MKEAH： Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering

External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world. Recent entity-relationship embedding approaches are deficient in representing some complex relations, resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information. To this end, we propose MKEAH: Multimodal Knowledge Extraction and Accumulation on Hyperplanes. To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information, two losses are proposed to learn the triplet representations from the complementary views: range loss and orthogonal loss. To interpret the capability of extracting topic-related knowledge, we present the Topic Similarity (TS) between topic and entity-relations. Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering. Our model outperformed state-of-the-art methods by 2.12% and 3.24% on two challenging knowledge-request datasets: OK-VQA and KRVQA, respectively. The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.