Cited by 644Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning ApplicationsAdvanced Neural Network Applications

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MirrorGAN: Learning Text-To-Image Generation by Redescription

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Qiao, Tingting, Zhang, Jing, Xu, Duanqing, Tao, Dacheng·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr.2019.00160

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Abstract

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].”
Method
Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 3×
“The image encoder is a convolutional neural network (CNN) [11] pretrained on ImageNet [5], and the decoder is a RNN [12].”
Method
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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].”
Method
BERT: 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].”
Method
Microsoft COCO: Common Objects in Context
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“We evaluated our model on two commonly used datasets, CUB bird dataset [30] and MS COCO dataset [17].”
Method
Learning 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].”
MethodInfluential
Unpaired 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].”
Generative Adversarial Nets
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

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10.1109/iccv.2017.629 · DOI · Paper page
taigman, 2017, Unsupervised crossdomain image generation, International Conference on Learning Representations (ICLR)

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Authors: Qiao, T., Zhang, J., Xu, D., & Tao, D.
Affiliations: The University of Sydney (AU)
University of Technology Sydney (AU)
ORCID: Zhang, Jing
Xu, Duanqing
Tao, Dacheng
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)
DOI: 10.1109/cvpr.2019.00160
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
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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

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MirrorGAN: Learning Text-To-Image Generation by Redescription

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