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Updated: Sep 17, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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A multimodal transformer-based visual question answering method integrating local and global information.

Cuiyang Huang1, Zihan Hu1

  • 1Jinan University-University of Birmingham Joint Institute, Guangzhou, RP China.

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Summary

This study introduces LGMTNet, a novel multimodal Transformer visual question answering (VQA) network. LGMTNet enhances multimodal feature fusion by integrating local and global image information for improved VQA performance.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Current visual question answering (VQA) models struggle with effective multimodal feature fusion.
  • Existing VQA approaches often neglect the importance of local image details.

Purpose of the Study:

  • To propose a novel multimodal Transformer VQA network, LGMTNet, that integrates local and global information.
  • To enhance multimodal feature fusion and improve the comprehension of visual-language data.

Main Methods:

  • Developed a multimodal Transformer VQA network (LGMTNet) incorporating local and global information integration.
  • Employed attention mechanisms to fuse local image features within global contexts.
  • Designed a deep encoder-decoder module for context-driven image feature attention.
  • Introduced a multimodal representation module to focus on key question terms and reduce linguistic noise.
  • Utilized a feature aggregation module to combine multimodal and question features for deeper comprehension.

Main Results:

  • LGMTNet demonstrated effective focus on local image features.
  • The model successfully integrated multimodal knowledge.
  • Enhanced feature fusion capabilities were observed in experimental results.

Conclusions:

  • LGMTNet addresses limitations in current VQA models by improving multimodal feature fusion.
  • The integration of local and global information enhances the model's ability to understand complex visual-language queries.
  • LGMTNet shows significant potential for advancing the field of visual question answering.