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MirrorGAN: Learning Text-To-Image Generation by Redescription
Open accessAbstract
Generating an image from a given text description has two goals: visual realism and semantic consistency. Although significant progress has been made in generating high-quality and visually realistic images using generative adversarial networks, guaranteeing semantic consistency between the text description and visual content remains very challenging. In this paper, we address this problem by proposing a novel global-local attentive and semantic-preserving text-to-image-to-text framework called MirrorGAN. MirrorGAN exploits the idea of learning text-to-image generation by redescription and consists of three modules: a semantic text embedding module (STEM), a global-local collaborative attentive module for cascaded image generation (GLAM), and a semantic text regeneration and alignment module (STREAM). STEM generates word- and sentence-level embeddings. GLAM has a cascaded architecture for generating target images from coarse to fine scales, leveraging both local word attention and global sentence attention to progressively enhance the diversity and semantic consistency of the generated images. STREAM seeks to regenerate the text description from the generated image, which semantically aligns with the given text description. Thorough experiments on two public benchmark datasets demonstrate the superiority of MirrorGAN over other representative state-of-the-art methods.
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“The image encoder is a convolutional neural network (CNN) [11] pretrained on ImageNet [5], and the decoder is a RNN [12].”
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“The image encoder is a convolutional neural network (CNN) [11] pretrained on ImageNet [5], and the decoder is a RNN [12].”
MethodBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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“The image encoder is a convolutional neural network (CNN) [11] pretrained on ImageNet [5], and the decoder is a RNN [12].”
MethodBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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“Second, we only utilize a basic method for text embedding in STEM and image captioning in STREAM, which could be further improved, for example, by using the recently proposed BERT model [7] and state-of-the-art image captioning models [2, 3].”
MethodMicrosoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · cited 1×
“We evaluated our model on two commonly used datasets, CUB bird dataset [30] and MS COCO dataset [17].”
MethodLearning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · cited 4×
“The image encoder is a convolutional neural network (CNN) [11] pretrained on ImageNet [5], and the decoder is a RNN [12].”
MethodInfluentialUnpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · cited 1×
“Similar ideas to our own have recently been used in CycleGAN and DualGAN, which handle the bi-directional translations within two domains together [42, 36, 1], significantly advance image-to-image translation [14, 28, 15, 37, 23].”
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · cited 1×
“Similar ideas to our own have recently been used in CycleGAN and DualGAN, which handle the bi-directional translations within two domains together [43, 37, 1, 32], significantly advance image-to-image translation [14, 28, 15, 38, 23].”
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
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- taigman, 2017, Unsupervised crossdomain image generation, International Conference on Learning Representations (ICLR)
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Metadata
- Authors
- Qiao, T., Zhang, J., Xu, D., & Tao, D.
- Affiliations
- The University of Sydney (AU)
- University of Technology Sydney (AU)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1505-1514
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962845008
- Semantic Scholar
- 5af2d424bb38db7e6a72f7bec2cfd8a5bb8af7fb
- Cited by
- 644
- References
- 45
- Citations by year
- 2026: 17 · 2025: 71 · 2024: 99 · 2023: 134 · 2022: 102 · 2021: 113 · 2020: 92 · 2019: 16
- FWCI
- 26.32
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61806062)
- Cultural Heritage Administration
- 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 › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- text-to-image generation, semantic consistency, redescription, global-local attention, image-text alignment, generative adversarial networks
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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