Rethinking Multi-Modal Alignment in Multi-Choice VideoQA from Feature and Sample Perspectives

Reasoning about causal and temporal event relations in videos is a new destination of Video Question Answering (VideoQA). The major stumbling block to achieve this purpose is the semantic gap between language and video since they are at different levels of abstraction. Existing efforts mainly focus on designing sophisticated architectures while utilizing frame-or object-level visual representations. In this paper, we reconsider the multi-modal alignment in VideoQA from feature and sample perspectives to achieve better performance. From the view of feature, we break down the video into trajectories and first leverage trajectory feature in VideoQA to enhance the alignment between two modalities. Moreover, we adopt a heterogeneous graph architecture and design a hierarchical framework to align both trajectory-level and frame-level visual feature with language feature. In addition, we found that VideoQA models are largely dependent on language priors and always neglect visuallanguage interactions. Thus, two effective yet portable training augmentation strategies are designed to strengthen the cross-modal correspondence ability of our model from the view of sample. Extensive results show that our method outperforms all state-of-the-art models on the challenging NExT-QA benchmark. * Long Chen is the corresponding author. … objects trajectories Q: Why did the boy in orange hold a ball on his head? A: want to throw the ball.