AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
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In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.
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Current multi-modal and audio-visual deepfake detectors are often based on attention-based fusion of heterogeneous data streams from multiple modalities.
“Current multi-modal deepfake detectors are often based on the attention-based fusion of heterogeneous data streams from multiple modalities.”
From Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization (2024) · cites this paper as [59]
Section 3.2 describes attention-based matching between images and text for image generation, not multimodal deepfake detection. Searches for deepfake detectors and audio-visual fusion found no passage addressing whether current detectors often use attention-based fusion. (LLM, conf=0.77)
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
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 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
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 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
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 19,268 · see citing works
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · see citing works
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works
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 · see citing works
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 · see citing works
Caltech-UCSD Birds-200-2011 Dataset
Wah, C. (2024) · CaltechAUTHORS (California Institute of Technology) · doi:10.57702/i9oeewf6 · Cited by 3,875 · see citing works
Conditional Image Generation with PixelCNN DecodersPreprint
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016) · Neural Information Processing Systems · doi:10.48550/arxiv.1606.05328 · Cited by 3,066 · see citing works
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 · see citing works
Self-citationGenerative 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 · see citing works
Deep Generative Image Models using a Laplacian Pyramid of Adversarial NetworksPreprint
Denton, E., Chintala, S., Szlam, A., & Fergus, R. (2015) · Neural Information Processing Systems · doi:10.48550/arxiv.1506.05751 · Cited by 2,360 · see citing works
Learning deep structured semantic models for web search using clickthrough data
Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. P. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505665 · Cited by 2,054 · see citing works
Self-citationStacked 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 2,036 · see citing works
Self-citationContext Encoding for Semantic Segmentation
Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., & Agrawal, A. (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.2018.00747 · Cited by 1,510 · see citing works
Self-citationDRAW: A Recurrent Neural Network For Image GenerationPreprint
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., & Wierstra, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04623 · Cited by 1,394 · see citing works
From captions to visual concepts and back
Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015) · 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.2015.7298754 · Cited by 1,305 · see citing works
Self-citationDensely Connected Pyramid Dehazing Network
Zhang, H., & Patel, V. M. (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.2018.00337 · Cited by 1,283 · see citing works
Self-citationImage De-Raining Using a Conditional Generative Adversarial Network
Zhang, H., Sindagi, V. A., & Patel, V. M. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2920407 · Cited by 1,232 · see citing works
Self-citationStackGAN++: 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 1,222 · see citing works
Self-citationLearning Deep Representations of Fine-Grained Visual Descriptions
Reed, S., Akata, Z., Lee, H., & Schiele, B. (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.2016.13 · Cited by 802 · see citing works
DRAW: A Recurrent Neural Network For Image Generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., & Wierstra, D. (2015) · Cited by 785 · see citing works
Minimum classification error rate methods for speech recognition
Juang, B.-H. F., Hou, W., & Lee, C. (1997) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.568732 · Cited by 618 · see citing works
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 597 · see citing works
VQA: Visual Question Answering: www.visualqa.org
Agrawal, A., Lu, J., Antol, S., Mitchell, M., Zitnick, C. L., Parikh, D., & Batra, D. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0966-6 · Cited by 570 · see citing works
Semantic Compositional Networks for Visual Captioning
Gan, Z., Gan, C., He, X., Pu, Y., Tran, K., Gao, J., Carin, L., & Deng, L. (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.2017.127 · Cited by 462 · see citing works
Self-citationZhang, Z., Yang, L., & Zheng, 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.2018.00963 · Cited by 455 · see citing works
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Zhu, Y., Elhoseiny, M., Liu, B., Peng, X., & Elgammal, A. (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.2018.00111 · Cited by 412 · see citing works
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 411 · see citing works
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
Zhang, Z., Xie, Y., Xing, F., McGough, M., & Yang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.378 · Cited by 354 · see citing works
Generating Images from Captions with AttentionPreprint
Mansimov, E., Parisotto, E., Ba, J. L., & Salakhutdinov, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02793 · Cited by 334 · see citing works
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 331 · see citing works
Multi-style Generative Network for Real-Time Transfer
Zhang, H., & Dana, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11018-5_32 · Cited by 258 · see citing works
Self-citationParallel Multiscale Autoregressive Density EstimationPreprint
Reed, S. E., van den Oord, A., Kalchbrenner, N., Colmenarejo, S. G., Wang, Z.-Y., Chen, Y., Belov, D., & de Freitas, N. (2017) · International Conference on Machine Learning · doi:10.48550/arxiv.1703.03664 · Cited by 223 · see citing works
Discriminative learning in sequential pattern recognition
He, X., Li, D., & Chou, W. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.926652 · Cited by 148 · see citing works
Self-citationImproving GANs Using Optimal TransportPreprint
Salimans, T., Zhang, H., Radford, A., & Metaxas, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.05573 · Cited by 120 · see citing works
Self-citation
Flagged first, then by citations
4 of the 47 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- A. Agrawal, J. Lu, S. Antol, M. Mitchell, C. L. Zitnick, D. Parikh, and D. Batra. VQA: visual question answering. IJCV, 123(1):4-31, 2017.
