A two-agent vision-language system with GPT-4o-generated chain-of-thought prompts improves highway weather, wetness, and congestion classification on small curated video datasets, with the biggest gains when sensor data is added.
Evaluating multimodal vision-language model prompting strategies for visual question answering in road scene understanding
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding
A two-agent vision-language system with GPT-4o-generated chain-of-thought prompts improves highway weather, wetness, and congestion classification on small curated video datasets, with the biggest gains when sensor data is added.