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Video ChatBot: Triggering Live Social Interactions by Automatic Video Commenting

Published: 01 October 2016 Publication History

Abstract

We demonstrate a video chatbot, which can generate human-level emotional comments referring to the videos shared by users and trigger a conversation with users. Our video chatbot performs a large-scale similar video search to find visually similar videos w.r.t. a given video using approximate nearest-neighbor search. Then, the comments associated with the searched similar videos are ranked by learning a deep multi-view embedding space for modeling video content, visual sentiment and textual comments. The top ranked comments are selected as responses to the given video and trigger the succeeding text-based chat between users and the chatbot. The demonstration is conducted on a newly collected dataset with over 102K videos and 10.6M comments. Moreover, our video chatbot has great potential to increase live social interactions.

References

[1]
Y. Pan, T. Mei, T. Yao, H. Li, and Y. Rui. Jointly modeling embedding and translation to bridge video and language. In CVPR, 2016.
[2]
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
[3]
D. Tran, L. D. Bourdev, R. Fergus, L. Torresani, and M. Paluri. C3d: generic features for video analysis. In ICCV, 2015.
[4]
J. Wang and S. Li. Query-driven iterated neighborhood graph search for large scale indexing. In ACM MM, 2012.
[5]
T. Yao, T. Mei, and C.-W. Ngo. Learning query and image similarities with ranking canonical correlation analysis. In ICCV, 2015.

Cited By

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  • (2024)Sentiment-Oriented Transformer-Based Variational Autoencoder Network for Live Video CommentingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363333420:4(1-24)Online publication date: 11-Jan-2024
  • (2023)Multimodal Expressive Embodied Conversational Agent DesignHCI International 2023 Posters10.1007/978-3-031-35989-7_31(244-249)Online publication date: 9-Jul-2023
  • (2021)Towards Augmented Reality Driven Human-City Interaction: Current Research on Mobile Headsets and Future ChallengesACM Computing Surveys10.1145/346796354:8(1-38)Online publication date: 4-Oct-2021

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Published In

cover image ACM Conferences
MM '16: Proceedings of the 24th ACM international conference on Multimedia
October 2016
1542 pages
ISBN:9781450336031
DOI:10.1145/2964284
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2016

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Author Tags

  1. deep convolutional neural networks
  2. multi-view embedding
  3. video commenting

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  • Demonstration

Conference

MM '16
Sponsor:
MM '16: ACM Multimedia Conference
October 15 - 19, 2016
Amsterdam, The Netherlands

Acceptance Rates

MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2024)Sentiment-Oriented Transformer-Based Variational Autoencoder Network for Live Video CommentingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363333420:4(1-24)Online publication date: 11-Jan-2024
  • (2023)Multimodal Expressive Embodied Conversational Agent DesignHCI International 2023 Posters10.1007/978-3-031-35989-7_31(244-249)Online publication date: 9-Jul-2023
  • (2021)Towards Augmented Reality Driven Human-City Interaction: Current Research on Mobile Headsets and Future ChallengesACM Computing Surveys10.1145/346796354:8(1-38)Online publication date: 4-Oct-2021

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