Emu Edit: Precise Image Editing via Recognition and Generation Tasks Article Swipe
Shelly Sheynin
,
Adam Polyak
,
Uriel Singer
,
Yuval Kirstain
,
Amit Zohar
,
Oron Ashual
,
Devi Parikh
,
Yaniv Taigman
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.10089
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.10089
Instruction-based image editing holds immense potential for a variety of applications, as it enables users to perform any editing operation using a natural language instruction. However, current models in this domain often struggle with accurately executing user instructions. We present Emu Edit, a multi-task image editing model which sets state-of-the-art results in instruction-based image editing. To develop Emu Edit we train it to multi-task across an unprecedented range of tasks, such as region-based editing, free-form editing, and Computer Vision tasks, all of which are formulated as generative tasks. Additionally, to enhance Emu Edit's multi-task learning abilities, we provide it with learned task embeddings which guide the generation process towards the correct edit type. Both these elements are essential for Emu Edit's outstanding performance. Furthermore, we show that Emu Edit can generalize to new tasks, such as image inpainting, super-resolution, and compositions of editing tasks, with just a few labeled examples. This capability offers a significant advantage in scenarios where high-quality samples are scarce. Lastly, to facilitate a more rigorous and informed assessment of instructable image editing models, we release a new challenging and versatile benchmark that includes seven different image editing tasks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.10089
- https://arxiv.org/pdf/2311.10089
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388788984
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388788984Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.10089Digital Object Identifier
- Title
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Emu Edit: Precise Image Editing via Recognition and Generation TasksWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-11-16Full publication date if available
- Authors
-
Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, Yaniv TaigmanList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.10089Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2311.10089Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2311.10089Direct OA link when available
- Concepts
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Image editing, Computer science, Task (project management), Benchmark (surveying), Video editing, Image (mathematics), Artificial intelligence, Process (computing), Generative grammar, Human–computer interaction, Programming language, Economics, Geodesy, Management, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.elements | 115 |
| abstract_inverted_index.includes | 186 |
| abstract_inverted_index.informed | 170 |
| abstract_inverted_index.language | 23 |
| abstract_inverted_index.learning | 94 |
| abstract_inverted_index.rigorous | 168 |
| abstract_inverted_index.struggle | 32 |
| abstract_inverted_index.advantage | 155 |
| abstract_inverted_index.benchmark | 184 |
| abstract_inverted_index.different | 188 |
| abstract_inverted_index.essential | 117 |
| abstract_inverted_index.examples. | 149 |
| abstract_inverted_index.executing | 35 |
| abstract_inverted_index.free-form | 74 |
| abstract_inverted_index.operation | 19 |
| abstract_inverted_index.potential | 5 |
| abstract_inverted_index.scenarios | 157 |
| abstract_inverted_index.versatile | 183 |
| abstract_inverted_index.abilities, | 95 |
| abstract_inverted_index.accurately | 34 |
| abstract_inverted_index.assessment | 171 |
| abstract_inverted_index.capability | 151 |
| abstract_inverted_index.embeddings | 102 |
| abstract_inverted_index.facilitate | 165 |
| abstract_inverted_index.formulated | 84 |
| abstract_inverted_index.generalize | 130 |
| abstract_inverted_index.generation | 106 |
| abstract_inverted_index.generative | 86 |
| abstract_inverted_index.multi-task | 43, 63, 93 |
| abstract_inverted_index.challenging | 181 |
| abstract_inverted_index.inpainting, | 137 |
| abstract_inverted_index.outstanding | 121 |
| abstract_inverted_index.significant | 154 |
| abstract_inverted_index.Furthermore, | 123 |
| abstract_inverted_index.compositions | 140 |
| abstract_inverted_index.high-quality | 159 |
| abstract_inverted_index.instructable | 173 |
| abstract_inverted_index.instruction. | 24 |
| abstract_inverted_index.performance. | 122 |
| abstract_inverted_index.region-based | 72 |
| abstract_inverted_index.Additionally, | 88 |
| abstract_inverted_index.applications, | 10 |
| abstract_inverted_index.instructions. | 37 |
| abstract_inverted_index.unprecedented | 66 |
| abstract_inverted_index.state-of-the-art | 49 |
| abstract_inverted_index.Instruction-based | 0 |
| abstract_inverted_index.instruction-based | 52 |
| abstract_inverted_index.super-resolution, | 138 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |