AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Attention Is All You Need

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

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

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

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

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

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

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067

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

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

Notice of Removal: DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation

Lyu, Y., Lin, T., Li, F., He, D., Dong, J., & Tan, T. (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/cvpr52729.2023.00666 · Cited by 20

Attention-Bridged Modal Interaction for Text-to-Image Generation

Tan, H., Yin, B., Xu, K., Wang, H., Liu, X., & Li, X. S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3347971 · Cited by 20

DMF-GAN: Deep Multimodal Fusion Generative Adversarial Networks for Text-to-Image Synthesis

Yang, B., Xiang, X., Kong, W., Zhang, J., & Peng, Y. K. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3358086 · Cited by 21

Interpretable Object Recognition by Semantic Prototype Analysis

Wan, Q., Wang, R., & Chen, X. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00085 · Cited by 20

Material Palette: Extraction of Materials from a Single Image

Lopes, I., Pizzati, F., & de Charette, R. (2024) · 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/cvpr52733.2024.00419 · Cited by 22

CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models

Meral, T. H. S., Simsar, E., Tombari, F., & Yanardag, P. (2024) · 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/cvpr52733.2024.00860 · Cited by 35

Distilling Diffusion Models Into Conditional GANs

Kang, M., Zhang, R., Barnes, C., Paris, S., Kwak, S., Park, J., Shechtman, E., Zhu, J.-Y., & Park, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73390-1_25 · Cited by 24

Hierarchically-Fused Generative Adversarial Network for Text to Realistic Image Synthesis

Huang, X., Wang, M., & Gong, M. (2019) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2019.00018 · Cited by 20

Query is GAN: Scene Retrieval With Attentional Text-to-Image Generative Adversarial Network

Yanagi, R., Togo, R., Ogawa, T., & Haseyama, M. (2019) · IEEE Access · doi:10.1109/access.2019.2947409 · Cited by 22

A multi-task learning model with adversarial data augmentation for classification of fine-grained images

Fu, Y., Li, X., & Ye, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.002 · Cited by 20

Image Generation with Gans-based Techniques: A Survey

Esfahani, S. N., & Latifi, S. (2019) · International Journal of Computer Science and Information Technology · doi:10.5121/ijcsit.2019.11503 · Cited by 20

Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation

Mao, F., Ma, B., Chang, H., Shan, S., & Chen, X. (2020) · Science China Information Sciences · doi:10.1007/s11432-020-2900-x · Cited by 21

DGattGAN: Cooperative Up-Sampling Based Dual Generator Attentional GAN on Text-to-Image Synthesis

Zhang, H., Zhu, H., Yang, S., & Li, W. (2021) · IEEE Access · doi:10.1109/access.2021.3058674 · Cited by 23

Maria: A Visual Experience Powered Conversational Agent

Liang, Z., Hu, H., Xu, C., Tao, C., Geng, X., Chen, Y., Liang, F., & Jiang, D. (2021) · doi:10.18653/v1/2021.acl-long.435 · Cited by 23

Estimation with Uncertainty via Conditional Generative Adversarial Networks

Lee, M., & Seok, J. (2021) · Sensors · doi:10.3390/s21186194 · Cited by 20

Learning to Rank Method Combining Multi-Head Self-Attention with Conditional Generative Adversarial Nets

Li, J., Zeng, H., Lei, P., Zhu, J., & Liu, Z. (2022) · Array · doi:10.1016/j.array.2022.100205 · Cited by 20

Deepfakes in digital media forensics: Generation, AI-based detection and challenges

Bendiab, G., Haiouni, H., Moulas, I., & Shiaeles, S. N. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103935 · Cited by 24

CLIP-GAN: Stacking CLIPs and GAN for Efficient and Controllable Text-to-Image Synthesis

Hou, Y., Zhang, W., Zhu, Z., & Yu, H. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3535304 · Cited by 20

