research-article

Deep Learning for Mobile Multimedia: A Survey

Authors:

Vasileios Mezaris,

Francesco G. B. De NataleAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 13, Issue 3s

Article No.: 34, Pages 1 - 22

https://doi.org/10.1145/3092831

Published: 28 June 2017 Publication History

Abstract

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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Deep Learning for Mobile Multimedia: A Survey

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 13, Issue 3s

Special Section on Deep Learning for Mobile Multimedia and Special Section on Best Papers from ACM MMSys/NOSSDAV 2016

August 2017

258 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3119899

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2017 ACM.

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 ACM 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2017

Accepted: 01 April 2017

Revised: 01 April 2017

Received: 01 March 2017

Published in TOMM Volume 13, Issue 3s

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