  Knowledge-based visual question answering (KVQA) task aims to answer
questions that require additional external knowledge as well as an
understanding of images and questions. Recent studies on KVQA inject an
external knowledge in a multi-modal form, and as more knowledge is used,
irrelevant information may be added and can confuse the question answering. In
order to properly use the knowledge, this study proposes the following: 1) we
introduce a novel semantic inconsistency measure computed from caption
uncertainty and semantic similarity; 2) we suggest a new external knowledge
assimilation method based on the semantic inconsistency measure and apply it to
integrate explicit knowledge and implicit knowledge for KVQA; 3) the proposed
method is evaluated with the OK-VQA dataset and achieves the state-of-the-art
performance.

Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language;Machine Learning