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CEKD: Cross-Modal Edge-Privileged Knowledge Distillation for Semantic Scene Understanding Using Only Thermal Images

22 Feb 2023IEEE ROBOTICS AND AUTOMATION LETTERS 2023 2archive 2025-07-28

Zhen Feng; Yanning Guo; Yuxiang Sun

Semantic scene understanding using thermal images has received great attention due to the advantage that thermal imaging cameras could see in challenging illumination conditions. However, thermal images are lack of color information and the edges in thermal images are often blurred, making them not very suitable to be directly used by existing semantic segmentation networks that are designed with RGB images. To address this problem, we propose a cross-modal edge-privileged knowledge distillation framework, which utilizes a well-trained RGB-Thermal fusion-based semantic segmentation network with edge-privileged information as the teacher, to guide the training of a semantic segmentation network as the student. The student network only uses thermal images. The experimental results on a public dataset demonstrate that under the guidance of the teacher, the student network achieves superior performance over the state of the arts using only thermal images.

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Code

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Tasks

Knowledge DistillationScene UnderstandingSegmentationSemantic SegmentationThermal Image Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Thermal Image Segmentation MFN Dataset CENet mIOU 56.1 #28 of 55 Archive leaderboard report

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Methods

Knowledge Distillation

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