A Survey on Efficient Vision‐Language Models

ABSTRACT Vision‐language models (VLMs) integrate visual and textual information, enabling a wide range of applications such as image captioning and visual question answering, making them crucial for modern AI systems. However, their high computational demands pose challenges for real‐time applications. This has led to a growing focus on developing efficient vision‐language models. In this survey, we review key techniques for optimizing VLMs on edge and resource‐constrained devices. We also explore compact VLM architectures, frameworks, and provide detailed insights into the performance–memory trade‐offs of efficient VLMs. Furthermore, we establish a GitHub repository at MPSC‐GitHub to compile all surveyed papers, which we will actively update. Our objective is to foster deeper research in this area. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Internet of Things Technologies > Artificial Intelligence