The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
TVSum: Summarizing web videos using titles
Abstract
Video summarization is a challenging problem in part because knowing which part of a video is important requires prior knowledge about its main topic. We present TVSum, an unsupervised video summarization framework that uses title-based image search results to find visually important shots. We observe that a video title is often carefully chosen to be maximally descriptive of its main topic, and hence images related to the title can serve as a proxy for important visual concepts of the main topic. However, because titles are free-formed, unconstrained, and often written ambiguously, images searched using the title can contain noise (images irrelevant to video content) and variance (images of different topics). To deal with this challenge, we developed a novel co-archetypal analysis technique that learns canonical visual concepts shared between video and images, but not in either alone, by finding a joint-factorial representation of two data sets. We introduce a new benchmark dataset, TVSum50, that contains 50 videos and their shot-level importance scores annotated via crowdsourcing. Experimental results on two datasets, SumMe and TVSum50, suggest our approach produces superior quality summaries compared to several recently proposed approaches.
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Cites
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Cited by
Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language
Zhang, S., Peng, H., Fu, J., & Luo, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6984 · Cited by 482
Stacked Memory Network for Video Summarization
Wang, J., Wang, W., Wang, Z., Wang, L., Feng, D., & Tan, T. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3350992 · Cited by 60
Han, S., Huang, W., Shi, H., Zhuo, L., Su, X., Zhang, S., Zhou, X., Qi, X., Liao, Y., & Liu, S. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.02438 · Cited by 22
Metadata
- Authors
- Song, Y., Vallmitjana, J., Stent, A., & Jaimes, A.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 5179-5187
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1924343884
- Cited by
- 623
- References
- 44
- FWCI
- 15.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Song, Y., Vallmitjana, J., Stent, A., & Jaimes, A. (2015). TVSum: Summarizing web videos using titles. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7299154
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