[visionlist] CFP: Special Issue on Complex Deep Learning and Evolutionary Computing Models in Computer Vision

[jungong han]

Special Issue for Journal of Complexity (Impact Factor: 4.62) Call for
Papers

Describing the content of a video/image automatically using natural
language is a challenging task, which is significantly harder than image
labelling and objects recognition. It requires not only accurate
recognition of objects and background scenes but also their attributes,
relations, and saliency (i.e., the likelihood of a recognized object to be
mentioned in the generated text). Deep learning models have demonstrated
great success in dealing with such complex computer vision tasks. Examples
include the use of deep convolutional neural networks combined with
recurrent models for image caption generation. Nevertheless, such deep
learning approaches tend to lose details pertaining to important regional
aspects in the image, and the generated captions tend to be short and less
informative. As a result, complex deep learning models that are able to
capture and translate regional details for better people/object/scene
classification to facilitate accurate image label description generation
are required. On the other hand, the success of deep learning models also
relies on the identification of optimal architectures and hyperparameters
that fit the task. In this regard, the superior search capabilities of
evolutionary computing algorithms allow them to tackle diverse optimization
problems including identification of optimal architectures and
hyperparameters of deep learning models.

This special issue is dedicated to mathematical modelling, simulation,
and/or analysis of deep learning and evolutionary computing models with
complex, adaptive behaviours, and phenomena in science and in real life, as
well as application and implementation of such complex deep learning and
evolutionary computing models to computer vision tasks. The aim is to
stimulate studies pertaining to not only complex deep learning-based
computer vision systems but also optimal topology and hyperparameter
identification for such deep complex networks through evolutionary
computing and related paradigms.

Potential topics include but are not limited to the following:

   - Complex deep neural networks on image description generation
   - Complex deep neural networks on visual question generation or answering
   - Deep neural networks and complex modelling on image segmentation
   - Deep neural networks and complex modelling on visual saliency detection
   - Deep neural networks and complex modelling on human or object
   attribute prediction
   - Deep neural networks and complex modelling on large-scale object
   recognition
   - Deep neural networks and complex modelling on scene classification
   - Deep neural networks and complex modelling on human action recognition
   - Deep neural networks and complex modelling on age estimation
   - Deep neural networks and complex modelling on facial and bodily
   expression recognition
   - Deep neural networks and complex modelling on language generation and
   speech recognition
   - Evolutionary computing techniques for optimal structure identification
   for diverse deep complex neural networks and modelling
   - Evolutionary computing techniques for optimal hyperparameter selection
   for diverse deep complex neural networks and modelling
   - Evolutionary computing techniques for optimal topology and
   hyperparameter identification for diverse complex ensemble neural networks
   and modelling
   - Complex neural networks for health monitoring and surveillance
   - Deep learning applications and complex system modelling for social
   media data analysis (e.g., Facebook photo description generation, online
   news/medical image annotation, script generation for movies, automatic
   description generation for historical photos/paintings in museums, and
   health/security surveillance)

Authors can submit their manuscripts through the Manuscript Tracking System
at https://mts.hindawi.com/submit/journals/complexity/cdl/.
Submission Deadline Friday, 28 September 2018
Publication Date February 2019

Papers are published upon acceptance, regardless of the Special Issue
publication date.
Lead Guest Editor

   - Li Zhang, Northumbria University, Newcastle, UK

Guest Editors

   - Chee Peng Lim, Deakin University, Geelong, Australia
   - Jungong Han, Lancaster University, Lancaster, UK
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