| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2017-10-17 |
| 書誌情報 |
ja : ACM Transactions on Multimedia Computing, Communications, and Applications
巻 13,
号 3,
p. 34-54,
発行日 2017-08-10
|
| タイトル |
|
|
タイトル |
Deep Learning for Mobile Multimedia: A Survey |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
太田, 香
MINH SON, Dao
VASILEIOS, Mezaris
DE NATALE, Francesco G. B.
|
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments. |
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言語 |
en |
| 出版者 |
|
|
出版者 |
ACM |
|
言語 |
en |
| 出版者版へのリンク |
|
|
表示名 |
10.1145/3092831 |
|
URL |
https://doi.org/10.1145/3092831 |
| DOI |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1145/3092831 |
| 日本十進分類法 |
|
|
主題Scheme |
NDC |
|
主題 |
007.1 |
| ISSN |
|
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
1551-6857 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© ACM, 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Volume 13 Issue 3s, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), http://dx.doi.org/10.1145/3092831. |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
|
内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
ACM_2017_13(3)_34 (1.1 MB)
