We input a sentence and generate multiple images fitting the description. In addition, there are categories having large variations within the category and several very similar categories. Ibn: Proceedings of the ACM International Conference on Multimedia – MM 2014 (2014), Zitnick, C., Parikh, D., Vanderwende, L.: Learning the visual interpretation of sentences. Zhang, Han, et al. In: Proceedings of the International Conference on Multimedia – MM 2010 (2010), Inaba, S. Kanezaki, A., Harada, T.: Automatic image synthesis from keywords using scene context. This is a pytorch implementation of Generative Adversarial Text-to-Image Synthesis paper, we train a conditional generative adversarial network, conditioned on text … ”Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks.” arXiv preprint (2017). Conditional GAN is an extension of GAN where both generator and discriminator receive additional conditioning variables c, yielding G(z, c) and D(x, c). Though AI is catching up on quite a few domains, text to image synthesis probably still needs a few more years of extensive work to be able to get productionalized. Conditional generative adversarial networks (cGANs), image synthesis, image-to-image translation, text-to-image synthesis, 3D GANs. ICVGIP’08. Samples generated by existing textto- image … As we can see, the flower images that are produced (16 images in each picture) correspond to the text description accurately. Abstract: Text-to-image synthesis is to generate images with the consistent content as the given text description, which is a highly challenging task with two main issues: visual reality and content … To achieve this goal, real-world images representing nouns are obtained from ImageNet and their foreground objects of interest are extracted using image segmentation. To demonstrate the effectiveness of the proposed approach, we have developed a mobile application that uses the RESTful API to retrieve the images from the web service that operate the image generation program. ACM Trans. Fortunately, deep learning has enabled enormous progress in both subproblems - natural language representation and image synthesis … Recently, text-to-image synthesis has achieved great progresses with the advancement of the Generative Adversarial Network (GAN). The paper talks about training a deep convolutional generative adversarial net- work (DC-GAN) conditioned on text features. 35 ›› Issue (3): 522-537. doi: 10.1007/s11390-020-0305-9 • Special Section of CVM 2020 • Previous Articles Next Articles A Comprehensive Pipeline for Complex Text-to-Image Synthesis … Text to Image Synthesis refers to the process of automatic generation of a photo-realistic image starting from a given text and is revolutionizing many real-world applications. ”Stackgan++: Realistic image synthesis with stacked generative adversarial networks.” arXiv preprint arXiv:1710.10916 (2017). Graph. ”Generative adversarial text to image synthesis.” arXiv preprint arXiv:1605.05396 (2016). The encoded text description em- bedding is first compressed using a fully-connected layer to a small dimension followed by a leaky-ReLU and then concatenated to the noise vector z sampled in the Generator G. The following steps are same as in a generator network in vanilla GAN; feed-forward through the deconvolutional network, generate a synthetic image conditioned on text query and noise sample. Generating images from natural language is one of the primary applications of recent conditional generative models. 7| Text To Image Synthesis . Text-to-Image Synthesis refers to the process of automatic generation of a photo-realistic image starting from a given text and is revolutionizing many real-world applications. StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks ICCV 2017 • hanzhanggit/StackGAN • Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Rother, C., Kolmogorov, V., Blake, A.: GrabCut. The key contributions … Our results are presented on the Oxford-102 dataset of flower images having 8,189 images of flowers from 102 different categories. For example, in Figure 8, in the third image description, it is mentioned that ‘petals are curved upward’. INTRODUCTION The task of image synthesis is central in many fields … We would like to mention here that the results which we have obtained for the given problem statement were on a very basic configuration of resources. In recent years, powerful neural network architectures like GANs (Generative Adversarial Networks) have been found to generate good results. Stage-II GAN: The defects in the low-resolution image from Stage-I are corrected and details of the object by reading the text description again are given a finishing touch, producing a high-resolution photo-realistic image. Reed, Scott, et al. The complete directory of the generated snapshots can be viewed in the following link: SNAPSHOTS. Text-to-Image-Synthesis Intoduction. 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