Papers › SpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation
SpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation
Siqi Fan, Zhe Wang, Yan Wang, Jingjing Liu
For semantic segmentation in urban scene understanding, RGB cameras alone often fail to capture a clear holistic topology in challenging lighting conditions. Thermal signal is an informative additional channel that can bring to light the contour and fine-grained texture of blurred regions in low-quality RGB image. Aiming at practical RGB-T (thermal) segmentation, we systematically propose a Spatial-aware Demand-guided Recursive Meshing (SpiderMesh) framework that: 1) proactively compensates inadequate contextual semantics in optically-impaired regions via a demand-guided target masking algorithm; 2) refines multimodal semantic features with recursive meshing to improve pixel-level semantic analysis performance. We further introduce an asymmetric data augmentation technique M-CutOut, and enable semi-supervised learning to fully utilize RGB-T labels only sparsely available in practical use. Extensive experiments on MFNet and PST900 datasets demonstrate that SpiderMesh achieves state-of-the-art performance on standard RGB-T segmentation benchmarks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Thermal Image Segmentation | MFN Dataset | SpiderMesh (B4) | mIOU | 58.4 | #14 of 55 | Archive leaderboard | report |
| Thermal Image Segmentation | MFN Dataset | SpiderMesh (ResNet-152) | mIOU | 57.9 | #17 of 55 | Archive leaderboard | report |
| Thermal Image Segmentation | MFN Dataset | SpiderMesh (ResNet-101) | mIOU | 56.1 | #26 of 55 | Archive leaderboard | report |
| Thermal Image Segmentation | MFN Dataset | SpiderMesh (ResNet-50) | mIOU | 54.4 | #37 of 55 | Archive leaderboard | report |
| Thermal Image Segmentation | PST900 | SpiderMesh | mIoU | 82.3 | #12 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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