![]() After a nearly thirty-year-long career, it is remarkable that these musicians can maintain a keen sense of ingenuity. Over somber woodwinds and hushed guitar, Yorke sings “When we realize that we are broke and nothing mends / We can drop under the surface.” These haunting words fade into the lush instrumentation that swells, accompanied by Yorke’s melismatic falsetto.Ī Light for Attracting Attention is a brilliant album, one which shows the artistic genius of Yorke and Greenwood. The final track, “Skrting On the Surface” ends on a rather depressing note. The band can’t help but make the listener ache as Yorke wistfully sings “I talk to the face in the mirror / Now he can’t get through / Turns out we’re in this together.” “Free In The Knowledge” is a poignant and introspective track, recognizing the finiteness of existence and making peace with change. Guitar, drums and other miscellaneous instruments are in full effect as Yorke strains “All those beautiful young hopes and dreams / Devoured by those evil eyes and those piggy limbs.” This spirit is quickly diminished with the next track, “Pana-vision” which is highlighted by Yorke’s ghostly falsetto and the unsettling piano arpeggios. “You Will Never Work In Television Again” has the angst and energy of The Bends, with the lyricism of OK Computer. A distinct melody takes shape through the song as Yorke sings “Opposites attract.” The beat tightens into a groove with the second track on the record. His sharp and melodic “Please, we all want the same / Please, we are all the same,” makes you feel his desperation. Yorke’s vocal performance is dripping with an intense feeling of yearning. He is member of ACM, IEEE, IAPR, APNNS and CCF, professional committee member of CAAI-PR, CAA-PRMI and CSIG-DIAR, and trustee of Shandong Association of Artificial Intelligence.The first two tracks in the album are cheekily titled “The Same” and “The Opposite.” Both feature the classic Radiohead composition of intense synths, eerie vocal manipulation and ambiguous lyricism. He has won the Best Paper Award of BICS2019 and the APNNS Young Researcher Award. He has been awarded outstanding reviewer by several journals, such as Pattern Recognition, Knowledge-Based Systems, Neurocomputing and Cognitive Systems Research. ![]() ![]() He has served as Chair/PC member/reviewer for many international conferences and top journals, such as IEEE TNNLS, IEEE TKDE, IEEE TCSVT, Pattern Recognition, Knowledge-Based Systems, Neurocomputing, ACM TKDD, AAAI, AISTATS, ICPR, IJCNN, ICONIP and ICDAR. His research interests include pattern recognition, machine learning and computer vision. 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. 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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