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arXiv · 2306.16410

Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

William Berrios·Gautam Mittal·Tristan Thrush·Douwe Kiela·Amanpreet Singh
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Abstract

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.

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William Berrios, Gautam Mittal, Tristan Thrush, Douwe Kiela, Amanpreet Singh. 2023-06-28. Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language. https://arxiv.org/abs/2306.16410

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