Multi-Level Knowledge Injecting for Visual Commonsense Reasoning

• Published in: IEEE transactions on circuits and systems for video technology Vol. 31; no. 3; pp. 1042 - 1054
• Main Authors: Wen, Zhang, Peng, Yuxin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cognition; Image recognition; Information transfer; Knowledge; Knowledge based systems; Knowledge discovery; Knowledge management; knowledge representation; Object recognition; Reasoning; Task analysis; transfer learning; Visual commonsense reasoning; visual question answering; Visual tasks; Visualization
• DOI: 10.1109/TCSVT.2020.2991866
• ISSN: 1051-8215

When glancing at an image, human can infer what is hidden in the image beyond what is visually obvious, such as objects' functions, people's intents and mental states. However, such a visual reasoning paradigm is tremendously difficult for computer, requiring knowledge about how the world works. To address this issue, we propose Commonsense Knowledge based Reasoning Model (CKRM) to acquire external knowledge to support Visual Commonsense Reasoning (VCR) task, where the computer is expected to answer challenging visual questions. Our key ideas are: (1) To bridge the gap between recognition-level and cognition-level image understanding, we inject external commonsense knowledge via multi-level knowledge transfer network , achieving cell-level, layer-level and attention-level joint information transfer. It can effectively capture knowledge from different perspectives, and perceive common sense of human in advance. (2) To further promote image understanding at cognitive level, we propose a knowledge based reasoning approach , which can relate the transferred knowledge to visual content and compose the reasoning cues to derive the final answer. Experiments are conducted on the challenging visual commonsense reasoning dataset VCR to verify the effectiveness of our proposed CKRM approach, which can significantly improve reasoning performance and achieve the state-of-the-art accuracy.