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An Overview of Image Caption Generation Methods.

Haoran Wang1, Yue Zhang1, Xiaosheng Yu2

  • 1College of Information Science and Engineering, Northeastern University, China.

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Artificial intelligence enables image captioning, transforming images into text descriptions. This review explores attention mechanisms in AI for better image understanding and human-computer interaction.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Image captioning is a key AI challenge, bridging computer vision and natural language processing.
  • It involves generating textual descriptions for image content, crucial for scene understanding.

Purpose of the Study:

  • To review current image captioning methods.
  • To focus on the role and impact of attention mechanisms in image caption generation.
  • To discuss datasets, evaluation metrics, and future challenges.

Main Methods:

  • Literature review of image captioning techniques.
  • Analysis of attention-based models in computer vision.
  • Discussion of common datasets and evaluation criteria.

Main Results:

  • Attention mechanisms significantly enhance image captioning performance.
  • Various methods have been developed, each with specific advantages and limitations.
  • Common datasets and evaluation metrics are identified.

Conclusions:

  • Image captioning is a rapidly advancing field with significant applications.
  • Attention mechanisms are pivotal for improving AI's ability to describe images.
  • Open challenges remain in achieving more sophisticated and context-aware image descriptions.