VTQA (Visual Text Question Answering)

VTQA is a dataset containing open-ended questions about image-text pairs. This dataset requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. The aim of this dataset is to develop and benchmark models that are capable of multimedia entity alignment, multi-step reasoning and open-ended answer generation. VTQA dataset consists of 10,238 image-text pairs and 27,317 questions. The images are real images from MSCOCO dataset, containing a variety of entities. The annotators are required to first annotate relevant text according to the image, and then ask questions based on the image-text pair, and finally answer the question open-ended.