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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In: Proceedings of the IEEE conference on computer vision and pattern recognition
7 Pith papers cite this work, alongside 36,888 external citations. Polarity classification is still indexing.
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TransitNet recovers low-SNR (6–8) and Earth-size injected Kepler transits at 95.2% accuracy and 93% recall, beating TLS/BLS while estimating midpoints from attention.
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.
A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.
An integrated UAV-AI pipeline using SegFormer and ConvNeXt achieves 97.6% dominant-species agreement and <8% dominance error against field quadrat surveys for two salt marsh grass species.
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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TransitNet: A Compact Attention-Augmented Deep Learning Framework for Low-SNR Transit Blind Searches
TransitNet recovers low-SNR (6–8) and Earth-size injected Kepler transits at 95.2% accuracy and 93% recall, beating TLS/BLS while estimating midpoints from attention.
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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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MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation
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.
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Towards Migrating Neural Network Implementations
A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.
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EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping
An integrated UAV-AI pipeline using SegFormer and ConvNeXt achieves 97.6% dominant-species agreement and <8% dominance error against field quadrat surveys for two salt marsh grass species.
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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.