Advancements and Challenges in Text-to-Image Synthesis: Exploring Deep Learning Techniques - Chen, Ge (Transactions on Computer Science and Intelligent Systems Research, 2024)
This paper presents an in-depth exploration of text-to-image synthesis, categorizing the field into three main areas based on deep learning methodologies. Each category undergoes meticulous analysis to chart its development and dissect its fundamental mechanisms. The emphasis is on the significant advancements in the field, with a special focus on major breakthroughs and the continual evolution of these technologies. Concurrently, the paper critically evaluates the challenges and limitations present in the current state of text-to-image synthesis, providing valuable insights into the obstacles hindering progress. The study also delves into potential applications, pondering the future impact of this technology across diverse industries. The implications of these advancements are examined, considering not only technological capabilities but also their wider societal and ethical consequences. This comprehensive review not only sheds light on the current landscape of text-to-image synthesis but also looks forward to future innovations, highlighting the dynamic and continuously evolving nature of this area. The fusion of deep learning with creative image generation is poised for groundbreaking applications, setting the stage for transformative shifts in the interaction and interpretation of visual and textual content.