The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Localizing Moments in Video with Natural Language
Open accessAbstract
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.
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Long-term recurrent convolutional networks for visual recognition and description
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“Most recently, moment localization was proposed in (Hendricks et al., 2017; Gao et al., 2017), where the goal is to localize a short moment from a long video sequence given a query description.”
BackgroundVaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
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Metadata
- Authors
- Hendricks, L. A., Wang, O., Shechtman, E., Sivic, J., Darrell, T., & Russell, B.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5804-5813
- OpenAlex
- W2742343242
- Cited by
- 958
- References
- 73
- FWCI
- 12.46
- Type
- conference paper
- Language
- en
- Open access
- green
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