Video2Subtitle: Matching Weakly-Synchronized Sequences via Dynamic Temporal Alignment

This paper investigates a new research task in multimedia analysis, dubbed as Video2Subtitle. The goal of this task is to finding the most plausible subtitle from a large pool for a querying video clip. We assume that the temporal duration of each sentence in a subtitle is unknown. Compared with existing cross-modal matching tasks, the proposed Video2Subtitle confronts several new challenges. In particular, video frames / subtitle sentences are temporally ordered, respectively, yet no precise synchronization is available. This casts Video2Subtitle into a problem of matching weakly-synchronized sequences. In this work, our technical contributions are two-fold. First, we construct a large-scale benchmark for the Video2Subtitle task. It consists of about 100K video clip / subtitle pairs with a full duration of 759 hours. All data are automatically trimmed from conversational sub-parts of movies and youtube videos. Secondly, an ideal algorithm for tackling Video2Subtitle requires both temporal synchronization of the visual / textual sequences, but also strong semantic consistency between two modalities. To this end, we propose a novel algorithm with the key traits of heterogeneous multi-cue fusion and dynamic temporal alignment. The proposed method demonstrates excellent performances in comparison with several state-of-the-art cross-modal matching methods. Additionally, we also depict a few interesting applications of Video2Subtitle, such as re-generating subtitle for given videos.