T
Tao Xu
Researcher at Lehigh University
Publications - 31
Citations - 7891
Tao Xu is an academic researcher from Lehigh University. The author has contributed to research in topics: Convolutional neural network & Local binary patterns. The author has an hindex of 17, co-authored 28 publications receiving 5895 citations. Previous affiliations of Tao Xu include Chinese Academy of Sciences & Wuhan University.
Papers
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Proceedings ArticleDOI
StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
Han Zhang,Tao Xu,Hongsheng Li +2 more
TL;DR: This paper proposes Stacked Generative Adversarial Networks (StackGAN) to generate 256 photo-realistic images conditioned on text descriptions and introduces a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold.
Proceedings ArticleDOI
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
TL;DR: AttnGAN as mentioned in this paper proposes an attentional generative network to synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description.
Journal ArticleDOI
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Han Zhang,Tao Xu,Hongsheng Li,Shaoting Zhang,Xiaogang Wang,Xiaolei Huang,Dimitris N. Metaxas +6 more
TL;DR: Zhang et al. as discussed by the authors proposed a two-stage generative adversarial network architecture, StackGAN-v1, which sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images.
Posted Content
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Han Zhang,Tao Xu,Hongsheng Li,Shaoting Zhang,Xiaogang Wang,Xiaolei Huang,Dimitris N. Metaxas +6 more
TL;DR: StackGAN as mentioned in this paper decomposes the text-to-image generation problem into more manageable subproblems through a sketch-refinement process, and introduces a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold.
Posted Content
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
TL;DR: AttnGAN as mentioned in this paper proposes an attentional generative network to synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description.