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Related Experiment Video

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Thangka Image Captioning Based on Semantic Concept Prompt and Multimodal Feature Optimization.

Wenjin Hu1, Lang Qiao2, Wendong Kang2

  • 1School of Mathematics and Computer Science, Northwest Minzu Univsersity, Lanzhou 730030, China.

Journal of Imaging
|August 25, 2023
PubMed
Summary

This study introduces SCAMF-Net to improve Chinese image captioning for diverse Thangka art. The novel Semantic Concept Prompt (SCP) and Multimodal Feature Optimization (MFO) modules significantly enhance caption accuracy and richness.

Keywords:
Thangkadeep learningimage captioningknowledge distillationvisual concepts

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Digital Humanities

Background:

  • Thangka images possess rich diversity, posing challenges for automated captioning.
  • Existing deep learning models generate inaccurate and simplistic Chinese captions for Thangka art.

Purpose of the Study:

  • To develop an advanced image captioning network for Thangka images.
  • To enhance the accuracy and semantic richness of Chinese captions for Thangka art.

Main Methods:

  • Proposed the Semantic Concept Prompt and Multimodal Feature Optimization network (SCAMF-Net).
  • Introduced a Semantic Concept Prompt (SCP) module for richer text encoding.
  • Developed a Multimodal Feature Optimization (MFO) module to improve image-text feature correlation.

Main Results:

  • SCAMF-Net significantly outperformed baseline models on the Thangka dataset across multiple metrics (BLEU-4, METEOR, ROUGE, CIDEr, SPICE).
  • Achieved substantial improvements, particularly a 76.6% increase in CIDEr score.
  • Demonstrated superior performance compared to state-of-the-art methods on the MSCOCO dataset.

Conclusions:

  • SCAMF-Net effectively addresses the limitations of current methods for Thangka image captioning.
  • The proposed SCP and MFO modules enhance semantic understanding and visual-text correlation.
  • This approach offers a promising solution for generating high-quality, descriptive Chinese captions for culturally rich imagery.