Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing
This study aims to comprehensively review and empirically evaluate the application of
multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object …
multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object …
Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges
Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning
text, images, and audio to adeptly tackle complex problems, including zero-shot in-context …
text, images, and audio to adeptly tackle complex problems, including zero-shot in-context …
[HTML][HTML] Leveraging Multimodal Large Language Models (MLLMs) for Enhanced Object Detection and Scene Understanding in Thermal Images for Autonomous …
The integration of thermal imaging data with multimodal large language models (MLLMs)
offers promising advancements for enhancing the safety and functionality of autonomous …
offers promising advancements for enhancing the safety and functionality of autonomous …
Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios
Urban traffic management faces significant challenges due to the dynamic environments,
and traditional algorithms fail to quickly adapt to this environment in real-time and predict …
and traditional algorithms fail to quickly adapt to this environment in real-time and predict …
Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos
Near-miss traffic incidents, positioned just above" unsafe acts" on the safety triangle theory,
offer crucial predictive insights for preventing crashes. However, these incidents are often …
offer crucial predictive insights for preventing crashes. However, these incidents are often …