Year: 2025
Type: article
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 https://github.com/MPSCUMBC/Efficient-Vision-Language-Models-A-Survey to compile all surveyed papers, which we will actively update. Our objective is to foster deeper research in this area.
DOI: 10.13016/m2vhh3-8yac
Authors: Shinde, Gaurav, Ravi, Anuradha, Dey, Emon, Sakib, Shadman, Rampure, Milind, Roy, Nirmalya
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: EAGER: CNS: RobSenCom: A Middleware to Improve the Connectivity between Heterogeneous Robots and IoT, G5095998002, REU Site: Research Experiences for Undergraduates in Smart Computing and Communications, G6671297155, CAREER: Scalable and Adaptable Cross-Domain Autonomous Health Assessment, G8876996369
OpenAlex ID: W6884870333
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