Localizing Moments in Video with Natural Language
Lisa Anne Hendricks,Lisa Anne Hendricks,Oliver Wang,Eli Shechtman,Josef Sivic,Trevor Darrell,Bryan Russell +6 more
- pp 5804-5813
TLDR
In this paper, a Moment Context Network (MCNCLN) is proposed to localize natural language queries in videos by integrating local and global video features over time, which can identify a specific temporal segment, or moment, from a video given a natural language text description.Abstract:
We consider retrieving a specific temporal segment, or moment, from a video given a natural language text description. Methods designed to retrieve whole video clips with natural language determine what occurs in a video but not when. To address this issue, we propose the Moment Context Network (MCN) which effectively localizes natural language queries in videos by integrating local and global video features over time. A key obstacle to training our MCN model is that current video datasets do not include pairs of localized video segments and referring expressions, or text descriptions which uniquely identify a corresponding moment. Therefore, we collect the Distinct Describable Moments (DiDeMo) dataset which consists of over 10,000 unedited, personal videos in diverse visual settings with pairs of localized video segments and referring expressions. We demonstrate that MCN outperforms several baseline methods and believe that our initial results together with the release of DiDeMo will inspire further research on localizing video moments with natural language.read more
Citations
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HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
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