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Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images.

Junno Yun1, Mehmet Akçakaya1

  • 1University of Minnesota.

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|January 13, 2026
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Summary
This summary is machine-generated.

This study introduces novel generative modeling and fusion techniques for few-shot segmentation (FSS) of infrared (IR) images. The methods enhance IR image analysis without paired RGB data, improving performance on challenging datasets.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Infrared (IR) imaging is crucial for autonomous driving, fire safety, and defense.
  • Semantic segmentation of IR images is challenging due to data scarcity, low contrast, and novel class emergence.
  • Existing few-shot segmentation (FSS) models for IR images often require paired visible RGB data, which is impractical in many applications.

Purpose of the Study:

  • To develop new strategies for few-shot segmentation (FSS) of infrared (IR) images without relying on paired visible RGB data.
  • To address challenges in IR image semantic segmentation, including data scarcity and limited contrast.
  • To improve the performance of FSS models in real-world IR imaging scenarios.

Main Methods:

  • Utilized generative modeling for synthesizing auxiliary data to enhance channel information and IR data for augmentation.
  • Developed a novel fusion ensemble module to integrate different modalities and improve the relationship between support and query sets.
  • Employed generative techniques to overcome data scarcity and limited contrast in IR images for FSS.

Main Results:

  • The proposed methods successfully perform few-shot segmentation on IR images without paired RGB data.
  • Synthesized auxiliary data improved the FSS model's ability to capture relationships between support and query sets.
  • IR data synthesis effectively addressed data scarcity, leading to improved segmentation accuracy.
  • The novel fusion ensemble module further enhanced performance by integrating multi-modal information.

Conclusions:

  • The developed generative modeling and fusion techniques offer a robust solution for few-shot segmentation of IR images.
  • These strategies effectively overcome limitations of existing FSS models, particularly the need for paired RGB data.
  • The approach shows significant improvements over state-of-the-art methods on various IR datasets, enabling broader applications of IR image analysis.