Special Issue on Meta-learning for Image/Video Segmentation
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
• 小类 : 工程：电子与电气 - 2区
Pattern recognition (PR) is in transition as the fast convergence of digital technologies and data science holds the promise to liberate consumer data and provide a faster and more cost-effective way of improving human initiatives. Particularly, deep learning, as one of the automatic discovery methods of regularities in data, is heavily influencing in the computer vision applications, including image segmentation, object tracking and recognition. The data driven-based deep learning algorithms have the potential to reshape the expectations of human’s actions, the way that companies’ stakeholders collaborate, and revamp business models in the various industries.
However, most of recent big data driven-based deep learning algorithms remain challenging to discover patterns in small data, which are insufficient to train deep networks.
To tackle these challenges, meta-learning is a recent technique to entail acquiring a prior over previous tasks and experiences, such that new tasks be learned from small amounts of data. It aims at using machine learning itself to automatically learn the most appropriate algorithms and parameters for deep learning algorithms. Particularly, meta-learning approaches that utilize neural network representations have made progress in few-shot image classification, reinforcement learning, and, more recently, image semantic segmentation, the training algorithms and model architectures have become increasingly specialized to the few-shot domain.
The objective of this special issue is to generate a comprehensive understanding of meta-learning in image/video segmentation for both theoretical and practical implications. This special issue is focused on the scope from responsible small data augmentation, meta-learning engine, to meta-learning applications. Authors are invited to submit outstanding and original unpublished research manuscripts focused on the latest findings in this field.
The following (but not limited to) topics are the particular interests of this special issue, including: