Uncertainty-aware Cross-Entropy for Semantic Segmentation

7 citations as recorded by crossref.

1. A comparative study on multi-task uncertainty quantification in semantic segmentation and monocular depth estimation S. Landgraf et al. https://doi.org/10.1515/teme-2025-0004
2. CoreNet: A Semi-Supervised Change Detection Framework With Correlation-Guided Feature Refinement and Regional-Wise Pseudo-Label Reassignment T. Lei et al. https://doi.org/10.1109/TGRS.2026.3660884
3. 𝒩𝒮𝑒𝑔𝓂𝑒𝑛𝑡: Label-Specific Deformations for Remote Sensing Image Segmentation Y. Kim et al. https://doi.org/10.1109/LGRS.2025.3595851
4. Distilling population specific expertise into a unified model for generalizable brain tumor segmentation A. Elzayat et al. https://doi.org/10.1038/s41598-026-35627-x
5. EMUFormer: Efficient Multi-task Uncertainties for Reliable Joint Semantic Segmentation and Monocular Depth Estimation S. Landgraf et al. https://doi.org/10.1007/s11263-026-02751-0
6. Fine-Scale Grassland Classification Using UAV-Based Multi-Sensor Image Fusion and Deep Learning Z. Cai et al. https://doi.org/10.3390/rs17183190
7. Two-stage uncertainty-aware adversarial patch attack for semantic segmentation J. Li et al. https://doi.org/10.1108/IJICC-11-2025-0774