He has published 4 books, 4 book chapters and more than 80 technical papers in the areas of artificial intelligence, pattern recognition, machine learning and computer vision.
ATTENTION ATTENTION MOVIE REVIEW FULL
Since January 2021,he has been a full professor at Department of Computer Science and Technology, Ocean University of China.
Between March 2014 and December 2020, he was an associate professor at Department of Computer Science and Technology, Ocean University of China, Qingdao, China. Between October 2011 and July 2013, he was a Postdoctoral Fellow with the Synchromedia Laboratory for Multimedia Communication in Telepresence, University of Quebec, Montreal, Canada. degree in Pattern Recognition and Intelligent Systems from Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China, in 2004, 20, respectively. degree in Operations Research and Cybernetics from Beijing University of Technology (BJUT), Beijing, China, and his Ph.D. degree in Mathematics from Hebei Normal University, Shijiazhuang, China, his M.S. Finally, we discuss the interpretability that attention brings to deep learning and present its potential future trends.
Besides, we summarize network architectures used in conjunction with the attention mechanism and describe some typical applications of attention mechanism. Furthermore, we classify existing attention models according to four criteria: the softness of attention, forms of input feature, input representation, and output representation. Each step of the attention mechanism implemented in the model is described in detail. Toward a better general understanding of attention mechanisms, we define a unified model that is suitable for most attention structures. This paper aims to give an overview of the state-of-the-art attention models proposed in recent years.
With the development of deep neural networks, attention mechanism has been widely used in diverse application domains. It is inspired by the biological systems of humans that tend to focus on the distinctive parts when processing large amounts of information. Attention has arguably become one of the most important concepts in the deep learning field.
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