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.
Vru-accident: A vision-language benchmark for video question answering and dense captioning for accident scene understanding
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.