  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; (2) constructing concise Question-Answer sequences
and designing textual prompts to refine instruction comprehension; (3)
implementing block expansion to fine-tune large VLMs efficiently; and (4)
advancing prediction accuracy via a unique sequential questioning-based
prediction augmentation. Demonstrating top-tier performance, our method
achieved a benchmark score of 33.4308, securing the leading position on the
leaderboard. The code can be found:
https://github.com/alibaba/AICITY2024_Track2_AliOpenTrek_CityLLaVA
