June 05, 2024
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
Written by
Richard Pang
Anurag Ajay
Alexander C. Li
Adrien Bardes
Suzanne Petryk
Oscar Mañas
Zhiqiu Lin
Anas Mahmoud
Bargav Jayaraman
Yunyang Xiong
Jonathan Lebensold
Candace Ross
Srihari Jayakumar
Haider Al-Tahan
Karthik Padthe
Vasu Sharma
Hu Xu
Ellen Tan
Megan Richards
Samuel Lavoie
Pietro Astolfi
Reyhane Askari
Jun Chen
Kushal Tirumala
Rim Assouel
Mazda Moayeri
Arjang Talattof
Kamalika Chaudhuri
Zechun Liu
Quentin Garrido
Karen Ullrich
Aishwarya Agrawal
Kate Saenko
Asli Celikyilmaz
Vikas Chandra
Publisher
arXiv
Research Topics
Core Machine Learning
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