We propose LENS, a modular approach for tackling computer vision problems by leveraging the power of large language models (LLMs). Our system uses a language model to reason over outputs from a set of independent and highly descriptive vision modules that provide exhaustive information about an image. We evaluate the approach on pure computer vision settings such as zero- and few-shot object recognition, as well as on vision and language problems. LENS can be applied to any off-the-shelf LLM and we find that the LLMs with LENS perform highly competitively with much bigger and much more sophisticated systems, without any multimodal training whatsoever. We open-source our code at https://github.com/ContextualAI/lens and provide an interactive demo.
@article{arxiv.2306.16410,
title = {Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language},
author = {William Berrios and Gautam Mittal and Tristan Thrush and Douwe Kiela and Amanpreet Singh},
journal= {arXiv preprint arXiv:2306.16410},
year = {2023}
}