VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
Llvip: A visible-infrared paired dataset for low-light vision
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
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cs.CV 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
A new large-scale synthetic multi-task benchmark dataset supplying pixel-perfect depth, domain-shifted night imagery, and multi-scale low-resolution pairs for aerial remote sensing.
UCGP is a universal physical adversarial patch that compromises cross-modal semantic alignment in IR-VLMs through curved-grid parameterization and representation-space disruption.
IAC-LTH accelerates IAC search for medical segmentation by progressively pruning unstable operations via Jensen-Shannon divergence on per-edge importance distributions, delivering comparable patient-level Dice scores with substantially lower wall-clock cost.
citing papers explorer
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VitaminP: cross-modal learning enables whole-cell segmentation from routine histology
VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
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SyMTRS: Benchmark Multi-Task Synthetic Dataset for Depth, Domain Adaptation and Super-Resolution in Aerial Imagery
A new large-scale synthetic multi-task benchmark dataset supplying pixel-perfect depth, domain-shifted night imagery, and multi-scale low-resolution pairs for aerial remote sensing.
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Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patches for Infrared Vision-Language Models
UCGP is a universal physical adversarial patch that compromises cross-modal semantic alignment in IR-VLMs through curved-grid parameterization and representation-space disruption.
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Efficient Search of Implantable Adaptive Cells for Medical Image Segmentation
IAC-LTH accelerates IAC search for medical segmentation by progressively pruning unstable operations via Jensen-Shannon divergence on per-edge importance distributions, delivering comparable patient-level Dice scores with substantially lower wall-clock cost.