Truly multi-modal YouTube-8M video classification with video, audio, and text
The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by inclu...
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sg-smu-ink.sis_research-50622018-07-20T05:02:58Z Truly multi-modal YouTube-8M video classification with video, audio, and text WANG, Zhe KUAN, Kingsley RAVANT, Mathieu MANEK, Gaurav SONG, Sibo FANG, Yuan et al, The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text. The newly introduced text data is termed as YouTube-8M-Text. We present a classification framework for the joint use of text, visual and audio features, and conduct an extensive set of experiments to quantify the benefit that this additional mode brings. The inclusion of text yields state-of-the-art results, e.g. 86.7% GAP on the YouTube-8M-Text validation dataset. 2017-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4059 https://ink.library.smu.edu.sg/context/sis_research/article/5062/viewcontent/Truly_Multi_modal_Youtube_8M_2017.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems |
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Databases and Information Systems WANG, Zhe KUAN, Kingsley RAVANT, Mathieu MANEK, Gaurav SONG, Sibo FANG, Yuan et al, Truly multi-modal YouTube-8M video classification with video, audio, and text |
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The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text. The newly introduced text data is termed as YouTube-8M-Text. We present a classification framework for the joint use of text, visual and audio features, and conduct an extensive set of experiments to quantify the benefit that this additional mode brings. The inclusion of text yields state-of-the-art results, e.g. 86.7% GAP on the YouTube-8M-Text validation dataset. |
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WANG, Zhe KUAN, Kingsley RAVANT, Mathieu MANEK, Gaurav SONG, Sibo FANG, Yuan et al, |
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WANG, Zhe KUAN, Kingsley RAVANT, Mathieu MANEK, Gaurav SONG, Sibo FANG, Yuan et al, |
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WANG, Zhe |
title |
Truly multi-modal YouTube-8M video classification with video, audio, and text |
title_short |
Truly multi-modal YouTube-8M video classification with video, audio, and text |
title_full |
Truly multi-modal YouTube-8M video classification with video, audio, and text |
title_fullStr |
Truly multi-modal YouTube-8M video classification with video, audio, and text |
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Truly multi-modal YouTube-8M video classification with video, audio, and text |
title_sort |
truly multi-modal youtube-8m video classification with video, audio, and text |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/4059 https://ink.library.smu.edu.sg/context/sis_research/article/5062/viewcontent/Truly_Multi_modal_Youtube_8M_2017.pdf |
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