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- W4320802937 abstract "In this paper, we propose a simple and effective text-to-image generative adversarial network named Hierarchical Fusion via GAN with Capsule Network discriminator (HFGAN-CN) , which uses only one generator and one discriminator to generate high-resolution images directly from text. We introduce a text-image feature fusion module via spatial and channel attention mechanism into the generator, which performs hierarchical fusion on the features of the two modes in the image generation process, so as to guide the generator to generate high-quality images in line with text semantics. We propose a match-aware discriminator to synthesize text-semantic compliant images and employ a capsule network to reconfigure the network structure of the discriminator without introducing an additional network. Experiments on the benchmark dataset show that our method is superior to the existing techniques and can effectively generate high-resolution images conforming to the text description." @default.
- W4320802937 created "2023-02-15" @default.
- W4320802937 creator A5066586426 @default.
- W4320802937 creator A5072923571 @default.
- W4320802937 date "2022-08-15" @default.
- W4320802937 modified "2023-09-27" @default.
- W4320802937 title "HFGAN-CN: T2I Model via Text-Image Hierarchical Attention Fusion" @default.
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- W4320802937 doi "https://doi.org/10.1109/ccdc55256.2022.10033800" @default.
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