Connecting Vision and Language plays an essential role in Generative Intelligence. For this
reason, large research efforts have been devoted to image captioning, ie describing images …

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory
skills, such as visual understanding, to achieve stronger generic intelligence. In this paper …

Existing text-to-image diffusion models struggle to synthesize realistic images given dense
captions, where each text prompt provides a detailed description for a specific image region …

The automatic generation of radiology reports has the potential to assist radiologists in the
time-consuming task of report writing. Existing methods generate the full report from image …

In this study, multiple lung diseases are diagnosed with the help of the Neural Network
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …

In recent years, the growing number of medical imaging studies is placing an ever-
increasing burden on radiologists. Deep learning provides a promising solution for …

Plain text has become a prevalent interface for text-to-image synthesis. However, its limited
customization options hinder users from accurately describing desired outputs. For example …

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …

This paper presents a Generative RegIon-to-Text transformer, GRiT, for object
understanding. The spirit of GRiT is to formulate object understanding as< region, text> …