Research Topic

Deep Learning in Multimedia: Status, Applications, and Algorithms

About this Research Topic

The development of intelligent data, image, and video analysis systems has experienced a significant boost in recent years thanks to the emergence of a machine learning paradigm known as deep learning (DL) and the availability of low-cost hardware to run neural networks efficiently. DL algorithms have enabled the development of highly accurate systems and have become a standard choice for analyzing different types of data. Dozens of multimedia applications using deep learning to analyze, classify, segment, measure, and recognize content from different modalities of data are currently available. Researchers in industry, public sector, and academia have published hundreds of scientific contributions in this area during the last year alone. These, in turn, facilitate the advent of intelligent systems for a wide spectrum of applications - from decision support systems to cognitive systems that enable efficient human-machine interaction and collaboration - in industry, robotics, health, automation, and assistive technology.

This Research Topic provides a forum for the discussion of the impact of deep learning on multimedia research and intelligent systems, and a focused issue for sharing novel scientific contributions in the area of deep learning in multimedia.

Topics of interest include (but are not limited to):

• Novel approaches for classification, object classification, localization, object detection, segmentation, time series analysis and classification (e.g., videos and sensor data) and registration using DL;
• Content-Based Retrieval (CBIR) of data (sensors, text, images, video, music, audio, etc., going beyond spatial dimension including also time) using DL;
• Content understanding using DL such as convolutional and recurrent neural networks, etc.;
• Cognitive systems using DL, or DL combined with other Machine Learning methods;
• Data generation, fusion, and enhancement methods using DL;
• Multimodal analysis using DL;
• Applications of DL in multimedia research, and intelligent systems more generally.

Authors are invited to submit their original contributions before the deadline following the submission guidelines.


Keywords: deep learning, algorithms, applied deep learning, multimedia, intelligent systems


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The development of intelligent data, image, and video analysis systems has experienced a significant boost in recent years thanks to the emergence of a machine learning paradigm known as deep learning (DL) and the availability of low-cost hardware to run neural networks efficiently. DL algorithms have enabled the development of highly accurate systems and have become a standard choice for analyzing different types of data. Dozens of multimedia applications using deep learning to analyze, classify, segment, measure, and recognize content from different modalities of data are currently available. Researchers in industry, public sector, and academia have published hundreds of scientific contributions in this area during the last year alone. These, in turn, facilitate the advent of intelligent systems for a wide spectrum of applications - from decision support systems to cognitive systems that enable efficient human-machine interaction and collaboration - in industry, robotics, health, automation, and assistive technology.

This Research Topic provides a forum for the discussion of the impact of deep learning on multimedia research and intelligent systems, and a focused issue for sharing novel scientific contributions in the area of deep learning in multimedia.

Topics of interest include (but are not limited to):

• Novel approaches for classification, object classification, localization, object detection, segmentation, time series analysis and classification (e.g., videos and sensor data) and registration using DL;
• Content-Based Retrieval (CBIR) of data (sensors, text, images, video, music, audio, etc., going beyond spatial dimension including also time) using DL;
• Content understanding using DL such as convolutional and recurrent neural networks, etc.;
• Cognitive systems using DL, or DL combined with other Machine Learning methods;
• Data generation, fusion, and enhancement methods using DL;
• Multimodal analysis using DL;
• Applications of DL in multimedia research, and intelligent systems more generally.

Authors are invited to submit their original contributions before the deadline following the submission guidelines.


Keywords: deep learning, algorithms, applied deep learning, multimedia, intelligent systems


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 November 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 November 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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