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README.md

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 <!-- 📍 Stay tuned for updates — the Hugging Face repository will be updated with download links and detailed documentation once the paper is accepted. -->
 
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 ## Abstract
 
 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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 - multi-turn dialogue
 - expert-corrected interactions
 
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 The questions are designed around practical pavement inspection needs, including:
 - presence verification
 - distress classification