“A Fast Text-driven Approach for Generating Artistic Content” by Lupașcu, Murdock, Mironică and Li
Conference:
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Entry Number: 12
Title:
- A Fast Text-driven Approach for Generating Artistic Content
Presenter(s)/Author(s):
References:
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019. Large Scale GAN Training for High Fidelity Natural Image Synthesis. ArXiv abs/1809.11096(2019).Google Scholar
Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. NeurIPS (2021).Google Scholar
Patrick Esser, Robin Rombach, and Bjorn Ommer. 2021. Taming Transformers for High-Resolution Image Synthesis. In CVPR.Google Scholar
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski. 2021. StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery. In ICCV.Google Scholar
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. In ICML.Google Scholar
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-Shot Text-to-Image Gen.. In ICML.Google Scholar
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. 2016. Generative Adversarial Text to Image Synthesis. In ICML.Google Scholar
Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He. 2018. AttnGAN: Fine-Grained Text to Image Generation With Attentional Generative Adversarial Networks. In CVPR.Google Scholar
