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MiLNet: Multiplex Interactive Learning Network for RGB-T Semantic Segmentation.

Jinfu Liu, Hong Liu, Xia Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 3, 2025
    PubMed
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    This study introduces MiLNet, a novel network for RGB-Thermal semantic segmentation, improving scene understanding under challenging lighting. MiLNet excels by integrating diverse feature learning, outperforming existing methods.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Robust semantic segmentation is crucial for scene understanding, especially under adverse illumination.
    • Current RGB-Thermal (RGB-T) methods often prioritize feature fusion over effective feature learning.
    • Existing approaches may not fully leverage the complementary information from visible and thermal infrared images.

    Purpose of the Study:

    • To propose a novel module-free network, MiLNet, for enhanced RGB-T semantic segmentation.
    • To improve feature learning by integrating multi-model, multi-modal, and multi-level strategies.
    • To address limitations in current methods by focusing on multiplex feature interaction.

    Main Methods:

    • Developed a module-free Multiplex Interactive Learning Network (MiLNet) for RGB-T semantic segmentation.

    Related Experiment Videos

  • Integrated knowledge transfer from vision foundation models into a task-specific model.
  • Employed an asymmetric simulated learning strategy for cross-modal geometric and semantic information exchange.
  • Utilized an inverse hierarchical fusion strategy with multi-label and multi-scale supervision.

    • Main Results:

    • MiLNet demonstrated superior performance compared to state-of-the-art methods on the MFNet and PST900 datasets.
    • Achieved higher mean Intersection over Union (mIoU) scores, indicating improved segmentation accuracy.
    • Successfully integrated multi-modal, multi-level, and multi-model feature learning.

    Conclusions:

    • MiLNet offers a significant advancement in RGB-T semantic segmentation, particularly under adverse conditions.
    • The proposed feature learning strategies effectively exploit complementary RGB-T information.
    • Further research is needed to enhance performance in few-sample segmentation scenarios.