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<p class="MsoNormal"><span lang="EN-US">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".<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computer Vision and Pattern Recognition".<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Deadline for manuscript submissions: 20 February 2022.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Background:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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).<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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).<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Manuscript Submission Information:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Manuscripts should be submitted online at
</span><a href="http://www.mdpi.com"><span lang="EN-US">www.mdpi.com</span></a><span lang="EN-US"> 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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Keywords:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">supervised, semisupervised, and unsupervised deep learning<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">deep reinforcement learning and active vision<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">principles and best practices for neural network architecture design<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">generative models for pattern recognition<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">interpretability and explainability of neural networks<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">robustness and generalization of neural networks (e.g., confidence, sample efficiency, out-of-distribution performance)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">metalearning, Auto-ML<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">image classification and segmentation<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">object detection<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">document analysis, e.g., handwriting recognition<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">biometrics<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">industrial applications such as predictive maintenance, automatic quality control, etc.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">medical image processing, digital histopathology<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Special Issue Editors:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Prof. Dr. Thilo Stadelmann, School of Engineering, Zurich University of Applied Sciences ZHAW, 8400 Winterthur, Switzerland<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Interests: artificial intelligence; deep learning; pattern recognition; reinforcement learning; speaker recognition<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Dr. Frank-Peter Schilling, School of Engineering, Zurich University of Applied Sciences ZHAW, 8400 Winterthur, Switzerland<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Interests: artificial intelligence; deep learning; pattern recognition; reinforcement learning<o:p></o:p></span></p>
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