A Survey on Knowledge-Driven Multimodal Semantic Understanding
ZHENG Yihao1, GUO Yijun2, WU Lifang1, HUANG Yan3
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124; 2. Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing 100191; 3. State Key Laboratory for Multi-modal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100191
Abstract：Multimodal learning methods based on deep learning model achieve excellent semantic understanding performance in static, controllable and simple scenarios. However, their generalization ability in dynamic, open and other complex scenarios is still unsatisfactory. Human-like knowledge is introduced into multimodal semantic understanding methods in recent research, yielding impressive results. To gain deeper understanding of the current research progress in knowledge-driven multimodal semantic understanding, two main types of multimodal knowledge representation frameworks are summarized based on systematic investigation and analysis of relevant methods in this paper. The two main types of multimodal knowledge representation frameworks are relational and aligned, respectively. Several representative applications are discussed, including image-text matching, object detection, semantic segmentation, and vision-and-language navigation. In addition, the advantages and disadvan-tages of the current methods and the possible development trend in the future are concluded.
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2023, Vol. 36 
