  Video Question Answering (Video QA) requires fine-grained understanding of
both video and language modalities to answer the given questions. In this
paper, we propose novel training schemes for multiple-choice video question
answering with a self-supervised pre-training stage and a supervised
contrastive learning in the main stage as an auxiliary learning. In the
self-supervised pre-training stage, we transform the original problem format of
predicting the correct answer into the one that predicts the relevant question
to provide a model with broader contextual inputs without any further dataset
or annotation. For contrastive learning in the main stage, we add a masking
noise to the input corresponding to the ground-truth answer, and consider the
original input of the ground-truth answer as a positive sample, while treating
the rest as negative samples. By mapping the positive sample closer to the
masked input, we show that the model performance is improved. We further employ
locally aligned attention to focus more effectively on the video frames that
are particularly relevant to the given corresponding subtitle sentences. We
evaluate our proposed model on highly competitive benchmark datasets related to
multiple-choice video QA: TVQA, TVQA+, and DramaQA. Experimental results show
that our model achieves state-of-the-art performance on all datasets. We also
validate our approaches through further analyses.
