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Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting.

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OpenVik introduces open visual knowledge extraction, generating format-free insights from images. This approach enhances machine understanding beyond pre-defined structures, improving visual reasoning applications.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Images contain rich relational knowledge crucial for machine understanding.
  • Current visual knowledge extraction methods are limited by pre-defined formats and vocabularies, restricting expressiveness.
  • A new paradigm for open visual knowledge extraction is needed.

Purpose of the Study:

  • To explore a novel paradigm for open visual knowledge extraction.
  • To develop a system capable of extracting format-free knowledge from images.
  • To enhance the expressiveness and applicability of visual knowledge.

Main Methods:

  • Introducing OpenVik, a system comprising an open relational region detector and a visual knowledge generator.
  • Utilizing a large multimodality model prompted with detected regions of interest for knowledge generation.
  • Exploring data enhancement techniques to diversify the generated knowledge.

Main Results:

  • OpenVik successfully extracts open visual knowledge with high correctness and uniqueness.
  • The system generates format-free knowledge, overcoming limitations of traditional methods.
  • Data enhancement techniques contribute to diversifying the extracted knowledge.

Conclusions:

  • OpenVik represents a significant advancement in open visual knowledge extraction.
  • The extracted knowledge demonstrates real-world applicability through consistent improvements in visual reasoning tasks.
  • This work opens new avenues for machines to understand complex visual relationships.