CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
Momentum contrast for unsupervised visual representation learning
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SHRUG-FM fuses geophysical OOD detection, embedding-space OOD detection, and predictive uncertainty via a shallow decision tree to let foundation models abstain from unreliable outputs on burn scar, flood, and landslide tasks.
A fused CNN-ViT model achieves 97.32% accuracy distinguishing AI-generated from real images on the CIFAKE dataset.
citing papers explorer
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A global dataset of continuous urban dashcam driving
CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
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SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
SHRUG-FM fuses geophysical OOD detection, embedding-space OOD detection, and predictive uncertainty via a shallow decision tree to let foundation models abstain from unreliable outputs on burn scar, flood, and landslide tasks.
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AI-Generated Image Recognition via Fusion of CNNs and Vision Transformers
A fused CNN-ViT model achieves 97.32% accuracy distinguishing AI-generated from real images on the CIFAKE dataset.