期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：12
DOI：10.14569/IJACSA.2021.01212115
语种：English
出版社：Science and Information Society (SAI)
摘要：Synthesizing new images from textual descriptions requires understanding the context of the text. It is a very chal-lenging problem in Natural Language Processing and Computer vision. Existing systems use Generative Adversarial Network (GAN) to generate images using a simple text encoder from their captions. This paper consist synthesizing images from textual descriptions using Caltech-UCSD birds datasets by baselining the generative model using Attentional Generative Adversarial Networks (AttnGAN) and using RoBERTa pre-trained neural language model for word embeddings. The results obtained are compared with the baseline AttnGAN model and conduct various analyses on incorporating RoBERTa text encoder concerning simple encoder in the existing system. Various performance improvements were noted compared to baseline Attention Gen-erative networks. The FID score has decreased from 23.98 in AttnGAN to 20.77 with incorporation of RoBERTa model with AttnGAN.
关键词：Natural language processing; computer vision; GANs; AttnGAN; RoBERTa
