Finding Moments in Video Collections Using Natural Language
We introduce the task of retrieving relevant video moments from a large corpus of untrimmed, unsegmented videos given a natural language query. Our task poses unique challenges as a system must efficiently identify both the relevant videos and localize the relevant moments in the videos. To address these challenges, we propose SpatioTemporal Alignment with Language (STAL), a model that represents a video moment as a set of regions within a series of short video clips and aligns a natural language query to the moment’s regions. Our alignment cost compares variable-length language and video features using symmetric squared Chamfer distance, which allows for efficient indexing and retrieval of the video moments. Moreover, aligning language features to regions within a video moment allows for finer alignment compared to methods that extract only an aggregate feature from the entire video moment. We evaluate our approach on two recently proposed datasets for temporal localization of moments in video with natural language (DiDeMo Hendricks et al. (2017) and Charades-STA Gao et al. (2017)) extended to our video corpus moment retrieval setting. We show that our STAL re-ranking model outperforms the recently proposed Moment Context Network Hendricks et al. (2017) on all criteria across all datasets on our proposed task, obtaining relative gains of 37%-118% for average recall and up to 30% for median rank. Moreover, our approach achieves more than 130× faster retrieval and 8× smaller index size with a 1M video corpus in an approximate setting.