  In this work, we address the issues of missing modalities that have arisen
from the Visual Question Answer-Difference prediction task and find a novel
method to solve the task at hand. We address the missing modality-the ground
truth answers-that are not present at test time and use a privileged knowledge
distillation scheme to deal with the issue of the missing modality. In order to
efficiently do so, we first introduce a model, the "Big" Teacher, that takes
the image/question/answer triplet as its input and outperforms the baseline,
then use a combination of models to distill knowledge to a target network
(student) that only takes the image/question pair as its inputs. We experiment
our models on the VizWiz and VQA-V2 Answer Difference datasets and show through
extensive experimentation and ablation the performances of our method and a
diverse possibility for future research.
