Datasets:
Modalities:
Image
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
computer-vision
vision-language
visual-question-answering
image-segmentation
object-detection
image-classification
License:
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<img src="figures/PaveBench.png" width="90%" />
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## Abstract
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PaveBench is a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. It supports four core tasks: classification, object detection, semantic segmentation, and vision-language question answering. On the visual side, PaveBench provides large-scale annotations on real top-down pavement images and includes a curated hard-distractor subset for robustness evaluation. On the multimodal side, it introduces PaveVQA, a real-image question answering dataset supporting single-turn, multi-turn, and expert-corrected interactions, covering recognition, localization, quantitative estimation, and maintenance reasoning.
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## Abstract
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PaveBench is a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. It supports four core tasks: classification, object detection, semantic segmentation, and vision-language question answering. On the visual side, PaveBench provides large-scale annotations on real top-down pavement images and includes a curated hard-distractor subset for robustness evaluation. On the multimodal side, it introduces PaveVQA, a real-image question answering dataset supporting single-turn, multi-turn, and expert-corrected interactions, covering recognition, localization, quantitative estimation, and maintenance reasoning.
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