  Humans can infer the affordance of objects by extracting related contextual
preconditions for each scenario. For example, upon seeing an image of a broken
cup, we can infer that this precondition prevents the cup from being used for
drinking. Reasoning with preconditions of commonsense is studied in NLP where
the model explicitly gets the contextual precondition. However, it is unclear
if SOTA visual language models (VLMs) can extract such preconditions and infer
the affordance of objects with them. In this work, we introduce the task of
preconditioned visual language inference and rationalization (PVLIR). We
propose a learning resource based on three strategies to retrieve weak
supervision signals for the task and develop a human-verified test set for
evaluation. Our results reveal the shortcomings of SOTA VLM models in the task
and draw a road map to address the challenges ahead in improving them.
