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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien Chin Chen) | |
| dc.contributor.author | Yu-Chen Huang | en |
| dc.contributor.author | 黃于真 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:36:01Z | - |
| dc.date.available | 2020-08-20 | |
| dc.date.copyright | 2019-08-20 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-08 | |
| dc.identifier.citation | [1] BARBIERI, F., ANKE, L.E., BALLESTEROS, M., SOLER, J., and SAGGION, H., 2017. Towards the understanding of gaming audiences by modeling twitch emotes. In Proceedings of the 3rd Workshop on Noisy User-generated Text, 11-20.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74442 | - |
| dc.description.abstract | 直播在網路中形成一股浪潮，帶給人們更臨場的觀看體驗，以及更即時的互動，吸引眾多的數位原住民。多數直播服務都有提供即時聊天室以增加即時互動性，而這些留言除了傳達出觀眾當下的情緒、想法，也提供了另一種和影片內容有關的豐富資訊來源，可用來挖掘觀眾對影片內容的意見。因此我們希望用這樣的概念來做精彩片段之擷取，以觀眾留言背後隱藏的意見來判別出影片中何處是精彩片段的開始與結束。並且結合當前主流的two-staged network的概念，透過兩階段的學習，先過濾出可能的片段，再進一步衡量這些片段屬於精彩片段的機率，減少訓練時間，且提升預測結果表現。最終本研究希望能夠設計出一套系統，可以有效率的透過留言去定位出人們喜愛的精彩片段。 | zh_TW |
| dc.description.abstract | Live streaming raises a burst of upsurge on the Internet. The reason is that it brings people a more on-the-spot viewing experience and more immediate interaction. Most live streaming services provide instant chat rooms to increase interaction. These messages convey the emotion of the audience and provide another rich source of information related to the video content, which can be used to mining the audience's opinions on the video content. Therefore, we hope to use this concept to extract highlight and use the information implicit in the audience message to locate highlight in the video. We also introduce the current mainstream two-staged network concept, through two-stage learning, the first filter out possible segments, further measure the probability that these segments belong to highlight, reduce training time, and improve the performance of prediction results. In the end, this study wants to design a system that can efficiently locate popular highlights through messages. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:36:01Z (GMT). No. of bitstreams: 1 ntu-108-R06725028-1.pdf: 1104399 bytes, checksum: 11562839039f99caaeabb7ecdcdb3a6e (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 摘要 i
ABSTRACT ii TABLE OF CONTENTS iii LIST OF FIGURES iv LIST OF TABLES v 1 INTRODUCTION 1 2 RELATED WORKS 4 2.1 Datasets and Evaluation Metrics 4 2.2 Temporal Action Detection and Two-staged Network 5 3 TWO-STAGED HIGHLIGHT EXTRACTION NETWORK 11 3.1 Sequence Probability Generator 12 3.2 Candidate Generator and Evaluator 14 3.3 Redundant Candidate Suppression 15 4 EXPERIMENT 16 4.1 Datasets and Evaluation Metrics 16 4.2 Effects of System Parameters on Training 18 4.3 Comparisons with other Methods 19 5 CONCLUSION 20 6 ACKNOWLEDGMENTS 21 7 REFERENCES 21 | |
| dc.language.iso | en | |
| dc.subject | 兩階段網絡 | zh_TW |
| dc.subject | 循環神經網路 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 群眾外包 | zh_TW |
| dc.subject | 精彩剪輯 | zh_TW |
| dc.subject | 直播 | zh_TW |
| dc.subject | Crowd Sourcing | en |
| dc.subject | Deep Learning | en |
| dc.subject | Highlight Extraction | en |
| dc.subject | Live Streaming | en |
| dc.subject | Two Staged Network | en |
| dc.subject | Recurrent Neural Network | en |
| dc.title | 結合兩階段網絡於影片精彩片段之擷取 | zh_TW |
| dc.title | Video Highlight Extraction with a Two-staged Network | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張詠淳(Yung-Chun Chang),陳孟彰(Meng Chang Chen) | |
| dc.subject.keyword | 深度學習,循環神經網路,兩階段網絡,直播,精彩剪輯,群眾外包, | zh_TW |
| dc.subject.keyword | Deep Learning,Recurrent Neural Network,Two Staged Network,Live Streaming,Highlight Extraction,Crowd Sourcing, | en |
| dc.relation.page | 26 | |
| dc.identifier.doi | 10.6342/NTU201902938 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-11 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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