  Our commonsense knowledge about objects includes their typical visual
attributes; we know that bananas are typically yellow or green, and not purple.
Text and image corpora, being subject to reporting bias, represent this
world-knowledge to varying degrees of faithfulness. In this paper, we
investigate to what degree unimodal (language-only) and multimodal (image and
language) models capture a broad range of visually salient attributes. To that
end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5
property types (color, shape, material, size, and visual co-occurrence) for
over 5000 subjects. We validate this dataset by showing that our grounded color
data correlates much better than ungrounded text-only data with crowdsourced
color judgments provided by Paik et al. (2021). We then use our dataset to
evaluate pretrained unimodal models and multimodal models. Our results indicate
that multimodal models better reconstruct attribute distributions, but are
still subject to reporting bias. Moreover, increasing model size does not
enhance performance, suggesting that the key to visual commonsense lies in the
data.
