inverted_index.diffusion | 8, 123 |
| abstract_inverted_index.entities' | 72 |
| abstract_inverted_index.including | 44, 128 |
| abstract_inverted_index.referring | 132 |
| abstract_inverted_index.represent | 21 |
| abstract_inverted_index.addressing | 24 |
| abstract_inverted_index.comparable | 146 |
| abstract_inverted_index.efficiency | 141 |
| abstract_inverted_index.generation | 12 |
| abstract_inverted_index.guarantees | 67 |
| abstract_inverted_index.inpainting | 115 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.locations. | 73 |
| abstract_inverted_index.synthesis, | 131 |
| abstract_inverted_index.consistency | 70 |
| abstract_inverted_index.depth-first | 94 |
| abstract_inverted_index.efficiently | 83 |
| abstract_inverted_index.experiments | 138 |
| abstract_inverted_index.flexibility | 121 |
| abstract_inverted_index.inpainting, | 129 |
| abstract_inverted_index.large-scale | 108 |
| abstract_inverted_index.progressive | 50, 79 |
| abstract_inverted_index.synthesized | 86 |
| abstract_inverted_index.adaptability | 153 |
| abstract_inverted_index.entity-level | 22, 90 |
| abstract_inverted_index.high-quality | 89 |
| abstract_inverted_index.segmentation | 109, 147 |
| abstract_inverted_index.Comprehensive | 137 |
| abstract_inverted_index.Specifically, | 15 |
| abstract_inverted_index.demonstrating | 145 |
| abstract_inverted_index.segmentation, | 133 |
| abstract_inverted_index.segmentation. | 14, 136 |
| abstract_inverted_index.location-aware | 46, 65 |
| abstract_inverted_index.representation | 6, 34 |
| abstract_inverted_index.representation. | 59 |
| abstract_inverted_index.state-of-the-art | 151 |
| abstract_inverted_index.\href{https://github.com/qqlu/Entity}{https://github.com/qqlu/Entity}. | 163 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |