  It is a common practice for recent works in vision language cross-modal
reasoning to adopt a binary or multi-choice classification formulation taking
as input a set of source image(s) and textual query. In this work, we take a
sober look at such an unconditional formulation in the sense that no prior
knowledge is specified with respect to the source image(s). Inspired by the
designs of both visual commonsense reasoning and natural language inference
tasks, we propose a new task termed Premise-based Multi-modal Reasoning(PMR)
where a textual premise is the background presumption on each source image. The
PMR dataset contains 15,360 manually annotated samples which are created by a
multi-phase crowd-sourcing process. With selected high-quality movie
screenshots and human-curated premise templates from 6 pre-defined categories,
we ask crowd-source workers to write one true hypothesis and three distractors
(4 choices) given the premise and image through a cross-check procedure.
Besides, we generate adversarial samples to alleviate the annotation artifacts
and double the size of PMR. We benchmark various state-of-the-art (pretrained)
multi-modal inference models on PMR and conduct comprehensive experimental
analyses to showcase the utility of our dataset.
