Text to Image Generative Model

Text-to-image generative models synthesize realistic images from textual descriptions, aiming to bridge the gap between human language and visual representation. Current research heavily focuses on addressing biases inherent in these models (often stemming from training data), improving compositional accuracy and mitigating vulnerabilities to adversarial attacks, including poisoning and backdoor attacks. These models are rapidly impacting various fields, from art and design to scientific visualization, but their ethical implications and potential for misuse necessitate ongoing investigation into robustness, fairness, and accountability.