A recent study has introduced a novel approach that leverages human attention to enhance the quality of images generated by artificial intelligence. This method employs a saliency detector to identify the most significant areas within an image and prioritizes the generation of these regions.
Traditional image generation methods uniformly optimize the entire image, whereas the new approach utilizes a saliency detector to identify and prioritize the more "important" areas, akin to how humans focus. This technique can enhance both image quality and the fidelity of text prompts.
Researchers utilized a stable diffusion model and a saliency detector to generate images, comparing them with traditional methods. The results indicated that the new method outperforms previous techniques in terms of image quality and the fidelity of text prompts.
Additionally, the researchers conducted human perception tests, which showed that images generated by the new method were more favored. This approach can be applied to various image generation tasks, such as text-to-image and image-to-image.
