Connectionists: Special Issue "Advances in Deep Neural Networks for Visual Pattern Recognition" -> deadline Feb 20

[Stadelmann Thilo (stdm)]

Friendly reminder and invitation to submit your work and the work of your students to the Special Issue "Advances in Deep Neural Networks for Visual Pattern Recognition".

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computer Vision and Pattern Recognition".

Deadline for manuscript submissions: 20 February 2022.

Background:

Deep neural networks have been the standard for pattern recognition in computer vision since the ImageNet competition in 2012. Great advances have been made since then, both methodologically and in terms of successful applications. However, with every passing year of alleged breakthroughs, we become more and more aware of the many remaining unknowns, almost to the point of admitting: "We know that we know nothing" (yet).

Methodologically, for example, evidence is growing that the long-standing image recognition paradigm of episodic classification of IID samples is stagnating, and that active vision approaches are necessary to increase recognition scores by another order of magnitude (Gori, "What's Wrong with Computer Vision?", 2018). Theoretically, it is still not well understood why deep neural networks are so very efficient in learning generalizable functions (Tishby, "Deep learning and the information bottleneck principle", 2015). This leads to a current trend of empirically detected design principles for neural networks (Kaplan et al., "Scaling laws for neural language models", 2020). Practically, many real-world applications are suffering from an unstable performance of learned models, raising issues of robustness, interpretability, and deployability, not speaking of issues with small training sets (related to sample complexity) (Stadelmann et al., "Deep Learning in the Wild", 2018).

In this Special Issue of the Journal of Imaging, we request contributions that cover all three aspects: methodical, theoretical, and practical work addressing current issues in visual pattern recognition with novel insights and scientifically founded evaluations.


Manuscript Submission Information:

Manuscripts should be submitted online at www.mdpi.com<http://www.mdpi.com> by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Keywords:

supervised, semisupervised, and unsupervised deep learning
deep reinforcement learning and active vision
principles and best practices for neural network architecture design
generative models for pattern recognition
interpretability and explainability of neural networks
robustness and generalization of neural networks (e.g., confidence, sample efficiency, out-of-distribution performance)
metalearning, Auto-ML
image classification and segmentation
object detection
document analysis, e.g., handwriting recognition
biometrics
industrial applications such as predictive maintenance, automatic quality control, etc.
medical image processing, digital histopathology


Special Issue Editors:

Prof. Dr. Thilo Stadelmann, School of Engineering, Zurich University of Applied Sciences ZHAW, 8400 Winterthur, Switzerland
Interests: artificial intelligence; deep learning; pattern recognition; reinforcement learning; speaker recognition

Dr. Frank-Peter Schilling, School of Engineering, Zurich University of Applied Sciences ZHAW, 8400 Winterthur, Switzerland
Interests: artificial intelligence; deep learning; pattern recognition; reinforcement learning
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20220128/4ec48b68/attachment.html>