End-to-End Text-to-Image Synthesis with Spatial Constrains

Wang, M., Lang, C., Liang, L., Feng, S., Wang, T., & Gao, Y. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3391709 · Cited by 19

Vulnerability vs. Reliability: Disentangled Adversarial Examples for Cross-Modal Learning

Li, C., Tang, H., Deng, C. D., Zhan, L., & Liu, W. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403084 · Cited by 19

Person image synthesis through siamese generative adversarial network

Chen, Y., Xia, S., Zhao, J., Jian, M., Zhou, Y. Z., Niu, Q., Yao, R., & Zhu, D. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.09.004 · Cited by 19

IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning

Liu, Z., Deng, J., Li, L., Cai, S., Xu, Q., Wang, S., & Huang, Q. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413777 · Cited by 19

S2IGAN: Speech-to-Image Generation via Adversarial Learning

Wang, X., Qiao, T., Zhu, J., Hanjalić, A., & Scharenborg, O. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1759 · Cited by 19

Language-Guided Global Image Editing via Cross-Modal Cyclic Mechanism

Jiang, W., Xu, N., Wang, J., Gao, C., Shi, J., Lin, Z., & Liu, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00212 · Cited by 19

R-GAN: Exploring Human-like Way for Reasonable Text-to-Image Synthesis via Generative Adversarial Networks

Qiao, Y., Chen, Q., Deng, C., Ding, N., Qi, Y., Tan, M., Ren, X., & Wu, Q. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475363 · Cited by 19

Delphi-based visual scenarios: An innovative use of generative adversarial networks

Zio, S. D., Calleo, Y., & Bolzan, M. (2023) · Futures · doi:10.1016/j.futures.2023.103280 · Cited by 19

A Sample Augmentation Method for Side-Scan Sonar Full-Class Images That Can Be Used for Detection and Segmentation

Yang, Z., Zhao, J., Yu, Y., & Huang, C. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3371051 · Cited by 20

Improved-StoryGAN for sequential images visualization

Li, C., Kong, L., & Zhou, Z. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102956 · Cited by 19

Artificial intelligence augmented design iteration support

Zhou, C., Chai, C., Liao, J., Chen, Z., & Shi, J. (2020) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid51228.2020.00086 · Cited by 21

DCFGAN: Dynamic Convolutional Fusion Generative Adversarial Network for Text-to-Image Synthesis

Tao, M., Wu, S., Zhang, X., & Wang, C. (2020) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba50161.2020.9277299 · Cited by 19

ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation

Wang, J., Lu, G., Xu, H., Li, Z., Xu, C., & Fu, Y. (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/cvpr52688.2022.01044 · Cited by 21

Semantic Bottleneck Scene GenerationPreprint

Azadi, S., Tschannen, M., Tzeng, E., Gelly, S., Darrell, T., & Lučić, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11357 · Cited by 19

SegAttnGAN: Text to Image Generation with Segmentation AttentionPreprint

Gou, Y., Wu, Q., Li, M., Gong, B., & Han, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.12444 · Cited by 19

Interspeech 2020

Baquero-Arnal, P., Jorge-Cano, J., Pastor, A. G., Silvestre-Cerdà, J. A., Iranzo-Sánchez, J., Navarro, J. A. S., Saiz, J. C., & Juan, A. (2020) · doi:10.21437/interspeech.2020 · Cited by 19

Focus your attention: A focal attention for multimodal learning

Liu, C., Mao, Z., Zhang, T., Liu, A.-A., Wang, B., & Zhang, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3046855 · Cited by 17

Generative Adversarial Network-Based Experience Design for Visual Communication: An Innovative Exploration in Digital Media Arts

Gao, M., & Pu, P. (2024) · IEEE Access · doi:10.1109/access.2024.3419212 · Cited by 19

3D Point Cloud on Semantic Information for Wheat Reconstruction

Yang, Y., Zhang, J., Wu, K., Zhang, X., Sun, J., Peng, S., Li, J., & Wang, M. (2021) · Agriculture · doi:10.3390/agriculture11050450 · Cited by 18

DualG-GAN, a Dual-channel Generator based Generative Adversarial Network for text-to-face synthesis

Luo, X., He, X., Chen, X., Qing, L., & Zhang, J. C. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.08.016 · Cited by 18

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

Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation.

