  Visual entailment is a recently proposed multimodal reasoning task where the
goal is to predict the logical relationship of a piece of text to an image. In
this paper, we propose an extension of this task, where the goal is to predict
the logical relationship of fine-grained knowledge elements within a piece of
text to an image. Unlike prior work, our method is inherently explainable and
makes logical predictions at different levels of granularity. Because we lack
fine-grained labels to train our method, we propose a novel multi-instance
learning approach which learns a fine-grained labeling using only sample-level
supervision. We also impose novel semantic structural constraints which ensure
that fine-grained predictions are internally semantically consistent. We
evaluate our method on a new dataset of manually annotated knowledge elements
and show that our method achieves 68.18\% accuracy at this challenging task
while significantly outperforming several strong baselines. Finally, we present
extensive qualitative results illustrating our method's predictions and the
visual evidence our method relied on. Our code and annotated dataset can be
found here: https://github.com/SkrighYZ/FGVE.
