  Text-video retrieval is a challenging task that aims to identify relevant
videos given textual queries. Compared to conventional textual retrieval, the
main obstacle for text-video retrieval is the semantic gap between the textual
nature of queries and the visual richness of video content. Previous works
primarily focus on aligning the query and the video by finely aggregating
word-frame matching signals. Inspired by the human cognitive process of
modularly judging the relevance between text and video, the judgment needs
high-order matching signal due to the consecutive and complex nature of video
contents. In this paper, we propose chunk-level text-video matching, where the
query chunks are extracted to describe a specific retrieval unit, and the video
chunks are segmented into distinct clips from videos. We formulate the
chunk-level matching as n-ary correlations modeling between words of the query
and frames of the video and introduce a multi-modal hypergraph for n-ary
correlation modeling. By representing textual units and video frames as nodes
and using hyperedges to depict their relationships, a multi-modal hypergraph is
constructed. In this way, the query and the video can be aligned in a
high-order semantic space. In addition, to enhance the model's generalization
ability, the extracted features are fed into a variational inference component
for computation, obtaining the variational representation under the Gaussian
distribution. The incorporation of hypergraphs and variational inference allows
our model to capture complex, n-ary interactions among textual and visual
contents. Experimental results demonstrate that our proposed method achieves
state-of-the-art performance on the text-video retrieval task.
