Advancements and Challenges in Text-to-Image Synthesis: Exploring Deep Learning Techniques

Ge Chen

doi:10.62051/fmg44921

Authors

Ge Chen

DOI:

https://doi.org/10.62051/fmg44921

Keywords:

Image generation; deep learning; GAN; diffusion; autoregressive.

Abstract

This paper presents an in-depth exploration of text-to-image synthesis, categorizing the field into three main areas based on deep learning methodologies. Each category undergoes meticulous analysis to chart its development and dissect its fundamental mechanisms. The emphasis is on the significant advancements in the field, with a special focus on major breakthroughs and the continual evolution of these technologies. Concurrently, the paper critically evaluates the challenges and limitations present in the current state of text-to-image synthesis, providing valuable insights into the obstacles hindering progress. The study also delves into potential applications, pondering the future impact of this technology across diverse industries. The implications of these advancements are examined, considering not only technological capabilities but also their wider societal and ethical consequences. This comprehensive review not only sheds light on the current landscape of text-to-image synthesis but also looks forward to future innovations, highlighting the dynamic and continuously evolving nature of this area. The fusion of deep learning with creative image generation is poised for groundbreaking applications, setting the stage for transformative shifts in the interaction and interpretation of visual and textual content.

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References

Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784.

Xia, W., Yang, Y., Xue, J. H., & Wu, B. (2021). Tedigan: Text-guided diverse face image generation and manipulation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2256-2265). DOI: https://doi.org/10.1109/CVPR46437.2021.00229

Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B... & Chen, M. (2021). Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741.

Huang, N., Zhang, Y., Tang, F., Ma, C., Huang, H., Dong, W., & Xu, C. (2024). Diffstyler: Controllable dual diffusion for text-driven image stylization. IEEE Transactions on Neural Networks and Learning Systems. DOI: https://doi.org/10.1109/TNNLS.2023.3342645

Zhu, X., Zhao, Z., Wei, X., & others. (2021). Action recognition method based on wavelet transform and neural network in wireless network. In 2021 5th International Conference on Digital Signal Processing (pp. 60-65). DOI: https://doi.org/10.1145/3458380.3458391

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-24574-4_28

Zhang, Q., Song, J., Huang, X., Chen, Y., & Liu, M. Y. (2023, June). Diffcollage: Parallel generation of large content with diffusion models. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 10188-10198). IEEE. DOI: https://doi.org/10.1109/CVPR52729.2023.00982

Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z.... & Wu, Y. (2022). Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2(3), 5.

Yu, J., Li, X., Koh, J. Y., Zhang, H., Pang, R., Qin, J.... & Wu, Y. (2021). Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627.

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851.