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

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

MirrorGAN: Learning Text-To-Image Generation by Redescription

Open access

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

Open this paper in CoChat — free account Open PDF

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.

Integrity signals

Registry

Crossref · 10.1109/cvpr.2019.00160

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 2×
“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
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 1×
“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
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325 · cited 1×
“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
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].”
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
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · 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].”
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · cited 2×
“Followed [35], a pre-trained bi-directional LSTM [27] was used to calculate the semantic embedding from text descriptions.”
Method
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
Background
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · cited 7×
“As shown in Figure 1 (a) and (c), if an image generated by T2I is semantically consistent with the given text description, its redescription by I2T should have exactly the same semantics with the given text description.”
BackgroundInfluential
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · cited 13×
“The Inception Score and the R-precision were calculated accordingly as in [38, 34].”
MethodInfluential
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318 · cited 8×
“As shown in Figure 1 (a) and (c), if an image generated by T2I is semantically consistent with the given text description, its redescription by I2T should have exactly the same semantics with the given text description.”
BackgroundMethodInfluential
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2018) · 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/cvpr.2018.00636 · Cited by 5,416 · cited 2×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
BackgroundMethod
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Caltech-UCSD Birds-200-2011 Dataset
Wah, C. (2024) · CaltechAUTHORS (California Institute of Technology) · doi:10.57702/i9oeewf6 · Cited by 3,620
StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.629 · Cited by 3,021 · cited 12×
“proposed StackGAN to generate images in two separate stages [39].”
BackgroundMethodInfluential
Generative Image Inpainting with Contextual Attention
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., & Huang, T. S. (2018) · 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/cvpr.2018.00577 · Cited by 2,633 · 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 Text to Image Synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494 · cited 4×
“First, we compare MirrorGAN with state-of-the-art text-to-image methods [24, 25, 39, 40, 20, 35] using the Inception Score and R-precision score on both CUB and COCO datasets.”
BackgroundMethodInfluential
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Yi, Z., Zhang, H., Tan, P., & Gong, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.310 · Cited by 2,262 · 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].”
Saccade target selection and object recognition: evidence for a common attentional mechanism.
Deubel, H., & Schneider, W. (1996) · Vision Research · doi:10.1016/0042-6989(95)00294-4 · Cited by 2,108
Stacked Attention Networks for Image Question Answering
Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. J. (2016) · 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/cvpr.2016.10 · Cited by 2,072 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
Background
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00143 · Cited by 1,923
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2856256 · Cited by 1,267 · cited 6×
“First, we compare MirrorGAN with state-of-the-art text-to-image methods [24, 25, 39, 40, 20, 35] using the Inception Score and R-precision score on both CUB and COCO datasets.”
MethodResultInfluential
Unsupervised Cross-Domain Image GenerationPreprint
Taigman, Y., Polyak, A., & Wolf, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02200 · Cited by 956 · 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], sig-niﬁcantly advance image-to-image translation [14, 28, 15, 37, 23].”
Method
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
Xu, H., & Saenko, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_28 · Cited by 753 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
Background
Progressive Attention Guided Recurrent Network for Salient Object Detection
Zhang, X., Wang, T., Qi, J., Lu, H., & Wang, G. (2018) · 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/cvpr.2018.00081 · Cited by 685 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
Background
Video Captioning With Attention-Based LSTM and Semantic Consistency
Gao, L., Guo, Z. Z., Zhang, H., Xu, X., & Shen, H. T. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2729019 · Cited by 645 · cited 1×
“dresses visual similarities. Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21,6,18,40], image/video captioning [33,9,31], visual question answering [2,32,35, 22], and neural machine translation [19,8]. Attention can be modeled spa…”
Background
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., & Yosinski, J. (2017) · 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/cvpr.2017.374 · Cited by 627
Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism
Fırat, O., Cho, K., & Bengio, Y. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1101 · Cited by 616 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
Background
Top-down control of visual attention in object detection
Oliva, A., Torralba, A. B., Castelhano, M. S., & Henderson, J. M. (2004) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2003.1246946 · Cited by 474
Learning What and Where to DrawPreprint
Reed, S., Akata, Z., Mohan, S., Tenka, S., Schiele, B., & Lee, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02454 · Cited by 444 · cited 4×
“2.88 ± 0.04 7.88 ± 0.07 GAWWN [25] 3.62 ± 0.07 - StackGAN [38] 3.70 ± 0.04 8.45 ± 0.03 StackGAN++ [39] 3.82 ± 0.06 - PPGN [20] - 9.58 ± 0.21 AttnGAN [34] 4 It can be seen that the image details generated by At-tnGAN are lost, colors are inconsistent with the text descriptions ( 3 rd and 4 th column), and the shape loo…”
MethodInfluential
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
Liu, J., Gao, C., Meng, D., & Hauptmann, A. G. (2018) · 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/cvpr.2018.00545 · Cited by 428 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
Background
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired DataPreprint
Almahairi, A., Rajeswar, S., Sordoni, A., Bachman, P., & Courville, A. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10151 · Cited by 374 · 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].”
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis
Hong, S., Yang, D., Choi, J., & Lee, H. (2018) · 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/cvpr.2018.00833 · Cited by 350 · cited 1×
“Although significant progress has been made in generating visually realistic images using generative adversarial networks (GANs) such as in [39, 42, 35, 13], guaranteeing semantic alignment of the generated image with the input text remains challenging.”
Background
Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network
Zhang, Z., Xie, Y., & Yang, L. (2018) · 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/cvpr.2018.00649 · Cited by 332 · cited 2×
“proposed training a T2I generator with hierarchically nested adversarial objectives [42].”
Background
Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning
Wang, J., Jiang, W., Ma, L., Liu, W., & Xu, Y. (2018) · 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/cvpr.2018.00751 · Cited by 237 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
Background
Exploring Human-Like Attention Supervision in Visual Question Answering
Qiao, T., Dong, J., & Xu, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12272 · Cited by 100 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
BackgroundSelf-citation
GroupCap: Group-Based Image Captioning with Structured Relevance and Diversity Constraints
Chen, F., Ji, R., Sun, X., Wu, Y., & Su, J. (2018) · 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/cvpr.2018.00146 · Cited by 85 · cited 1×
“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
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
Wen, J., Liu, R., Zheng, N., Zheng, Q., Gong, Z., & Yuan, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015401 · Cited by 42 · 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].”
Exploring Human-like Attention Supervision in Visual Question AnsweringPreprint
Qiao, T., Dong, J., & Xu, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06308 · Cited by 42 · cited 1×
“Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”
BackgroundSelf-citation
Ancient Painting to Natural Image: A New Solution for Painting Processing
Qiao, T., Zhang, W., Zhang, M., Ma, Z., & Xu, D. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00061 · Cited by 22 · 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].”
Self-citation

