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Related Experiment Video

Updated: Feb 28, 2026

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MSSA: memory-driven and simplified scaled attention for enhanced image captioning.

Mohammad Alamgir Hossain1,2, ZhongFu Ye3, Md Bipul Hossen4

  • 1School of Information Science and Technology, University of Science and Technology of China (USTC), Hefei, Anhui, China.

Scientific Reports
|February 26, 2026
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Summary
This summary is machine-generated.

This study introduces MSSA, a novel image captioning framework that enhances multimodal integration. MSSA uses advanced feature extraction and a simplified attention mechanism to generate accurate image descriptions, outperforming existing methods.

Keywords:
Image captioningMemory-driven attentionMultimodal feature extractionSimplified Scaled attention

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Image captioning bridges visual understanding and textual description.
  • Existing methods face challenges in multimodal integration and efficient caption generation.

Purpose of the Study:

  • Introduce MSSA (Memory-Driven and Simplified Scaled Attention), a novel framework for enhanced image captioning.
  • Improve multimodal integration and caption generation accuracy through advanced feature extraction and attention mechanisms.

Main Methods:

  • Leverage Extended Multimodal Feature Extraction including geometric, color, texture (LBP), edge (Canny), and frequency (Gabor) features.
  • Integrate Memory-Driven Attention (MDA) with an LSTM-based memory for feature alignment.
  • Employ Simplified Scaled Attention (SSA) for efficient context vector generation via scaled dot-product attention.

Main Results:

  • MSSA demonstrates superior performance over state-of-the-art methods on the MSCOCO dataset across multiple metrics.
  • The framework shows efficiency through streamlined feature extraction and attention mechanisms.
  • Publicly available code and resources facilitate reproducibility and further research.

Conclusions:

  • MSSA offers a robust and efficient approach to image captioning by combining comprehensive feature extraction with a simplified attention module.
  • The framework effectively enhances multimodal integration and caption generation.
  • Future work can explore further optimizations and applications of this streamlined approach.