
This paper introduces a solution that integrates Parallel Dense Video Captioning (PDVC) with CLIP visual features to improve dense captioning of traffic safety scenario videos, addressing real-world challenges through an end-to-end approach.


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enhancing-traffic-safety-through-parallel-dense-video-captioning-for-comprehensive-event-analysis


Enhancing Traffic Safety through Parallel Dense Video Captioning for Comprehensive Event Analysis


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CityLLaVA introduces an efficient fine-tuning framework for Visual Language Models (VLMs) to enhance their comprehension and prediction accuracy in urban traffic scenarios, including bounding box-guided visual prompt engineering, textual prompt construction, and block expansion-based fine-tuning.


efficient-fine-tuning-of-visual-language-models-for-detailed-traffic-safety-analysis-in-urban-scenarios


Efficient Fine-Tuning of Visual Language Models for Detailed Traffic Safety Analysis in Urban Scenarios
