AVA-VLM reduces visual-token usage by 69% while improving PPE-violation F1 by 13 points over direct-QA baselines by training a VLM to adaptively crop high-resolution local regions from a downsampled global image.
Vision transformer-based visual language understanding of the con- struction process
2 Pith papers cite this work, alongside 19 external citations. Polarity classification is still indexing.
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Detection-guided prompting raises small VLM hazard F1 from 34.5% to 50.6% and BERTScore from 0.61 to 0.82 on construction images with only 2.5 ms added latency.
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AVA-VLM: Adaptive Visual Attention-Vision Language Model for In-the-Wild Construction Site Monitoring
AVA-VLM reduces visual-token usage by 69% while improving PPE-violation F1 by 13 points over direct-QA baselines by training a VLM to adaptively crop high-resolution local regions from a downsampled global image.
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Integration of Object Detection and Small VLMs for Construction Safety Hazard Identification
Detection-guided prompting raises small VLM hazard F1 from 34.5% to 50.6% and BERTScore from 0.61 to 0.82 on construction images with only 2.5 ms added latency.