Open Visual Knowledge Extraction via Relation-Oriented Multimodality
  Model Prompting
Hejie Cui Xinyu Fang Zihan Zhang Ran Xu Xuan Kan Xin Liu Yue Yu Manling Li Yangqiu Song Carl Yang
Images contain rich relational knowledge that can help machines understand
the world. Existing methods on visual knowledge extraction often rely on the
pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation
types), restricting the expressiveness of the extracted knowledge. In this
work, we take a first exploration to a new paradigm of open visual knowledge
extraction. To achieve this, we present OpenVik which consists of an open
relational region detector to detect regions potentially containing relational
knowledge and a visual knowledge generator that generates format-free knowledge
by prompting the large multimodality model with the detected region of
interest. We also explore two data enhancement techniques for diversifying the
generated format-free visual knowledge. Extensive knowledge quality evaluations
highlight the correctness and uniqueness of the extracted open visual knowledge
by OpenVik. Moreover, integrating our extracted knowledge across various visual
reasoning applications shows consistent improvements, indicating the real-world
applicability of OpenVik.