Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems

TL;DR

This survey reviews how Large Language Models are integrated with computer vision for image segmentation, emphasizing applications in intelligent transportation systems and discussing challenges and future directions.

Contribution

It provides a comprehensive taxonomy of LLM-augmented image segmentation approaches and discusses their potential in ITS applications and future research challenges.

Findings

LLMs can significantly enhance scene understanding in ITS.

Current approaches vary based on prompting mechanisms and architectures.

Key challenges include real-time performance and safety-critical reliability.

Abstract

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.