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Deep learning for visual understanding: A review

Author:
Yanming Guo  Yu Liu  Ard Oerlemans  Songyang Lao  Song Wu  Michael S. Lew  


Journal:
Neurocomputing


Issue Date:
2016


Abstract(summary):

Abstract Deep learning algorithms are a subset of the machine learning algorithms, which aim at discovering multiple levels of distributed representations. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. This work aims to review the state-of-the-art in deep learning algorithms in computer vision by highlighting the contributions and challenges from over 210 recent research papers. It first gives an overview of various deep learning approaches and their recent developments, and then briefly describes their applications in diverse vision tasks, such as image classification, object detection, image retrieval, semantic segmentation and human pose estimation. Finally, the paper summarizes the future trends and challenges in designing and training deep neural networks.


Page:
27-27


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