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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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A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint
Ullah, U., Lee, J.-S., An, C.-H., Lee, H., Park, S.-Y., Baek, R., & Choi, H. (2022) · Sensors · doi:10.3390/s22186816 · Cited by 19
Word-Level Fine-Grained Story Visualization
Li, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_20 · Cited by 18
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing
Shi, J., Xu, N., Zheng, H., Smith, A., Luo, J., & Xu, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01911 · Cited by 18
Multimodality-guided Image Style Transfer using Cross-modal GAN Inversion
Wang, H., Wu, P., Rosa, K. D., Wang, C., & Shrivastava, A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00490 · Cited by 21
Zhang, Z., Zhao, Z., Zhang, Z., Huai, B., & Yuan, J. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413939 · Cited by 18
Pande, S., Chouhan, S., Sonavane, R., Walambe, R. A., Ghinea, G., & Kotecha, K. V. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.055 · Cited by 18
Learning by Imagination: A Joint Framework for Text-Based Image Manipulation and Change Captioning
Ak, K. E., Sun, Y., & Lim, J. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3154154 · Cited by 17
Text-Guided Human Image Manipulation via Image-Text Shared Space
Xu, X., Chen, Y.-C., Tao, X., & Jia, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3085339 · Cited by 16
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Human-in-the-Loop Design with Machine Learning
Wang, P., Peng, D., Li, L., Chen, L., Wu, C., Wang, X., Childs, P., & Guo, Y. (2019) · Proceedings of the ... International Conference on Engineering Design · doi:10.1017/dsi.2019.264 · Cited by 16
Self-citationAttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
Frolov, S., Sharma, A., Hees, J., Karayil, T., Raue, F., & Dengel, A. R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92659-5_23 · Cited by 16
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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