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Semantic Image Synthesis via Adversarial Learning
Open accessAbstract
In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e.g. intelligent image manipulation. We attempt to accomplish such synthesis: given a source image and a target text description, our model synthesizes images to meet two requirements: 1) being realistic while matching the target text description; 2) maintaining other image features that are irrelevant to the text description. The model should be able to disentangle the semantic information from the two modalities (image and text), and generate new images from the combined semantics. To achieve this, we proposed an end-to-end neural architecture that leverages adversarial learning to automatically learn implicit loss functions, which are optimized to fulfill the aforementioned two requirements. We have evaluated our model by conducting experiments on Caltech-200 bird dataset and Oxford-102 flower dataset, and have demonstrated that our model is capable of synthesizing realistic images that match the given descriptions, while still maintain other features of original images.
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“These can also be used for biological image synthesis [208, 209] and text-to-image synthesis [210, 211, 212].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [210]
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Nekamiche, N., Zakaria, C., Bouchareb, S., & Smaı̈li, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21743-2_25 · Cited by 3
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse IssuePreprint
Khorramshahi, P., Souri, H., Chellappa, R., & Feizi, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.11921 · Cited by 5
Remember What You have drawn: Semantic Image Manipulation with MemoryPreprint
Shi, X., Wu, Z., Lin, G., Cai, J., & Joty, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.12579 · Cited by 3
Xu, Z., Lin, T., Tang, H., Li, F., He, D., Sebe, N., Timofte, R., Gool, L. V., & Ding, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13333 · Cited by 3
CapGAN: Text-to-Image Synthesis Using Capsule GANs
Omar, M., Rehman, H. U., Samin, O. B., Alazab, M., Politano, G. M. M., & Benso, A. (2023) · Information · doi:10.3390/info14100552 · Cited by 2
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Metadata
- Authors
- Dong, H., Yu, S., Wu, C., & Guo, Y.
- Affiliations
- Imperial College London (GB)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5707-5715
- OpenAlex
- W2964313012
- Cited by
- 276
- References
- 49
- Citations by year
- 2026: 3 · 2025: 18 · 2024: 28 · 2023: 45 · 2022: 45 · 2021: 46 · 2020: 34 · 2019: 32 · 2018: 22 · 2017: 3
- FWCI
- 7.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Virginia Agricultural Experiment Station, Virginia Polytechnic Institute and State University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- semantic image synthesis, text-to-image generation, adversarial learning, image editing, realistic image synthesis, multimodal learning
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