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Recently, Large Language Models (LLMs) have been serving as general-purposeinterfaces, posing a significant demand for comprehensive visual knowledge.However, it remains unclear how well current LLMs and their visually augmentedcounterparts (VaLMs) can master visual commonsense knowledge. To investigatethis, we propose ImageNetVC, a human-annotated dataset specifically designedfor zero- and few-shot visual commonsense evaluation across 1,000 ImageNetcategories. Utilizing ImageNetVC, we benchmark the fundamental visualcommonsense knowledge of both unimodal LLMs and VaLMs. Furthermore, we analyzethe factors affecting the visual commonsense knowledge of large-scale models,providing insights into the development of language models enriched with visualcommonsense knowledge. Our code and dataset are available athttps://github.com/hemingkx/ImageNetVC.

ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories