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Titlebook: Digital TV and Wireless Multimedia Communication; 17th International F Guangtao Zhai,Jun Zhou,Xiaokang Yang Conference proceedings 2021 Spr

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楼主: 极大
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https://doi.org/10.1007/978-3-658-41068-1er identification. Text factors contain two types of information: the number of characters in both query and reference and the number of the same characters in both query and reference. We conclude that the performance increases when the query and reference contain more characters, and the minimum n
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Conference proceedings 2021hanghai, China, in December 2020..The 21 full papers and 16 short papers presented in this volume were carefully reviewed and selected from 120 submissions. They were organized in topical sections on image processing; machine learning; quality assessment; telecommunications; video surveillance; and virtual reality..
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1865-0929 120 submissions. They were organized in topical sections on image processing; machine learning; quality assessment; telecommunications; video surveillance; and virtual reality..978-981-16-1193-3978-981-16-1194-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
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https://doi.org/10.1007/978-3-531-19691-6ture. Eventually, we used a softmax classifier to classify the emotion information. To manifest the benefit of the proposed architecture, we evaluate it on the TSCBE dataset constructed by our group. Our results can be indicative of that the model proposed in this work can achieve higher accuracy compared with baseline.
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https://doi.org/10.1007/978-3-658-41068-1umber of needed characters is 50. The number of the same characters in both query and reference has little influence on the identification when the number of characters is more than 50. The conclusions are verified by repeated writer identification tests with different amount of characters on the handwriting document pages.
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UAV Aerial Image Detection Based on Improved FCOS Algorithm reduced, and the reasoning speed is improved under the condition of maintaining the accuracy. Under the same conditions, compared with the original FCOS, the accuracy of the improved FCOS is improved by about 6% and the speed is improved by 3 FPS.
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Bi-LSTM Based on Attention Mechanism for Emotion Analysisture. Eventually, we used a softmax classifier to classify the emotion information. To manifest the benefit of the proposed architecture, we evaluate it on the TSCBE dataset constructed by our group. Our results can be indicative of that the model proposed in this work can achieve higher accuracy compared with baseline.
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