Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
Deep Learning for Intelligent Multimedia Systems

Deep Learning for Intelligent Multimedia Systems

Journal
Impact Score 1.81

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 15-10-2020
Journal Impact Score: 1.81
Journal Name: Multimedia Systems
Publisher: Multimedia Systems
Journal & Submission Website: https://www.springer.com/journal/530

Special Issue Call for Papers

Guest Editors

Meng Liu, Shandong Jianzhu University, China ([email protected])

Yan Yan, Texas State University, USA ([email protected])

Tian Gan, Shandong University, China ([email protected])

Hua Huang, Beijing Institute of Technology, China ([email protected])

Mohan Kankanhalli, National University of Singapore, Singapore ([email protected])

Scope

We are living in the era of multimedia: a tremendous amount of videos, images, and texts are generated, published, and spread daily. In other words, multimedia data is becoming an indispensable part of today’s big data. In fact, the large-scale multimedia data has raised challenges and opportunities for developing intelligent multimedia systems, like retrieval, recommendation, recognition, categorization, and generation systems. Although shallow learning has achieved some progress, its processing capacity for large-scale data is still limited. Meanwhile, deep learning algorithms have enabled the development of highly accurate systems and have become a standard choice for analyzing different types of data. For instance, convolutional neural networks have demonstrated high capability in image classification, recurrent neural networks are widely exploited in modelling temporal sequence in NLP. Inspired by this, we are keen on applying deep learning techniques to boost the performance of multimedia analysis tasks, including object/action detection, image/video captioning, and image/video classification.

The goal of this special issue is to assemble recent advances in the deep-learning based multimedia analytics and relatively new areas. The multimedia data of interest covers a wide spectrum, ranging from text, audio, image, click-through logs, Web videos, EEG signals, to surveillance videos. In particular, we expect the novel contribution focus on the following research lines:

  1. State-of-the-art models and algorithms for various multimedia analysis tasks range from object detection, semantic classification, entity annotation, to multimedia captioning, multimedia question answering and storytelling, which play an important role in public security, entertainment, healthcare, social media, and so on
  2. Novel directions based on the emerging multimedia data
  3. Surveys of recent progress in this research area
  4. The benchmark dataset construction.

Topics

The list of possible topics includes, but is not limited to:

Important dates




Closed Special Issues

Publisher
Journal Details
Closing date
G2R Score
Few-shot Learning for Intelligent Multimedia Systems

Few-shot Learning for Intelligent Multimedia Systems

Multimedia Systems
Closing date: 15-11-2021 G2R Score: 1.81
Few-shot Learning for Intelligent Multimedia Systems

Few-shot Learning for Intelligent Multimedia Systems

Multimedia Systems
Closing date: 15-11-2021 G2R Score: 1.81
Trustworthy Multimedia Big Data Computing

Trustworthy Multimedia Big Data Computing

Multimedia Systems
Closing date: 30-10-2021 G2R Score: 1.81
Data-driven Personalisation of Television Content

Data-driven Personalisation of Television Content

Multimedia Systems
Closing date: 15-09-2021 G2R Score: 1.81
Data-driven Personalisation of Television Content

Data-driven Personalisation of Television Content

Multimedia Systems
Closing date: 15-09-2021 G2R Score: 1.81
Deep Learning for Multimedia Healthcare

Deep Learning for Multimedia Healthcare

Multimedia Systems
Closing date: 15-12-2020 G2R Score: 1.81
Deep Learning for Intelligent Multimedia Systems

Deep Learning for Intelligent Multimedia Systems

Multimedia Systems
Closing date: 15-10-2020 G2R Score: 1.81
Deep learning methods for cyber bullying detection in multi-modal data

Deep learning methods for cyber bullying detection in multi-modal data

Multimedia Systems
Closing date: 30-07-2020 G2R Score: 1.81
Deep Learning for Emerging Big Multimedia Super-Resolution

Deep Learning for Emerging Big Multimedia Super-Resolution

Multimedia Systems
Closing date: 15-07-2020 G2R Score: 1.81
Low complexity methods for multimedia security

Low complexity methods for multimedia security

Multimedia Systems
Closing date: 15-06-2020 G2R Score: 1.81