Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems
It addresses the need for accurate scene understanding in ITS to improve safety and efficiency, though it is a survey rather than novel research.
This survey reviews the integration of Large Language Models (LLMs) with image segmentation for intelligent transportation systems (ITS), highlighting how this new paradigm enhances scene understanding for applications like autonomous driving and traffic monitoring.
The integration of Large Language Models (LLMs) with computer vision is profoundly transforming perception tasks like image segmentation. For intelligent transportation systems (ITS), where accurate scene understanding is critical for safety and efficiency, this new paradigm offers unprecedented capabilities. This survey systematically reviews the emerging field of LLM-augmented image segmentation, focusing on its applications, challenges, and future directions within ITS. We provide a taxonomy of current approaches based on their prompting mechanisms and core architectures, and we highlight how these innovations can enhance road scene understanding for autonomous driving, traffic monitoring, and infrastructure maintenance. Finally, we identify key challenges, including real-time performance and safety-critical reliability, and outline a perspective centered on explainable, human-centric AI as a prerequisite for the successful deployment of this technology in next-generation transportation systems.