Wu, X., Zhao, H., Zheng, L., Ding, S., & Li, X. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547821 · Cited by 18

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

Clever little tricks: A socio-technical history of text-to-image generative models

Steinfeld, K. (2023) · International Journal of Architectural Computing · doi:10.1177/14780771231168230 · Cited by 21

Distill The Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation

Peng, R., YaWen, Z., & Zhao, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.152 · 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

SAW-GAN: Multi-granularity Text Fusion Generative Adversarial Networks for text-to-image generation

Jin, D., Yu, Q., Yu, L., & Qi, M. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111795 · Cited by 18

RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation

Liu, H., & Bhanu, B. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3402143 · Cited by 18

TextIR: A Simple Framework for Text-Based Editable Image Restoration

Bai, Y., Wang, C., Xie, S., Dong, C., Yuan, C., & Wang, Z. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3550844 · Cited by 29

Text-Guided Image Inpainting

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

EfficientAutoGAN: Predicting the Rewards in Reinforcement-Based Neural Architecture Search for Generative Adversarial Networks

Fan, Y., Tang, X., Zhou, G., & Shen, J. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3040796 · Cited by 18

Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection

Chen, C., Ma, H., Yao, G., Lv, N., Yang, H., Li, C., & Wan, S. (2021) · Remote Sensing · doi:10.3390/rs13101894 · Cited by 18

Development and deployment of a generative model-based framework for text to photorealistic image generation

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

Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep Learning Approaches

Wang, W., Hu, Y., Luo, Y., & Zhang, T. (2020) · Sensing and Imaging · doi:10.1007/s11220-020-00285-4 · Cited by 20

A Survey on the Progression and Performance of Generative Adversarial Networks

Ghosh, B., Dutta, I. K., Totaro, M. W., & Bayoumi, M. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225510 · Cited by 18

Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based Customization

Liu, Y., An, J., Zhang, W., Wu, D., Gu, J., Lin, Z., & Wang, W. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681243 · Cited by 17

Digital Watermarking Technology for AI-Generated Images: A Survey

Luo, H., Li, L., & Li, J. (2025) · Mathematics · doi:10.3390/math13040651 · Cited by 22

Imagine it for me: Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts.

Zhu, Y., Elhoseiny, M., Liu, B., & Elgammal, A. (2017) · Cited by 17

Scene Graph tO Image Generation with Contextualized Object Layout Refinement

Ivgi, M., Benny, Y., David, A. B., Berant, J., & Wolf, L. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506651 · Cited by 17

Collaborative Learning with Disentangled Features for Zero-shot Domain Adaptation

Jhoo, W. Y., & Heo, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00877 · Cited by 19

A Comprehensive Review on Image Synthesis with Adversarial Networks: Theory, Literature, and Applications

Tyagi, S., & Yadav, D. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09672-w · Cited by 18

Text-to-Traffic Generative Adversarial Network for Traffic Situation Generation

Huo, G., Zhang, Y., Wang, B., Hu, Y., & Yin, B. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3136143 · Cited by 17

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

TiGAN: Text-Based Interactive Image Generation and Manipulation

Zhou, Y., Zhang, R., Gu, J., Tensmeyer, C., Yu, T., Chen, C., Xu, J., & Sun, T. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20270 · Cited by 17

AraBERT and DF-GAN fusion for Arabic text-to-image generation

Bahani, M., Ouaazizi, A. E., & Maalmi, K. (2022) · Array · doi:10.1016/j.array.2022.100260 · Cited by 18

