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Infrared Image Caption Based on Object-Oriented Attention.

Junfeng Lv1, Tian Hui1, Yongfeng Zhi1

  • 1Institute of Unmanned System Research, Northwestern Polytechnical University, Xi'an 710072, China.

Entropy (Basel, Switzerland)
|May 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an object-oriented attention method using YOLOv6 and LSTM for infrared image captioning. The approach enhances object-description correlation, offering a feasible solution for embedded devices in industrial applications.

Keywords:
adaptive weighting moduledomain transfer object detectioninfrared image captionobject oriented attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Intelligent applications on embedded devices are growing.
  • Automatic image captioning for infrared images is challenging due to feature differences and semantic complexity.
  • Applications include night security and scene understanding.

Purpose of the Study:

  • To improve the correlation between descriptions and objects in infrared images.
  • To develop a feasible solution for infrared image captioning on embedded devices.

Main Methods:

  • Utilized YOLOv6 and Long Short-Term Memory (LSTM) as an encoder-decoder structure.
  • Proposed an object-oriented attention mechanism to align semantic information with generated words.
  • Optimized the pseudo-label learning process for improved domain adaptability of the object detector.

Main Results:

  • Achieved BLUE-4 scores of 31.6 on KAIST and 41.2 on Infrared City and Town datasets.
  • Demonstrated good performance in generating words explicitly associated with detected object regions.
  • Showcased robustness and effectiveness compared to state-of-the-art methods.

Conclusions:

  • The proposed object-oriented attention method effectively addresses challenges in infrared image captioning.
  • The approach provides a practical solution for deploying intelligent applications on embedded devices.
  • This work contributes to advancements in automated understanding of infrared imagery.