Deep Learning for Computer Vision: Algorithms, Theory and Application

Dear Colleagues,

In recent years, deep learning methods have made important breakthroughs in several fields, with computer vision being one of the most prominent cases. Compared with traditional machine learning methods that rely on handcrafted features, deep learning-based methods allow the acquisition of knowledge directly from data and are hence capable of extracting much more abstract and semantic features with better representation capability. With their cascaded layers that can contain hundreds of millions of parameters, they can model highly nonlinear functions. Despite these advances, issues such as with handling noisy or distorted data, training deep networks with limited labeled data, and training models while protecting data confidentiality and integrity are some of the challenges faced by deep computer vision practitioners. As such, it is now necessary to explore the advanced algorithms, theories, and optimization approaches as applied to deep computer vision.

This Topical Collection aims at bringing together researchers and scientists from various disciplines to present recent advances in dealing with the challenging problems of computer vision within the framework of deep learning. We invite authors to submit manuscripts on topics related to the theme of the Topical Collection and which have not been previously published. The topics of interest include, but are not limited to:

• Deep learning algorithms and models (supervised/weakly supervised/unsupervised)
• Feature learning and feature representation based on deep learning
• Deep learning-based image recognition
• Deep learning-based video understanding
• Deep learning-based remote sensing image analysis
• Deep learning-based saliency/co-saliency detection
• Deep learning-based visual object tracking
• Deep learning-based image super-resolution
• Deep learning-based image quality assessment
• Deep network compression and acceleration

Prof. Dr. Jungong Han
Prof. Dr. Guiguang Ding
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.