Enhanced Text-to-Image Synthesis With Self-Supervision

Tan, Y. X., Lee, C. P., Neo, M., Lim, K. M., & Lim, J. Y. (2023) · IEEE Access · doi:10.1109/access.2023.3268869 · Cited by 17

MISL: Multi-grained image-text semantic learning for text-guided image inpainting

Wu, X., Zhao, K., Huang, Q., Wang, Q. J., Yang, Z., & Hao, G. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109961 · Cited by 17

MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models

Zhao, J., Zheng, H., Wang, C., Lan, L., & Yang, W. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02065 · Cited by 19

MCA: Moment Channel Attention Networks

Jiang, Y., Jiang, Z., Han, L., Huang, Z., & Zheng, N. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28035 · Cited by 17

Text-Guided Explorable Image Super-Resolution

Gandikota, K. V., & Chandramouli, P. (2024) · 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/cvpr52733.2024.02447 · Cited by 20

Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning

Cui, W., Zheng, G., & Wang, W. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.444 · Cited by 17

A survey of hardware architectures for generative adversarial networks

Shrivastava, N., Hanif, M. A., Mittal, S., Sarangi, S. R., & Shafique, M. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102227 · Cited by 21

CAAE++: Improved CAAE for Age Progression/Regression

Zeng, J., Ma, X., & Zhou, K. (2018) · IEEE Access · doi:10.1109/access.2018.2877706 · Cited by 17

Cross-modal Hallucination for Few-shot Fine-grained RecognitionPreprint

Pahde, F., Jähnichen, P., Klein, T., & Nabi, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05147 · Cited by 17

Generative Artificial intelligence-Enhanced MultiModal Semantic Communication in Internet of Vehicles: System Design and Methodologies

Lu, J., Yang, W., Xiong, Z., Xing, C., Tafazolli, R., Quek, T. Q. S., & Debbah, M. A. (2025) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2025.3545399 · Cited by 16

Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding

Ma, J., Wang, J., Zhao, W. X., Liu, G., & Wen, X. B. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3628271 · Cited by 18

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

Fully Functional Image Manipulation Using Scene Graphs in A Bounding-Box Free Way

Su, S., Gao, L., Zhu, J., Shao, J., & Song, J. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475326 · Cited by 16

Text-Guided Sketch-to-Photo Image Synthesis

Osahor, U., & Nasrabadi, N. M. (2022) · IEEE Access · doi:10.1109/access.2022.3206771 · Cited by 17

Deep Learning Subtraction Angiography: Improved Generalizability with Transfer Learning

Crabb, B. T., Hamrick, F. A., Richards, T. J., Eiswirth, P., Noo, F., Hsiao, A., & Fine, G. (2022) · Journal of Vascular and Interventional Radiology · doi:10.1016/j.jvir.2022.12.008 · Cited by 16

Text-to-Image Synthesis based on Object-Guided Joint-Decoding Transformer

Wu, F., Liu, L., Hao, F., He, F., & Cheng, J. S. (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/cvpr52688.2022.01758 · Cited by 17

Exploring the use of AI text-to-image generation to downregulate negative emotions in an expressive writing application

Azuaje, G., Liew, K., Buening, R., She, W. J., Siriaraya, P., Wakamiya, S., & Aramaki, E. (2023) · Royal Society Open Science · doi:10.1098/rsos.220238 · Cited by 18

Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation

Pan, Z., Zhou, X., & Tian, H. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00444 · Cited by 19

VTM-GAN: video-text matcher based generative adversarial network for generating videos from textual description

Mehmood, R., Bashir, R., & Giri, K. J. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01468-4 · Cited by 16

GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation

Qin, C., Yu, N., Chen, X., Zhang, S., Chen, Z., Ermon, S., Fu, Y., Xiong, C., & Xu, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02110 · Cited by 24

Semantic Communication for Multi-modal Data Transmission

Gu, J., Zhang, X., Cui, Q., & Tao, X. (2023) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp58612.2023.10404655 · Cited by 19

