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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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RefCap: image captioning with referent objects attributes.

Seokmok Park1, Joonki Paik2,3

  • 1Department of Image, Chung-Ang University, 84 Heukseok-ro, Seoul, 06974, Republic of South Korea.

Scientific Reports
|December 7, 2023
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Summary
This summary is machine-generated.

This study introduces a novel referring expression image captioning model. It generates specific image descriptions focused on user-selected objects, enhancing visual comprehension and computer vision applications.

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Visual-linguistic multi-modality research has advanced visual comprehension.
  • Image captioning is a key task in visual-linguistic understanding.
  • Existing methods may lack specificity in generated captions.

Purpose of the Study:

  • To develop a referring expression image captioning model.
  • To enable generation of captions focused on user-specified objects.
  • To improve the relevance and specificity of image descriptions.

Main Methods:

  • The model integrates visual grounding, referring object selection, and image captioning modules.
  • User-provided object keywords are used as a prefix for caption generation.
  • Experiments were conducted on the RefCOCO and COCO captioning datasets.

Main Results:

  • The proposed model effectively generates meaningful captions.
  • Captions are aligned with users' specific interests and target objects.
  • The method demonstrates improved specificity in image description.

Conclusions:

  • The developed model successfully addresses the need for targeted image captioning.
  • Supervising with interesting objects enhances caption relevance.
  • This approach advances applications in computer vision and multi-modal AI.