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Rethinking symbolic and visual context in Referring Expression Generation.

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Situational context is vital for referring expression generation (REG), influencing how objects are described. This review categorizes context

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Area of Science:

  • Computational Linguistics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Referring Expression Generation (REG) traditionally uses symbolic object properties.
  • Modern visual REG integrates multimodal data, treating it as a complex, vision-language problem.
  • Defining and categorizing context's influence remains a challenge, especially in complex visual settings.

Purpose of the Study:

  • To systematically review the types and functions of visual context in REG.
  • To propose integrating and extending diverse perspectives on visual context in REG research.
  • To analyze how symbolic REG utilizes context, including positive and negative semantic forces.

Main Methods:

  • Systematic literature review of symbolic and visual REG approaches.
  • Analysis of contextual integration categories derived from rule-based symbolic REG.
  • Framework application to evaluate visual REG's consideration of contextual influences.

Main Results:

  • Identified categories of contextual integration, including positive and negative semantic forces.
  • Found that current visual REG research has explored only a subset of contextual influences.
  • Highlighted gaps in understanding and utilizing visual context for end-to-end generation.

Conclusions:

  • Visual context significantly impacts referring expression generation.
  • There's a need to integrate diverse contextual perspectives for more robust visual REG.
  • Future research should explore additional contextual integration methods for REG and multimodal generation.