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Related Experiment Video

Updated: Oct 10, 2025

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Attention-Guided Image Captioning through Word Information.

Ziwei Tang1, Yaohua Yi1, Hao Sheng1

  • 1School of Printing and Packaging, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel word guided attention (WGA) method to improve image captioning. WGA enhances detail and accuracy in generated image descriptions, achieving competitive results on standard benchmarks.

Keywords:
current word guidanceimage captioningprevious word guidanceword level attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Current image captioning models often fail to capture all objects or provide realistic details.
  • Existing attention mechanisms in image captioning have limitations in covering all relevant image regions.

Purpose of the Study:

  • To propose a novel Word Guided Attention (WGA) method for enhancing image captioning.
  • To improve the detail and accuracy of generated image descriptions by focusing on word-image relationships.

Main Methods:

  • WGA extracts word information using embedded words and memory cells via transformation and multiplication.
  • Word information is applied to attention results through elementwise multiplication to obtain attended feature vectors.
  • WGA is integrated into the decoder at different time steps to generate previous word attention (PW) and current word attention (CW).

Main Results:

  • The proposed WGA method achieved competitive performance on the MSCOCO dataset.
  • PW results showed a Bilingual Evaluation Understudy score (BLEU-4) of 39.1 and a Consensus-Based Image Description Evaluation score (CIDEr-D) of 127.6.
  • CW results demonstrated a BLEU-4 score of 39.1 and a CIDEr-D score of 127.2 on the Karpathy test split.

Conclusions:

  • The Word Guided Attention (WGA) method significantly improves image captioning performance.
  • WGA enhances the descriptive quality and factual accuracy of generated image captions.
  • The method shows strong potential for advancing the field of image captioning research.