Learning-Based Object and Pattern Recognition

Dear Colleagues,

Object recognition plays an important role in various real-world applications, including autonomous driving, intelligent visual surveillance, etc. Recent years have witnessed the rapid progress of deep neural networks on learning-based object recognition. However, there is still a large room to design more advanced techniques for object recognition. In addition, it remains non-trivial for practitioners to explore how to apply existing works to more practical applications. This special issue seeks submissions about the latest learning-based object and pattern recognition models, methodologies, and applications. It targets both academic researchers and industrial practitioners from computer vision and machine learning communities. Topics of interest include, but are not limited to:

• Object recognition
• Active learning for object recognition
• Multi-task learning for object recognition
• Deep learning for object recognition
• Meta-learning for object recognition
• Online learning for object recognition
• Model compression for object recognition
• Reinforcement learning for object recognition
• Self-supervised learning for object recognition
• Unsupervised learning for object recognition
• Graph neural network for object recognition

Prof. Dr. Changsheng Li
Dr. Guibo Zhu
Guest 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. Applied Sciences 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.