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Mining core information by evaluating semantic importance for unpaired image captioning.

Jiahui Wei1, Zhixin Li1, Canlong Zhang1

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Neural Networks : the Official Journal of the International Neural Network Society
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Summary

This study introduces Mining Core Information by Evaluating Semantic Importance (MCIESI) for unpaired image captioning. MCIESI effectively mines and generates core image information into human-like sentences, overcoming data limitations.

Keywords:
Generative adversarial trainingMining core informationTransformerUnpaired image captioning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Supervised image captioning requires extensive manual annotation, which is costly and time-consuming.
  • The challenge of obtaining paired image-annotation data necessitates methods for unpaired image captioning.

Purpose of the Study:

  • To develop a novel method for unpaired image captioning that generates semantically relevant and grammatically correct captions.
  • To address the limitations of existing methods by focusing on mining and embodying core image information.

Main Methods:

  • Utilized scene graphs to represent image semantics and evaluate object/interaction importance for mining core information.
  • Employed semantic constraints to guide sentence generation based on mined image information.
  • Incorporated grammatical constraints via adversarial training and relative constraints using triplet loss.

Main Results:

  • The proposed Mining Core Information by Evaluating Semantic Importance (MCIESI) method demonstrates effectiveness in unpaired image captioning.
  • Generated captions are semantically plausible and grammatically correct, aligning with human cognitive processes.

Conclusions:

  • MCIESI successfully mines essential image content and translates it into coherent, human-like descriptions without paired data.
  • The method offers a promising solution for generating high-quality image captions in data-scarce scenarios.