Conditional Text-to-Image Synthesis

MIMIC-CXR from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA. Source: Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification Image Source: https://arxiv.org/abs/1901.07042

The COCO-MIG benchmark (Common Objects in Context Multi-Instance Generation) is a benchmark used to evaluate the generation capability of generators on text containing multiple attributes of multi-instance objects. This benchmark consists of 800 sets of examples sampled from the COCO dataset. Following the layout of the COCO dataset, each instance is assigned random color information, and corresponding global image descriptions are constructed according to templates. The COCO-MIG also provides a complete pipeline for resampling and evaluating. For relevant tools and specific details, please refer to our project's homepage.