Gossiping the videos: An embedding-based generative adversarial framework for time-sync comments generation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11441 LNAI, Page: 412-424
2019
- 6Citations
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
Recent years have witnessed the successful rise of the time-sync gossiping comment, or so-called Danmu combined with online videos. Along this line, automatic generation of Danmus may attract users with better interactions. However, this task could be extremely challenging due to the difficulties of informal expressions and semantic gap between text and videos, as Danmus are usually not straightforward descriptions for the videos, but subjective and diverse expressions. To that end, in this paper, we propose a novel Embedding-based Generative Adversarial (E-GA) framework to generate time-sync video comments with gossiping behavior. Specifically, we first model the informal styles of comments via semantic embedding inspired by variational autoencoders (VAE), and then generate Danmus in a generatively adversarial way to deal with the gap between visual and textual content. Extensive experiments on a large-scale real-world dataset demonstrate the effectiveness of our E-GA framework.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85065042807&origin=inward; http://dx.doi.org/10.1007/978-3-030-16142-2_32; https://link.springer.com/10.1007/978-3-030-16142-2_32; https://dx.doi.org/10.1007/978-3-030-16142-2_32; https://link.springer.com/chapter/10.1007/978-3-030-16142-2_32
Springer Science and Business Media LLC
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