CloudLULC-Net is an end-to-end heterogeneous SAR-optical fusion network for LULC mapping under cloud contamination that achieves 86.60% OA, 83.29% F1, and 73.51% mIoU on a new global benchmark of 40,223 samples.
In: Proceedings of the IEEE conference on computer vision and pattern recognition
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CrackGeoFM is a multi-task framework that adapts a frozen visual foundation model with FCEM, CFAM, and SMTD modules for crack mask prediction, skeleton reconstruction, and uncertainty estimation, reporting SOTA results across 20 datasets including few-shot settings.
MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.
TransitNet recovers 93% of injected Earth-size and sub-Earth transits at low SNR where BLS and TLS recover only 60%, while running 4-25 times faster.
A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.
Fine-tunes SegFormer-B0 and B1 on FoodSeg103 for ingredient segmentation, reporting mIoU of 0.2521 and 0.3204, then derives ingredient area percentages for nutrition awareness.
citing papers explorer
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Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
CloudLULC-Net is an end-to-end heterogeneous SAR-optical fusion network for LULC mapping under cloud contamination that achieves 86.60% OA, 83.29% F1, and 73.51% mIoU on a new global benchmark of 40,223 samples.
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Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure
CrackGeoFM is a multi-task framework that adapts a frozen visual foundation model with FCEM, CFAM, and SMTD modules for crack mask prediction, skeleton reconstruction, and uncertainty estimation, reporting SOTA results across 20 datasets including few-shot settings.
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Ingredient-Level Food Image Segmentation for Nutrition Awareness
Fine-tunes SegFormer-B0 and B1 on FoodSeg103 for ingredient segmentation, reporting mIoU of 0.2521 and 0.3204, then derives ingredient area percentages for nutrition awareness.