- I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde- Farley, S. Ozair, A. C. Courville, and Y. Bengio. Generative ad- versarial nets. In NIPS, 2014. 1, 2
- M. Schuster and K. K. Paliwal. Bidirectional recurrent neural net- works. IEEE Trans. Signal Processing, 45(11):2673-2681, 1997. 4
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Park, T., Liu, M.-Y., Wang, T.-C., & Zhu, J.-Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00244 · Cited by 2,974 · see citing works
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Jabbar, A., Li, X., & Omar, B. (2020) · ACM Computing Surveys · doi:10.1145/3463475 · Cited by 387 · see citing works
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Zhang, H., Koh, J. Y., Baldridge, J., Lee, H., & Yang, Y. (2021) · 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/cvpr46437.2021.00089 · Cited by 359 · see citing works
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Xia, W., Yang, Y., Xue, J.-H., & Wu, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00229 · Cited by 376 · see citing works
Yasarla, R., & Patel, V. M. (2019) · 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.2019.00860 · Cited by 345 · see citing works
Avrahami, O., Fried, O., & Lischinski, D. (2023) · ACM Transactions on Graphics · doi:10.1145/3592450 · Cited by 345 · see citing works
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Sun, G., Wang, W., Dai, J., & Gool, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58536-5_21 · Cited by 346 · see citing works
Seeing What a GAN Cannot GeneratePreprint
(2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 335 · see citing works
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 331 · see citing works
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DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
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VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization
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Object-Driven Text-To-Image Synthesis via Adversarial Training
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Self-citationAutoGAN: Neural Architecture Search for Generative Adversarial Networks
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Generative Adversarial Networks for face generation: A survey
Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/1122445.1122456 · Cited by 326 · see citing works
Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images
Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., & Shao, L. (2019) · 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.2019.00218 · Cited by 308 · see citing works
Self-citationDeep learning based computer vision approaches for smart agricultural applications
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Attention, please! A survey of neural attention models in deep learning
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Compositional Visual Generation with Composable Diffusion Models
Liu, N., Li, S., Du, Y., Torralba, A. B., & Tenenbaum, J. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19790-1_26 · Cited by 302 · see citing works
A survey on data‐efficient algorithms in big data era
Adadi, A. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00419-9 · Cited by 331 · see citing works
All are Worth Words: A ViT Backbone for Diffusion Models
Bao, F., Nie, S., Xue, K., Cao, Y., Li, C., Su, H., & Zhu, J. (2023) · 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/cvpr52729.2023.02171 · Cited by 296 · see citing works
Proceedings of the 32nd ACM International Conference on Multimedia
Pan, Q., Zhao, Z. W., Wang, Z., Long, S., Wu, Y., Ji, W., Liang, H., & Liang, R. (2024) · doi:10.1145/3664647 · Cited by 290 · see citing works
ManiGAN: Text-Guided Image Manipulation
Li, B., Qi, X., Lukasiewicz, T., & Torr, P. H. S. (2020) · 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/cvpr42600.2020.00790 · Cited by 287 · see citing works
Hertzmann, A. (2018) · Arts · doi:10.3390/arts7020018 · Cited by 265 · see citing works
CLIPstyler: Image Style Transfer with a Single Text Condition
Kwon, G., & Ye, J. 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/cvpr52688.2022.01753 · Cited by 262 · see citing works
Multi-style Generative Network for Real-Time Transfer
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Self-citationAdversarial Representation Learning for Text-to-Image Matching
Sarafianos, N., Xu, X., & Kakadiaris, I. A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00591 · Cited by 258 · see citing works
A Review on Generative Adversarial Networks: Algorithms, Theory, and ApplicationsPreprint
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.06937 · Cited by 252 · see citing works
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Han, C., Rundo, L., Araki, R., Nagano, Y., Furukawa, Y., Mauri, G., Nakayama, H., & Hayashi, H. (2019) · IEEE Access · doi:10.1109/access.2019.2947606 · Cited by 253 · see citing works
Effectively Unbiased FID and Inception Score and Where to Find Them
Chong, M. J., & Forsyth, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00611 · Cited by 242 · see citing works
StoryGAN: A Sequential Conditional GAN for Story Visualization
Li, Y., Gan, Z., Shen, Y., Liu, J., Cheng, Y., Wu, Y., Carin, L., Carlson, D., & Gao, J. (2019) · 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.2019.00649 · Cited by 227 · see citing works
Self-citationTang, H., Xu, D., Sebe, N., Wang, Y., Corso, J. J., & Yan, Y. (2019) · 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.2019.00252 · Cited by 226 · see citing works
More Control for Free! Image Synthesis with Semantic Diffusion Guidance
Liu, X., Park, D. H., Azadi, S., Zhang, G., Chopikyan, A., Hu, Y., Shi, H., Rohrbach, A., & Darrell, T. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00037 · Cited by 239 · see citing works
Text2LIVE: Text-Driven Layered Image and Video Editing
Bar-Tal, O., Ofri-Amar, D., Fridman, R., Kasten, Y., & Dekel, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19784-0_41 · Cited by 222 · see citing works
