Text to image synthesis, one of the most fascinating applications of GANs, is one of the hottest topics in all of machine learning and artificial intelligence. This paper comprises techniques for training a GAN to synthesise human faces and images of flowers from text descriptions. In this paper...
本文提出了细粒度的图像生成,通过借助文本描述生成包含充分细节的图像,利用attention-driven、multi-stage refinement、GAN三种方法来生成理想的图片,建立了文本描述到图片细节的attention。构建DAMSM使text-encoder与image-encoder生成的特征能够在公共空间中对齐,表示相似性,也就是所利用多模态相似性为目标函数来协同优化特征...
以及同期的一篇工作:An image is worth one word: Personalizing text-to- image generation using textual inversion,该方法提出了一种通过在冻结的文本到图像模型的嵌入空间中使用新的标记来表示视觉概念,如对象或风格,从而得到小型的个性化标记嵌入。然而这种方法受限于冻结扩散模型的表达能力。 三、方法 3.1 Designing...
We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS. - ayansengupta17/GAN
简介:Semantic-Spatial Aware GAN提出了一种新的语义空间感知GAN框架,文章发表于2021年10月。论文地址:https://arxiv.org/pdf/2104.00567v3.pdf代码地址:https://github.com/wtliao/text2image本博客是精读这篇论文的报告,包含一些个人理解、知识拓展和总结。
embed_size: Size of embeddings, default = dataset.embed_dim (if using CGAN or WGAN) Plotting the generated images Run plot_gan_losses(disc_loss, genr_loss) in trainer_GAN.py . References [1] Generative Adversarial Text-to-Image Synthesis https://arxiv.org/abs/1605.05396 [2] Text-to-Ima...
Therefore, in this paper, we propose to design a multi-stage generation model, and we address this problem by developing a novel generation model called Text-representation Generative Adversarial Network (TRGAN). TRGAN contains two modules: Joint attention stacked generation module (JASGM) and ...
代码地址:https://github.com/senmaoy/Recurrent-Affine-Transformation-for-Text-to-image-Synthesis 精读与理解:RAT-GAN:文本到图像合成中的递归仿射变换 Recurrent Affine Transformation for Text-to-image Synthesis 2、SSA-GAN:Text to Image Generation with Semantic-Spatial Aware GAN ...
代码:https://github.com/netanelyo/Recipe2ImageGAN. 13.Image-to-Image Translation with Text Guidance 文本控制image-to-image,数据集:COCO。 14.MirrorGAN: Learning Text-to-image Generation by Redescription 介绍了MirrorGAN:text-to-image-to-text framework,思想有点类似于CycleGAN。
现代文本到图像(text-to-image,T2I)生成模型,例如 DALL-E [7, 8]、Imagen [9, 10]、Stable Diffusion [5]、StyleGAN-T [4] 和 GigaGAN [11],展示了根据文本描述合成逼真、艺术和详细图像的卓越能力。 这些进步是通过大规模数据集 [12] 和模型 [5,7,11] 的帮助实现的。 然而,尽管它们的生成质量令...