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A key challenge in video question answering is how to realize the cross-modalsemantic alignment between textual concepts and corresponding visual objects.Existing methods mostly seek to align the word representations with the videoregions. However, word representations are often not able to convey a completedescription of textual concepts, which are in general described by thecompositions of certain words. To address this issue, we propose to first builda syntactic dependency tree for each question with an off-the-shelf tool anduse it to guide the extraction of meaningful word compositions. Based on theextracted compositions, a hypergraph is further built by viewing the words asnodes and the compositions as hyperedges. Hypergraph convolutional networks(HCN) are then employed to learn the initial representations of wordcompositions. Afterwards, an optimal transport based method is proposed toperform cross-modal semantic alignment for the textual and visual semanticspace. To reflect the cross-modal influences, the cross-modal information isincorporated into the initial representations, leading to a model namedcross-modality-aware syntactic HCN. Experimental results on three benchmarksshow that our method outperforms all strong baselines. Further analysesdemonstrate the effectiveness of each component, and show that our model isgood at modeling different levels of semantic compositions and filtering outirrelevant information.

Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering