Call for papers for a special issue On “Deep Learning for Information Fusion”

  in Special Issue   Posted on August 13, 2017

Information for the Special Issue

Submission Deadline: Thu 30 Nov 2017
Journal Impact Factor : 13.669
Journal Name : Information Fusion
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

In the last couple of years, deep learning algorithms have pushed the boundaries for numerous problems in areas such as computer vision, natural language processing, and audio processing. The performance of advanced machine (deep) learning algorithms has attained the numbers which were unexpected a decade ago. For a given problem, information can be obtained from multiple sources and such multimodal datasets represent information at varying abstraction levels. Combining information from multiple sources can further boost the performance. Recent research has also focused on multimodal deep learning, i.e. representation learning paradigm which learns joint/combined feature from multiple sources. In this relatively new area, information from multiple sources are combined in a deep learning framework. For example, combining audio and video data to obtain joint feature representation.

This special issue focuses on sharing recent advances in algorithms and applications that involve combining multiple sources of information using deep learning. Topics appropriate for this special issue include novel supervised, unsupervised, semi-supervised and reinforcement algorithms, new formulations, and applications related to deep learning and information fusion.

Topics appropriate for this special issue include (but are not necessarily limited to):

  • New models for multimodal deep learning
  • Deep learning models for multimodal sensing and processing
  • Multi-sensor fusion with deep learning
  • Feature fusion using deep learning models
  • Shared representation learning
  • Combining multiple sources in deep learning
  • Combining multiple deep learning models
  • Joint deep feature learning
  • Cross modality learning
  • Hierarchical deep learning models for information fusion
  • Transfer learning in multimodal deep learning
  • Multimodal deep metric learning
  • Applications of multimodal deep learning in image and computer vision related areas such as object recognition, biometrics, forensics and medical data analysis
  • Applications of multimodal deep learning in text related areas such as natural language processing, and image to text generation.

Manuscripts must clearly delineate the role of deep learning information fusion. The manuscript will be judged solely on the basis of new contributions excluding the contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein.

Manuscripts should be submitted electronically at:

The corresponding author will have to create a user profile if one has not been established previously at Elsevier.

To ensure that all manuscripts are correctly identified for consideration in the Special Issue of Deep Learning for Information Fusion, it is important that authors select “VSI: DL-Fusion\”.

Guest Editors

Mayank Vatsa, IIIT-Delhi (India),
Richa Singh, IIIT-Delhi (India),
Afzel Noore, West Virginia University (USA),

Deadline for Submission: November 30, 2017

Closed Special Issues