Synthesis of a realistic image from matching visual descriptions provided in the textual format is a challenge that has attracted attention in the recent research community in the field of artificial intelligence. Generation of the image from given text input is a problem, where given a text input, an image which matches text description must be generated. However, a relatively new class of convolutional neural networks referred to as generative adversarial networks (GANs) has provided compelling results in understanding textual features and generating high-resolution images. In this work, the main aim is to generate an automobile image from the given text input using generative adversarial networks and manipulate automobile colour using text-adaptive discriminator. This work involves creating a detailed text description of each image of a car to train the GAN model to produce images.
CITATION STYLE
Sindhu, N., & Mamatha, H. R. (2021). Generating automobile images dynamically from text description. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 53, pp. 197–211). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-5258-8_21
Mendeley helps you to discover research relevant for your work.