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Multi-Modal Graph Aggregation Transformer for image captioning.

Lizhi Chen1, Kesen Li2

  • 1School of Computer Science and Technology, Soochow University, Suzhou 215000, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the Multi-Modal Graph Aggregation Transformer (MMGAT) for improved image captioning. The MMGAT model effectively integrates multi-modal information, significantly enhancing descriptive accuracy and contextual understanding in generated captions.

Keywords:
Graph AggregationImage captioningMulti-ModalTransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Current image captioning models struggle with contextual information and object relationships.
  • Reliance on regional features alone limits the semantic understanding of images.

Purpose of the Study:

  • To develop a novel model that effectively integrates multi-modal information for enhanced image captioning.
  • To address the limitations of existing methods in capturing contextual and semantic details.

Main Methods:

  • Introducing the Multi-Modal Graph Aggregation Transformer (MMGAT).
  • Representing images as a graph with three sub-graphs: context grid, region, and semantic text.
  • Utilizing aggregators for guided message passing between sub-graphs to refine node features.

Main Results:

  • Achieved state-of-the-art performance with 144.6% CIDEr on MS-COCO and 80.3% CIDEr on Flickr30k.
  • Demonstrated significant improvements in image captioning accuracy and contextual understanding.
  • Rigorous analysis confirmed the effectiveness of each component of the MMGAT design.

Conclusions:

  • The MMGAT model successfully leverages multi-modal graph aggregation for superior image captioning.
  • The proposed approach enhances the model's ability to understand and describe complex image content.
  • MMGAT represents a significant advancement in the field of automated image description.