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.
Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large scale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization.
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.