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Transformers01:26

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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Related Experiment Video

Updated: May 21, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Multi-level semantic-aware transformer for image captioning.

Qin Xu1, Shan Song2, Qihang Wu2

  • 1Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, 230601, China; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, 230601, China; School of Computer Science and Technology, Anhui University, Hefei, 230601, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Multi-Level Semantic-Aware Transformer (MLSAT) for image captioning. MLSAT enhances visual representation by focusing on salient objects, significantly improving captioning performance over existing methods.

Keywords:
Attention mechanismImage captioningRelative spatial relationshipsTransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Effective visual representation is key for image captioning.
  • Current grid-based methods often miss fine-grained semantic details from salient objects.

Purpose of the Study:

  • To propose a novel image captioning method, the Multi-Level Semantic-Aware Transformer (MLSAT).
  • To improve visual representation by integrating contextual details and high-level semantic information focused on salient objects.

Main Methods:

  • Developed Visual Content Guided Attention (VCGA) to model spatial correlations and semantic interactions.
  • Introduced the Multi-Level Semantic-Aware (MLSA) module for fine-grained semantic information extraction and integration.
  • Utilized Semantic Information Extractor (SIE), Semantic Refiner (SR), and Visual-Semantic Fusion Block (V-SFB) within the MLSA module.

Main Results:

  • The proposed MLSAT method significantly outperforms state-of-the-art models on the MS-COCO dataset.
  • Achieved a CIDEr (c40) score of 135.1% on the official online testing server.
  • Demonstrated superior performance in capturing salient object information for enhanced image captioning.

Conclusions:

  • MLSAT effectively addresses the limitations of fragmented visual features in existing methods.
  • The integration of multi-level semantic information leads to more accurate and contextually relevant image captions.
  • The proposed approach represents a significant advancement in the field of image captioning.