Mohan S. Kankanhalli
Ioannis (Yiannis) Patras
Takahiro Komamizu
ABSTRACT
With video capture devices becoming widely popular, the amount of video data generated per day has seen a rapid increase over the past few years. Browsing through hours of video data to retrieve useful information is a tedious and boring task. Video Summarization technology has played a crucial role in addressing this issue. It is a well-researched topic in the multimedia community. However, the focus so far has been limited to creating summary to videos which are short (only a few minutes). This workshop aims to call for researchers on relevant background to focus on novel solutions for user-centric narrative summarization of long videos. This workshop will also cover important aspects of video summarization research like what is "important" in a video, how to evaluate the goodness of a created summary, open challenges in video summarization, etc.
- Wei-Ta Chu and Yu-Hsin Liu. 2019. Spatiotemporal modeling and label distribution learning for video summarization. In 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). IEEE, 1--6.Google Scholar
Cross Ref
- Mohamed Elfeki and Ali Borji. 2019. Video summarization via actionness ranking. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 754--763.Google ScholarCross Ref
- Jiri Fajtl, Hajar Sadeghi Sokeh, Vasileios Argyriou, Dorothy Monekosso, and Paolo Remagnino. 2018. Summarizing videos with attention. In Asian Conference on Computer Vision. Springer, 39--54.Google Scholar
- Litong Feng, Ziyin Li, Zhanghui Kuang, and Wei Zhang. 2018. Extractive video summarizer with memory augmented neural networks. In Proceedings of the 26th ACM international conference on Multimedia. 976--983.Google ScholarDigital Library
- Xufeng He, Yang Hua, Tao Song, Zongpu Zhang, Zhengui Xue, Ruhui Ma, Neil Robertson, and Haibing Guan. 2019. Unsupervised video summarization with attentive conditional generative adversarial networks. In Proceedings of the 27th ACM International Conference on multimedia. 2296--2304.Google ScholarDigital Library
- Yunjae Jung, Donghyeon Cho, Dahun Kim, Sanghyun Woo, and In So Kweon. 2019. Discriminative feature learning for unsupervised video summarization. In Proceedings of the AAAI Conference on artificial intelligence, Vol. 33. 8537--8544.Google ScholarDigital Library
- Shamit Lal, Shivam Duggal, and Indu Sreedevi. 2019. Online video summarization: Predicting future to better summarize present. In 2019 IEEE Winter Conference on applications of computer vision (WACV). IEEE, 471--480.Google ScholarCross Ref
- Behrooz Mahasseni, Michael Lam, and Sinisa Todorovic. 2017. Unsupervised video summarization with adversarial lstm networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 202--211.Google ScholarCross Ref
- Mrigank Rochan and Yang Wang. 2019. Video summarization by learning from unpaired data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7902--7911.Google ScholarCross Ref
- Mrigank Rochan, Linwei Ye, and Yang Wang. 2018. Video summarization using fully convolutional sequence networks. In Proceedings of the European conference on computer vision (ECCV). 347--363.Google ScholarDigital Library
- Yongkang Wong, Shaojing Fan, Yangyang Guo, Ziwei Xu, Karen Stephen, Rishabh Sheoran, Anusha Bhamidipati, Vivek Barsopia, Jianquan Liu, and Mohan S. Kankanhalli. 2022. Compute to Tell the Tale: Goal-Driven Narrative Generation. In ACM International Conference on Multimedia. 6875?6882.Google Scholar
- Li Yuan, Francis EH Tay, Ping Li, Li Zhou, and Jiashi Feng. 2019. Cycle-SUM: Cycle-consistent adversarial LSTM networks for unsupervised video summarization. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 9143--9150.Google ScholarDigital Library
Index Terms
- NarSUM '23: The 2nd Workshop on User-Centric Narrative Summarization of Long Videos
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