CiF is a large new civil infrastructure segmentation dataset that shows zero-shot foundation models and domain-supervised models plateau at roughly 25% mAP, establishing infrastructure inspection as an open challenge for current visual AI.
Lawrence Zitnick
4 Pith papers cite this work. Polarity classification is still indexing.
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H2G distills 2D foundation-model affinities into a Lorentz hyperbolic feature field that represents hierarchical 3D groupings at multiple granularities.
BMD-45 is a new large-scale CCTV vehicle detection dataset from developing cities that reveals a 2.5x performance gap for models adapted from prior benchmarks.
STAL transfers spectral tail uplift cues via a frequency teacher to train a spatial detector for AI-generated images, discarding frequency modules at inference for strong cross-generator generalization.
citing papers explorer
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Cracks in the Foundation: A Civil Infrastructure Dataset to Challenge Vision Foundation Models
CiF is a large new civil infrastructure segmentation dataset that shows zero-shot foundation models and domain-supervised models plateau at roughly 25% mAP, establishing infrastructure inspection as an open challenge for current visual AI.
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H2G: Hierarchy-Aware Hyperbolic Grouping for 3D Scenes
H2G distills 2D foundation-model affinities into a Lorentz hyperbolic feature field that represents hierarchical 3D groupings at multiple granularities.
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BMD-45: A Large-Scale CCTV Vehicle Detection Dataset for Urban Traffic in Developing Cities
BMD-45 is a new large-scale CCTV vehicle detection dataset from developing cities that reveals a 2.5x performance gap for models adapted from prior benchmarks.
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Spectral Tail Auxiliary Learning for AI-Generated Image Detection
STAL transfers spectral tail uplift cues via a frequency teacher to train a spatial detector for AI-generated images, discarding frequency modules at inference for strong cross-generator generalization.