Controllable 3D Face Generation with Conditional Style Code Diffusion

Shen, X., Ma, J., Zhou, C., & Yang, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28283 · Cited by 18

Iterative Multi-granular Image Editing using Diffusion Models

Joseph, K. J., Udhayanan, P., Shukla, T., Agarwal, A., Karanam, S., Goswami, K., & Srinivasan, B. V. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00792 · Cited by 25

Knowledge Graph Enhanced Multimodal Transformer for Image-Text Retrieval

Zheng, J., Liang, M., Yu, Y., Li, Y., & Xue, Z. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00013 · Cited by 19

Desigen: A Pipeline for Controllable Design Template Generation

Weng, H., Huang, D., Qiao, Y., Hu, Z., Lin, C.-Y., Zhang, T., & Chen, C. L. P. (2024) · 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/cvpr52733.2024.01209 · Cited by 19

A unified cycle-consistent neural model for text and image retrieval

Cornia, M., Baraldi, L., Tavakoli, H. R., & Cucchiara, R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09251-4 · Cited by 16

Text-Based Image Retrieval Using Deep Learning

Singhania, U., & Tripathy, B. K. (2020) · Advances in information quality and management · doi:10.4018/978-1-7998-3479-3.ch007 · Cited by 16

Cross-modal Feature Alignment based Hybrid Attentional Generative Adversarial Networks for text-to-image synthesis

Cheng, Q., & Gu, X. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102866 · Cited by 18

Clothing generation by multi-modal embedding: A compatibility matrix-regularized GAN model

Liu, L., Zhang, H., & Zhou, D. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104097 · Cited by 17

AttrLostGAN: 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

AffectGAN: Affect-Based Generative Art Driven by Semantics

Galanos, T., Liapis, A., & Yannakakis, G. N. (2021) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw52867.2021.9666317 · Cited by 16

TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation

Dinh, T. M., Nguyen, R. M. H., & Hua, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_34 · Cited by 16

A Comprehensive Pipeline for Complex Text-to-Image Synthesis

Fang, F., Luo, F., Zhang, H., Zhou, H., Chow, A. L. H., & Xiao, C. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-0305-9 · Cited by 16

Controllable and Progressive Image Extrapolation

Li, Y., Jiang, L., & Yang, M. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00219 · Cited by 16

Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor DetectionPreprint

Han, C., Rundo, L., Araki, R., Nagano, Y., Furukawa, Y., Mauri, G., Nakayama, H., & Hayashi, H. (2019) · arXiv (Cornell University) · doi:10.17863/cam.57676 · Cited by 16

InMoDeGAN: Interpretable Motion Decomposition Generative Adversarial Network for Video GenerationPreprint

Wang, Y., Brémond, F., & Dantcheva, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03049 · Cited by 16

Synthetic Data Generation for Fraud Detection using GANsPreprint

Charitou, C., Dragičević, S., & Garcez, A. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.12546 · Cited by 16

Generation of Royalty-Free Images with NLP-Based Text Encoding and Generative adversarial networks

Barbadaker, A., Khadse, M., Shaikh, S., & Kulkarni, S. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307047

AttnGAN: Realistic Text-to-Image Synthesis with Attentional Generative Adversarial Networks

Mathesul, S., Bhutkar, G., & Rambhad, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98388-8_35 · Cited by 5

Text to Image Generation Based on Adaptive Attention

Chen, Y., Bao, F., Shao, J., Yang, J., Wang, Y., & Zhao, T. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0125-7_11

Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks

Li, L., Sun, Y., Hu, F., Zhou, T., Xi, X., & Ren, J. (2020) · Discrete Dynamics in Nature and Society · doi:10.1155/2020/6452536 · Cited by 7

Text to Image Generation Using Attentional Generative Adversarial Network

Rao, S. B., Shetty, S. S., Singh, C., Srinivas, P. M., & Bekal, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-47221-3_7