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.

本文提出了一种新颖的方法来解决视觉问答 - 差异预测任务中由缺失模态引起的问题，使用特权知识蒸馏方案来处理测试时缺少的基本真相答案，通过一个采用图像 / 问题 / 答案三元组作为输入的 “大型” 教师模型以及一种将知识蒸馏到以图像 / 问题对作为输入的目标网络（学生）的模型组合，我们在 VizWiz 和 VQA-V2 答案差异数据集上进行了实验证明了我们的方法的性能和未来研究的多样性。