skip to main content
10.1145/3581783.3612078acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

VCMaster: Generating Diverse and Fluent Live Video Comments Based on Multimodal Contexts

Published: 27 October 2023 Publication History

Abstract

Live video commenting, or "bullet screen," is a popular social style on video platforms. Automatic live commenting has been explored as a promising approach to enhance the appeal of videos. However, existing methods neglect the diversity of generated sentences, limiting the potential to obtain human-like comments. In this paper, we introduce a novel framework called "VCMaster" for multimodal live video comments generation, which balances the diversity and quality of generated comments to create human-like sentences. We involve images, subtitles, and contextual comments as inputs to better understand complex video contexts. Then, we propose an effective Hierarchical Cross-Fusion Decoder to integrate high-quality trimodal feature representations by cross-fusing critical information from previous layers. Additionally, we develop a Sentence-Level Contrastive Loss to enlarge the distance between generated and contextual comments by contrastive learning. It helps the model to avoid the pitfall of simply imitating provided contextual comments and losing creativity, encouraging the model to achieve more diverse comments while maintaining high quality. We also construct a large-scale multimodal live video comments dataset with 292,507 comments and three sub-datasets that cover nine general categories. Extensive experiments demonstrate that our model achieves a level of human-like language expression and remarkably fluent, diverse, and engaging generated comments compared to baselines.

Supplemental Material

MP4 File
Oral presentation video to introduce VCMaster framework for Live Video Comments Generation.

References

[1]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).
[2]
Tao Chen, Felix X Yu, Jiawei Chen, Yin Cui, Yan-Ying Chen, and Shih-Fu Chang. 2014b. Object-based visual sentiment concept analysis and application. In Proceedings of the 22nd ACM international conference on Multimedia. 367--376.
[3]
Yan-Ying Chen, Tao Chen, Winston H Hsu, Hong-Yuan Mark Liao, and Shih-Fu Chang. 2014a. Predicting viewer affective comments based on image content in social media. In proceedings of international conference on multimedia retrieval. 233--240.
[4]
Yan-Ying Chen, Tao Chen, Taikun Liu, Hong-Yuan Mark Liao, and Shih-Fu Chang. 2015. Assistive image comment robot-a novel mid-level concept-based representation. IEEE Transactions on Affective Computing, Vol. 6, 3 (2015), 298--311.
[5]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020a. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).
[6]
Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. arXiv preprint arXiv:1805.04833 (2018).
[7]
Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013).
[8]
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751 (2019).
[9]
Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning. PMLR, 5583--5594.
[10]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[11]
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055 (2015).
[12]
Qin Li, Jiankai Yin, and Yan Wang. 2021. An Image Comment Method Based on Emotion Capture Module. In 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC). IEEE, 334--339.
[13]
Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. 2020. Oscar: Object-semantics aligned pre-training for vision-language tasks. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXX 16. Springer, 121--137.
[14]
Yehao Li, Ting Yao, Tao Mei, Hongyang Chao, and Yong Rui. 2016. Share-and-chat: Achieving human-level video commenting by search and multi-view embedding. In Proceedings of the 24th ACM international conference on Multimedia. 928--937.
[15]
Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten De Rijke. 2019. Explainable outfit recommendation with joint outfit matching and comment generation. IEEE Transactions on Knowledge and Data Engineering, Vol. 32, 8 (2019), 1502--1516.
[16]
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, Vol. 32 (2019).
[17]
Shuming Ma, Lei Cui, Damai Dai, Furu Wei, and Xu Sun. 2019. Livebot: Generating live video comments based on visual and textual contexts. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 6810--6817.
[18]
Clara Meister, Tiago Pimentel, Gian Wiher, and Ryan Cotterell. 2022. Typical decoding for natural language generation. arXiv preprint arXiv:2202.00666 (2022).
[19]
Jonghwan Mun, Linjie Yang, Zhou Ren, Ning Xu, and Bohyung Han. 2019. Streamlined dense video captioning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 6588--6597.
[20]
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 311--318.
[21]
Qi Qin, Wenpeng Hu, and Bing Liu. 2020. Feature projection for improved text classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 8161--8171.
[22]
Jiahang Shi, Hanxi Li, Shuixiu Wu, and Mingwen Wang. 2019. Auto Image Comment via Deep Attention. In 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). IEEE, 252--256.
[23]
Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, and Nigel Collier. 2022. A contrastive framework for neural text generation. arXiv preprint arXiv:2202.06417 (2022).
[24]
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems, Vol. 27 (2014).
[25]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017).
[26]
Oriol Vinyals and Quoc Le. 2015. A neural conversational model. arXiv preprint arXiv:1506.05869 (2015).
[27]
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2015. Show and tell: A neural image caption generator. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3156--3164.
[28]
Weiying Wang, Jieting Chen, and Qin Jin. 2020. Videoic: A video interactive comments dataset and multimodal multitask learning for comments generation. In Proceedings of the 28th ACM International Conference on Multimedia. 2599--2607.
[29]
Hao Wu, Gareth James Francis Jones, and Francois Pitie. 2021a. Knowing Where and What to Write in Automated Live Video Comments: A Unified Multi-Task Approach. In Proceedings of the 2021 International Conference on Multimodal Interaction. 619--627.
[30]
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang. 2021b. Cvt: Introducing convolutions to vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 22--31.
[31]
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016).
[32]
An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, and Chang Zhou. 2022. Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese. arXiv preprint arXiv:2211.01335 (2022).
[33]
Yue Yin, Hanzhou Wu, and Xinpeng Zhang. 2021. Neural visual social comment on image-text content. IETE Technical Review, Vol. 38, 1 (2021), 100--111.
[34]
Zehua Zeng, Chenyang Tu, Neng Gao, Cong Xue, Cunqing Ma, and Yiwei Shan. 2021. CMVCG: Non-autoregressive Conditional Masked Live Video Comments Generation Model. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8.
[35]
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675 (2019).

Index Terms

  1. VCMaster: Generating Diverse and Fluent Live Video Comments Based on Multimodal Contexts

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      MM '23: Proceedings of the 31st ACM International Conference on Multimedia
      October 2023
      9913 pages
      ISBN:9798400701085
      DOI:10.1145/3581783
      Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 27 October 2023

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. automatic live commenting
      2. diversity and quality
      3. multimodal

      Qualifiers

      • Research-article

      Funding Sources

      Conference

      MM '23
      Sponsor:
      MM '23: The 31st ACM International Conference on Multimedia
      October 29 - November 3, 2023
      Ottawa ON, Canada

      Acceptance Rates

      Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 178
        Total Downloads
      • Downloads (Last 12 months)87
      • Downloads (Last 6 weeks)3
      Reflects downloads up to 05 Mar 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media