Semantics Disentangling for Text-To-Image Generation
Yin, G., Liu, B., Sheng, L., Yu, N., Wang, X., & Shao, J. (2019) · 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.2019.00243 · Cited by 218 · see citing works
Adversarial text-to-image synthesis: A review
Frolov, S., Hinz, T., Raue, F., Hees, J., & Dengel, A. R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.019 · Cited by 214 · see citing works
Few-shot Video-to-Video SynthesisPreprint
Wang, T.-C., Liu, M.-Y., Tao, A., Liu, G., Kautz, J., & Catanzaro, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12713 · Cited by 235 · see citing works
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
Guo, C., Zuo, X., Wang, S., & Cheng, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19833-5_34 · Cited by 206 · see citing works
A Review: Generative Adversarial Networks
Gonog, L., & Zhou, Y. (2019) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2019.8833686 · Cited by 205 · see citing works
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and Fusion
Wang, Y. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3408317 · Cited by 196 · see citing works
Towards Language-Free Training for Text-to-Image Generation
Zhou, Y., Zhang, R., Chen, C.-Y., Li, C.-Y., Tensmeyer, C., Yu, T., Gu, J., Xu, J., & Sun, T. (2021) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.01738 · Cited by 242 · see citing works
Kar, A. K., Varsha, P. S., & Rajan, S. (2023) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-023-00356-x · Cited by 209 · see citing works
Text to Image Generation with Semantic-Spatial Aware GAN
Liao, W., Hu, K., Yang, M. Y., & Rosenhahn, B. (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/cvpr52688.2022.01765 · Cited by 194 · see citing works
COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network
Jiang, Y., Chen, H., Loew, M. H., & Ko, H. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3042523 · Cited by 190 · see citing works
AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment
Li, C., Zhang, Z.-C., Wu, H., Sun, W., Min, X.-K., Liu, X.-H., Zhai, G.-T., & Lin, W.-S. (2023) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2023.3319020 · Cited by 288 · see citing works
A Survey of AI-Generated Content (AIGC)
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2024) · ACM Computing Surveys · doi:10.1145/3704262 · Cited by 1,099 · see citing works
Vision-Language Models in Remote Sensing: Current progress and future trends
Xiang, L., Wen, C., Hu, Y., Yuan, Z., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3383473 · Cited by 199 · see citing works
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
Chen, K., Choy, C., Savva, M., Chang, A. X., Funkhouser, T. A., & Savarese, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_7 · Cited by 187 · see citing works
Islam, A., Long, C., Basharat, A., & Hoogs, A. J. (2020) · 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/cvpr42600.2020.00473 · Cited by 184 · see citing works
Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Hong, F.-T., Zhang, L., Shen, L., & Xu, D. (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/cvpr52688.2022.00339 · Cited by 190 · see citing works
Liu, C., Mao, Z., Liu, A.-A., Zhang, T., Wang, B., & Zhang, Y. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350869 · Cited by 206 · see citing works
Crop leaf disease recognition based on Self-Attention convolutional neural network
Zeng, W., & Li, M. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105341 · Cited by 182 · see citing works
Lee, J., Kim, E., Lee, Y., Kim, D., Chang, J., & Choo, J. (2020) · 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/cvpr42600.2020.00584 · Cited by 177 · see citing works
GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis
Tao, M., Bao, B., Tang, H., & Xu, C. (2023) · 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/cvpr52729.2023.01366 · Cited by 181 · see citing works
DALL-EVAL: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models
Cho, J., Zala, A., & Bansal, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00283 · Cited by 199 · see citing works
Translation between Molecules and Natural Language
Edwards, C., Lai, T., Ros, K., Honke, G., Cho, K., & Ji, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.26 · Cited by 225 · see citing works
Image synthesis with adversarial networks: A comprehensive survey and case studies
Shamsolmoali, P., Zareapoor, M., Granger, É., Zhou, H., Wang, R., Celebi, M. E., & Yang, J. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.014 · Cited by 176 · see citing works
Training-Free Layout Control with Cross-Attention Guidance
Chen, M., Laina, I., & Vedaldi, A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00526 · Cited by 180 · see citing works
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
Wu, C., Liang, J., Ji, L., Yang, F., Fang, Y., Jiang, D., & Duan, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19787-1_41 · Cited by 167 · see citing works
Quantum State Tomography with Conditional Generative Adversarial Networks
Ahmed, S., Muñoz, C. S., Nori, F., & Kockum, A. F. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.140502 · Cited by 164 · see citing works
- Authors
- Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X.
- Affiliations
- Lehigh University (US)
- Microsoft (United States) (US)
- Rutgers, The State University of New Jersey (US)
- Duke University (US)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1316-1324
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963966654
- Cited by
- 1,923
- References
- 47
- Citations by year
- 2026: 94 · 2025: 246 · 2024: 322 · 2023: 313 · 2022: 230 · 2021: 289 · 2020: 243 · 2019: 157 · 2018: 28 · 2017: 1
- FWCI
- 54.03
- 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)
- 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, generative adversarial networks, attention mechanism, attention visualization
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.
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