Diverse Image

Diverse image research focuses on generating and analyzing images that represent a wide range of visual styles, cultural contexts, and object compositions, aiming to mitigate biases and improve the robustness and fairness of computer vision systems. Current research employs various generative models, including diffusion models and normalizing flows, often coupled with techniques like fine-tuning, prompt engineering, and loss function modifications to enhance image diversity and control. This work is crucial for addressing biases in existing datasets and models, leading to more equitable and reliable applications across diverse cultural and socioeconomic contexts, and improving the generalizability of computer vision algorithms.