  It is well known that most of the conventional video question answering
(VideoQA) datasets consist of easy questions requiring simple reasoning
processes. However, long videos inevitably contain complex and compositional
semantic structures along with the spatio-temporal axis, which requires a model
to understand the compositional structures inherent in the videos. In this
paper, we suggest a new compositional VideoQA method based on transformer
architecture with a deformable attention mechanism to address the complex
VideoQA tasks. The deformable attentions are introduced to sample a subset of
informative visual features from the dense visual feature map to cover a
temporally long range of frames efficiently. Furthermore, the dependency
structure within the complex question sentences is also combined with the
language embeddings to readily understand the relations among question words.
Extensive experiments and ablation studies show that the suggested dense but
efficient model outperforms other baselines.
