UFO is a new publicly available hand-labeled dataset of 215 PlanetScope image chips from 14 urban flood events annotated for inundated and non-inundated areas, validated via segmentation model with 77.3 mean IoU and comparisons to existing water products.
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2026 4representative citing papers
ZID-Net decouples diffusion-based priors into a training-only head to create an efficient feed-forward network for single-image dehazing, reporting 40.75 dB PSNR on RESIDE and 19 ms inference.
DeepSignature embeds digitally signed content-encoding watermarks via neural networks for robust image authentication, source attribution, and latent-space tamper localization.
H-SemiS decomposes multi-class KOA severity grading into binary sub-tasks in a semi-supervised setup with self-supervision and quantum-inspired mixing, outperforming baselines on two multi-class and two binary datasets.
citing papers explorer
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Urban Flood Observations (UFO): A hand-labeled training and validation dataset of post-flood inundation
UFO is a new publicly available hand-labeled dataset of 215 PlanetScope image chips from 14 urban flood events annotated for inundated and non-inundated areas, validated via segmentation model with 77.3 mean IoU and comparisons to existing water products.
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ZID-Net: Zero-Inference Diffusion Prior Decoupling Network for Single Image Dehazing
ZID-Net decouples diffusion-based priors into a training-only head to create an efficient feed-forward network for single-image dehazing, reporting 40.75 dB PSNR on RESIDE and 19 ms inference.
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DeepSignature: Digitally Signed, Content-Encoding Watermarks for Robust and Transparent Image Authentication
DeepSignature embeds digitally signed content-encoding watermarks via neural networks for robust image authentication, source attribution, and latent-space tamper localization.
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H-SemiS: Hierarchical Fusion of Semi and Self-Supervised Learning for Knee Osteoarthritis Severity Grading
H-SemiS decomposes multi-class KOA severity grading into binary sub-tasks in a semi-supervised setup with self-supervision and quantum-inspired mixing, outperforming baselines on two multi-class and two binary datasets.