Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues
This work addresses the problem of improving conditional reasoning in vision-language tasks for researchers, though it is incremental as it builds on existing multimodal frameworks.
The authors introduced a new multimodal reasoning task called Premise-based Multimodal Reasoning (PMR), which incorporates textual premises as background knowledge for images, and created a dataset of 15,360 annotated samples to benchmark existing models, showing that current state-of-the-art methods struggle with this formulation.
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.