Hierarchical Cross-Modal Graph Consistency Learning for Video-Text Retrieval

Due to the popularity of video contents on the Internet, the information retrieval between videos and texts has attracted broad interest from researchers, which is a challenging cross-modal retrieval task. A common solution is to learn a joint embedding space to measure the cross-modal similarity. However, many existing approaches either pay more attention to textual information, video information, or cross-modal matching methods, but less to all three. We believe that a good video-text retrieval system should take into account all three points, fully exploiting the semantic information of both modalities and considering a comprehensive match. In this paper, we propose a Hierarchical Cross-Modal Graph Consistency Learning Network (HCGC) for video-text retrieval task, which considers multi-level graph consistency for video-text matching. Specifically, we first construct a hierarchical graph representation for the video, which includes three levels from global to local: video, clips and objects. Similarly, the corresponding text graph is constructed according to the semantic relationships among sentence, actions and entities. Then, in order to learn a better match between the video and text graph, we design three types of graph consistency (both direct and indirect): inter-graph parallel consistency, inter-graph cross consistency and intra-graph cross consistency. Extensive experimental results on different video-text datasets demonstrate the effectiveness of our approach on both text-to-video and video-to-text retrieval.