  Multimodal image-language transformers have achieved impressive results on a
variety of tasks that rely on fine-tuning (e.g., visual question answering and
image retrieval). We are interested in shedding light on the quality of their
pretrained representations -- in particular, if these models can distinguish
different types of verbs or if they rely solely on nouns in a given sentence.
To do so, we collect a dataset of image-sentence pairs (in English) consisting
of 421 verbs that are either visual or commonly found in the pretraining data
(i.e., the Conceptual Captions dataset). We use this dataset to evaluate
pretrained image-language transformers and find that they fail more in
situations that require verb understanding compared to other parts of speech.
We also investigate what category of verbs are particularly challenging.
