Visual Genome

Visual Genome is a large-scale dataset of images paired with rich annotations, including object detection, attributes, relationships, and detailed captions, aiming to advance research in visual scene understanding. Current research focuses on improving the quality and quantity of these annotations, developing robust scene graph generation models (often employing transformer-based architectures) that handle noisy or incomplete data, and addressing challenges like long-tailed distributions and hallucination in vision-language models. This work is significant for its potential to improve various computer vision applications, such as image captioning, object detection, and visual question answering, by enabling more accurate and nuanced understanding of complex visual scenes.