  Building joint representations across images and text is an essential step
for tasks such as Visual Question Answering and Video Question Answering. In
this work, we find that the representations must not only jointly capture
features from both modalities but should also be diverse for better
generalization performance. To this end, we propose joint vision-language
representation learning by diversifying the tokenization learning process,
enabling tokens that are sufficiently disentangled from each other to be
learned from both modalities. We observe that our approach outperforms the
baseline models in a majority of settings and is competitive with
state-of-the-art methods.
