Semantic BI-Embedded GRU for Fill-in-the-Blank Image Question Answering

Abstract

Image question answering is becoming a very attractive topic in the field of computer vision and natural language processing in recent years. In this work, we propose a novel Semantic Bi-Embedded Gated Recurrent Unit (SBE-GRU) method to answer fill-in-the-blank style multiple choice questions for images. Different from the single network, we use the SBE-GRU model to learn the high-level semantic information existing in images. To learn the semantic mapping of an image from the visual level to the language level, we feed the visual-language mappings into a stacked GRU. Moreover, to choose the right answer in the candidate options more simply and effectively, we regard the answer sentence as an answer list while training. In the extensive experiments, the proposed method can get better results compared with the state-of-the-art and CCA methods.