VIG: Visual Information-Guided Knowledge-Based Visual Question Answering

A better knowledge-based visual question answering (KBVQA) model needs to rely on visual features, question features, and related external knowledge to solve an open visual question answering task. Although the existing knowledge-based visual question answering works have achieved some accomplishments, there are still the following challenges: 1) There is a serious lack of visual feature information. Image information is worth a thousand words. Only relying on the converted salient text information is difficult to express the original rich information of the image. 2) The external knowledge acquired is not comprehensive enough, and there is a lack of relevant knowledge directly retrieved by visual feature information. To solve these challenges, we propose a Visual Information-Guided knowledge-based visual question answering (VIG) model. It fully considers the utilization of visual features information. Specifically: 1) We introduce multi-granularity visual information that can comprehensively characterize visual feature information. 2) We consider not only the knowledge retrieved through text information but also the knowledge directly retrieved from visual feature information. Finally, we feed the visual features and retrieved multiple text knowledge into an encoder-decoder module to generate an answer. We perform extensive experiments on the OKVQA dataset and achieve state-of-the-art performance of 60.27% accuracy.