Multi-Input Deep Learning and Its Application to Video Recognition
| Project/Area Number |
15K16019
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Perceptual information processing
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Inoue Nakamasa 東京工業大学, 情報理工学院, 助教 (10733397)
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 深層学習 / 映像認識 / パターン認識 |
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| Outline of Final Research Achievements |
In this project, we proposed a deep learning method for video recognition. The proposed method is based on vocabulary expansion using word vectors. Its performance is demonstrated on the TRECVID video dataset. We presented this work at ACM Multimedia.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果は、映像や画像を認識するための人工知能技術に関するものである。画像データとテキストデータの情報を組み合わせることで、認識精度が向上することを示した。これは映像のどの部分に何があるかを詳細に検索する次世代の検索システムに役立つ技術である。
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Report
(5 results)
Research Products
(6 results)
