Advanced Deep Learning and Neural Network Technologies for Image Recognition

Dear Colleagues,

Visual recognition is a fundamental cognitive ability in humans which is essential for identifying objects/people. The in-depth explorations of deep learning and neural networks have facilitated advances in understanding the high-level semantic of visual contents. However, despite these advances, there are still many challenges remaining. For example, modern deep learning systems are data starved and rely greatly on the i.i.d. assumption of training and testing tasks. The common image corruptions and the recent presence of adversarial noise or attacks have raised higher requirements of robustness. In addition, application in biometrics, medical image analysis, and autonomous driving scenes require inducing non-trivial domain knowledge.   

The main aim of this Special Issue is to seek original contributions that address the above challenges or highlight emerging applications in image recognition. The topics of interest include but are not limited to: 

• Robust recognition with perturbations;
• Explainable deep learning;
• Multimodal or multiview recognition;
• Weakly supervised learning;
• Self-supervision learning;
• Privacy-aware recognition;
• Data augmentation;
• Transfer learning and domain adaptation;
• Few/one shot learning;
• GANs for image recognition;
• Deepfake detection;
• Distance metric learning;
• Uncertainty estimation;
• Out-of-distribution detection;
• Activity and online learning;
• Advanced and novel biometrics systems;
• Practical and reliable medical image recognition

Dr. Xiaofeng Liu
Dr. Harry Yang
Dr. Zhenhua Guo
Prof. Dr. Jane You
Guest Editors

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