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A Survey on Learning Objects' Relationship for Image Captioning.

Du Runyan1,2,3,4, Zhang Wenkai1,2,3,4, Guo Zhi1,2,3,4

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This study reviews methods for incorporating object relationships in image captioning. Understanding these relationships improves image descriptions, enhancing computer vision and natural language processing models.

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Image captioning is a complex task involving transforming visual information into textual descriptions.
  • Incorporating relational information between image objects significantly enhances caption quality and readability.
  • Existing research focuses on relationship mining and learning for improved image captioning.

Purpose of the Study:

  • To summarize relational representation and encoding methods in image captioning.
  • To analyze the strengths and weaknesses of various relational approaches.
  • To identify common datasets and current challenges in relational image captioning.

Main Methods:

  • Review of relational representation techniques.
  • Analysis of relational encoding strategies.
  • Discussion of commonly used datasets for relational captioning.

Main Results:

  • Identification of key methods for relational representation and encoding.
  • Evaluation of the advantages and disadvantages of different approaches.
  • Compilation of relevant datasets for the task.

Conclusions:

  • Relational information is crucial for advanced image captioning.
  • Further research is needed to address current challenges in relational image captioning.
  • This review provides a comprehensive overview for researchers in the field.