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Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla

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发表于 2025-3-21 16:30:44 | 显示全部楼层 |阅读模式
书目名称Neural Information Processing
副标题26th International C
编辑Tom Gedeon,Kok Wai Wong,Minho Lee
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla
描述.The three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019..The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The second volume, LNCS 11954, is organized in topical sections on image processing by neural techniques; learning from incomplete data; model compression and optimisation; neural learning models; neural network applications; and social network computing..
出版日期Conference proceedings 2019
关键词artificial intelligence; classification; cognitive neurosciences; computational linguistics; computation
版次1
doihttps://doi.org/10.1007/978-3-030-36711-4
isbn_softcover978-3-030-36710-7
isbn_ebook978-3-030-36711-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

书目名称Neural Information Processing影响因子(影响力)




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书目名称Neural Information Processing网络公开度




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书目名称Neural Information Processing读者反馈学科排名




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发表于 2025-3-21 21:50:04 | 显示全部楼层
Multi-person 3D Pose Estimation from Monocular Image Sequencessidering the temporal smoothness. We evaluate our framework on the Human3.6M dataset and the multi-person image sequence captured by ourselves. The quantitative results on the Human3.6M dataset and the qualitative results on our constructed test data demonstrate the effectiveness of our proposed method.
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Shape Description and Retrieval in a Fused Scale Spacee are extracted across scales. Finally, shape retrieval is conducted by an integration of the retrieval results individually yielded at multiple scales. Experimental results on benchmark datasets validate the accuracy, efficiency and robustness of our proposed method.
发表于 2025-3-22 06:02:48 | 显示全部楼层
Gated Contiguous Memory U-Net for Single Image Dehazingcombine the features of different levels. We evaluate our proposed method using two public image dehazing benchmarks. The experiments demonstrate that our network can achieve a state-of-the-art performance when compared with other popular methods.
发表于 2025-3-22 12:44:28 | 显示全部楼层
Conference proceedings 2019 different domains. The second volume, LNCS 11954, is organized in topical sections on image processing by neural techniques; learning from incomplete data; model compression and optimisation; neural learning models; neural network applications; and social network computing..
发表于 2025-3-22 13:41:13 | 显示全部楼层
Conference proceedings 2019g, ICONIP 2019, held in Sydney, Australia, in December 2019..The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across
发表于 2025-3-22 18:12:38 | 显示全部楼层
0302-9743 Processing, ICONIP 2019, held in Sydney, Australia, in December 2019..The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniq
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发表于 2025-3-23 04:46:03 | 显示全部楼层
STNet: A Style Transformation Network for Deep Image Steganographyarbitrary size with 0.06 bit per pixel, improving over other deep steganographic models which only can embed fixed-length secret. Experiment results demonstrate that our STNet can achieve great visual effect, security, and reliability.
发表于 2025-3-23 08:52:49 | 显示全部楼层
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