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Humans possess a strong capability for reasoning beyond common sense. Forexample, given an unconventional image of a goldfish laying on the table nextto an empty fishbowl, a human would effortlessly determine that the fish is notinside the fishbowl. The case, however, may be different for a vision-languagemodel, whose reasoning could gravitate towards the common scenario that thefish is inside the bowl, despite the visual input. In this paper, we introducea novel probing dataset named ROME (reasoning beyond commonsense knowledge) toevaluate whether the state-of-the-art pre-trained vision-language models havethe reasoning capability to correctly interpret counter-intuitive content. ROMEcontains images that defy commonsense knowledge with regards to color, shape,material, size and positional relation. Experiments on the state-of-the-artpre-trained vision-language models reveal that most of these models are stilllargely incapable of interpreting counter-intuitive scenarios. We hope thatROME will spur further investigations on reasoning beyond commonsense knowledgein vision-language research.

ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common Sense