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Related Experiment Video

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Methods to Test Visual Attention Online
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Social Image Captioning: Exploring Visual Attention and User Attention.

Leiquan Wang1, Xiaoliang Chu2, Weishan Zhang3

  • 1College of Computer & Communication Engineering, China University of Petroleum (East China), Qingdao 266555, China. richiewlq@gmail.com.

Sensors (Basel, Switzerland)
|February 23, 2018
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Summary
This summary is machine-generated.

This study introduces a dual attention model for social image captioning, incorporating user tags to improve descriptions. The novel approach enhances image captioning by considering both visual and user attention.

Keywords:
social image captioninguser attentionuser-contributed tagsvisual attention

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Image captioning is a growing field, but social image captioning using user-contributed tags remains under-explored.
  • Conventional image captioning models often overlook user attention signals present in social media tags.
  • Existing methods are not directly applicable to the unique characteristics of social images.

Purpose of the Study:

  • To propose a novel dual attention model for social image captioning.
  • To integrate visual attention and user attention for more relevant image descriptions.
  • To address the limitations of existing models in handling user-contributed tags.

Main Methods:

  • Developed a dual attention model combining visual attention and user attention.
  • Visual attention focuses on salient image features.
  • User attention leverages user-contributed tags to refine image descriptions.

Main Results:

  • The proposed dual attention model significantly improves social image captioning.
  • Experiments on the Microsoft (MS) COCO dataset validate the model's effectiveness.
  • Simultaneous use of visual and user attention leads to superior captioning performance.

Conclusions:

  • The dual attention model offers a superior approach to social image captioning.
  • Incorporating user attention alongside visual attention is crucial for social images.
  • This method enhances the relevance and accuracy of generated image captions.