SHARP applies a spectrum-aware dynamic RoPE scaling schedule that promotes resolution more strongly in early denoising stages and relaxes it later, outperforming static baselines on quality metrics for remote sensing images.
Bailey, Walter F
7 Pith papers cite this work. Polarity classification is still indexing.
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VSLP infers dense segmentations from global label proportions via a pre-trained transformer for initial confidence maps followed by variational optimization using Wasserstein fidelity and a learned regularizer, outperforming prior weakly supervised methods on histopathology datasets.
FMG-Pan is a model-guided instance-wise adaptation framework for real-world pansharpening that adds physical fidelity constraints to deliver state-of-the-art fusion quality with training and inference completed in seconds on single image pairs.
SPIRE turns IRSTD into centroid regression via single-point supervision and a high-resolution probabilistic encoder, matching prior performance with lower compute and false alarms.
A generative compression model using historical priors for Earth observation data achieves up to 10,000x reduction after exascale training on an Armv9 supercomputer.
A text-guided fusion method for RGB-IR object detection aligns modalities via semantic bridging and incorporates both consensus and discrepancy cues through dynamic recalibration.
GreenScatter retrieves soil moisture through vegetation using a physics-based radiative transfer model and RCS estimation for UAV radar, validated with 4.49% average VWC error in corn and soybean field experiments.
citing papers explorer
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SHARP: Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis
SHARP applies a spectrum-aware dynamic RoPE scaling schedule that promotes resolution more strongly in early denoising stages and relaxes it later, outperforming static baselines on quality metrics for remote sensing images.
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Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions
VSLP infers dense segmentations from global label proportions via a pre-trained transformer for initial confidence maps followed by variational optimization using Wasserstein fidelity and a learned regularizer, outperforming prior weakly supervised methods on histopathology datasets.
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Fast Model-guided Instance-wise Adaptation Framework for Real-world Pansharpening with Fidelity Constraints
FMG-Pan is a model-guided instance-wise adaptation framework for real-world pansharpening that adds physical fidelity constraints to deliver state-of-the-art fusion quality with training and inference completed in seconds on single image pairs.
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Rethinking IRSTD: Single-Point Supervision Guided Encoder-only Framework is Enough for Infrared Small Target Detection
SPIRE turns IRSTD into centroid regression via single-point supervision and a high-resolution probabilistic encoder, matching prior performance with lower compute and false alarms.
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Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction
A generative compression model using historical priors for Earth observation data achieves up to 10,000x reduction after exascale training on an Armv9 supercomputer.
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Bridging the RGB-IR Gap: Consensus and Discrepancy Modeling for Text-Guided Multispectral Detection
A text-guided fusion method for RGB-IR object detection aligns modalities via semantic bridging and incorporates both consensus and discrepancy cues through dynamic recalibration.
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GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar
GreenScatter retrieves soil moisture through vegetation using a physics-based radiative transfer model and RCS estimation for UAV radar, validated with 4.49% average VWC error in corn and soybean field experiments.