Flagged first, then by citations

Research works printed but not linked: 2

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

Sorted by citations

DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02155 · Cited by 2,295
“There also exists work on text-to-image synthesis [14, 16, 19, 24, 27, 35, 36, 50, 51, 55, 58, 67, 74].”
Background
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
“Field Subfield Method Super-resolution SRGAN [63], ESRGAN [64], Cycle-in-Cycle GANs [65], SRDGAN [66], TGAN [67] DR-GAN [68], TP-GAN [69], PG2 [70], PSGAN [71], Image synthesis and manipulation APDrawingGAN [72], IGAN [73], Image processing and computer vision introspective adversarial networks [74], GauGAN [75] Textu…”
BackgroundSelf-citation
Blended Diffusion for Text-driven Editing of Natural Images
Avrahami, O., Lischinski, D., & Fried, O. (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/cvpr52688.2022.01767 · Cited by 889
“Recent works that leverage the tremendous progress in vision-language models and datadriven image generation have demonstrated that text-based interfaces for image creation and manipulation are now finally within reach [12, 23, 29, 30, 38, 39, 41, 47, 51, 57].”
Background
Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
“…and text-to-image generation are two generative tasks related to cross-modal matching, where captioning refers to generating a piece of text description for a given image or video [179] while text-to-image generation aims to generate a realistic image that matches the given text description [180].”
BackgroundSelf-citation
Vector Quantized Diffusion Model for Text-to-Image Synthesis
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., & Guo, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01043 · Cited by 720

See all 644

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

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 ›

Are you an author, or do you see a problem with this page? Request a review

MirrorGAN: Learning Text-To-Image Generation by Redescription

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks