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Inspired by recent trends in vision and language learning, we exploreapplications of attention mechanisms for visio-lingual fusion within anapplication to story-based video understanding. Like other video-based QAtasks, video story understanding requires agents to grasp complex temporaldependencies. However, as it focuses on the narrative aspect of video it alsorequires understanding of the interactions between different characters, aswell as their actions and their motivations. We propose a novel co-attentionaltransformer model to better capture long-term dependencies seen in visualstories such as dramas and measure its performance on the video questionanswering task. We evaluate our approach on the recently introduced DramaQAdataset which features character-centered video story understanding questions.Our model outperforms the baseline model by 8 percentage points overall, atleast 4.95 and up to 12.8 percentage points on all difficulty levels andmanages to beat the winner of the DramaQA challenge.

Co-attentional Transformers for Story-Based Video Understanding