CORE MACHINE LEARNING
An Introduction to Vision-Language Modeling
June 05, 2024
Abstract
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.
AUTHORS
Written by
Anurag Ajay
Alexander C. Li
Suzanne Petryk
Zhiqiu Lin
Anas Mahmoud
Jun Chen
Mazda Moayeri
Aishwarya Agrawal
Adrien Bardes
Arjang Talattof
Asli Celikyilmaz
Bargav Jayaraman
Ellen Tan
Haider Al-Tahan
Jonathan Lebensold
Kamalika Chaudhuri
Karen Ullrich
Karthik Padthe
Kate Saenko
Kushal Tirumala
Megan Richards
Pietro Astolfi
Quentin Garrido
Reyhane Askari
Richard Pang
Rim Assouel
Samuel Lavoie
Srihari Jayakumar
Vasu Sharma
Vikas Chandra
Yunyang Xiong
Zechun Liu
Publisher
arXiv
Research Topics
Core Machine Learning
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