Text-to-Image Datasets: Characteristics, Challenges, and Opportunities

Text-to-image synthesis is an intriguing field of study that seeks to create visuals from textual descriptions. The primary objective of this domain is to provide visuals that align with the provided written description for both semantic coherence and visual reality. Despite significant advancements in text-to-image synthesis in recent years, it continues to encounter numerous hurdles, primarily concerning picture realism and semantic coherence. To address these challenges, selecting diverse datasets with comprehensive annotations will markedly improve model performance in addressing these difficulties. Datasets with varied visual material and comprehensive textual descriptions aid models in understanding intricate links between text and images, enhancing both semantic coherence and image authenticity. This review paper examines 20 datasets available for text-to-image synthesis, categorizing them by scope, variety, and application domains. The meticulous selection and curation of datasets are crucial for enhancing text-to-image synthesis technology. Ultimately, the careful selection and curation of datasets play a pivotal role in advancing the state-of-theart in text-to-image synthesis.