CityLLaVA: Efficient Fine-Tuning for VLMs in City Scenario

arXiv:2405.03194v1 Announce Type: new
Abstract: In the vast and dynamic landscape of urban settings, Traffic Safety Description and Analysis plays a pivotal role in applications ranging from insurance inspection to accident prevention. This paper introduces CityLLaVA, a novel fine-tuning framework for Visual Language Models (VLMs) designed for urban scenarios. CityLLaVA enhances model comprehension and prediction accuracy through (1) employing bounding boxes for optimal visual data preprocessing, including video best-view selection and visual prompt engineering during both training and testing phases; …

arxiv city cs.cv fine-tuning type vlms