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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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书目名称Neural Information Processing
副标题26th International C
编辑Tom Gedeon,Kok Wai Wong,Minho Lee
视频videohttp://file.papertrans.cn/664/663641/663641.mp4
丛书名称Communications in Computer and Information 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 two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019..For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models..
出版日期Conference proceedings 2019
关键词artificial intelligence; computer networks; computer systems; computer vision; deep learning; education; e
版次1
doihttps://doi.org/10.1007/978-3-030-36808-1
isbn_softcover978-3-030-36807-4
isbn_ebook978-3-030-36808-1Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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A Deep Learning Scheme for Extracting Pedestrian-Parcel Tuples from Videosy re-identification of pedestrians and parcels. In the interaction module, we propose a lightweight interaction model for discriminating the affiliation between pedestrians and parcels in a single RGB image. Experiments on a video data at a subway entrance validate the proposed approach.
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Fault Tolerant Broad Learning Systemtolerant BLS (FTBLS). First, we develop a fault tolerant objective function for BLS. Based on the developed objective function, we develop a training algorithm to construct a BLS network. The simulation results show that our proposed FTBLS is much better than the classical BLS.
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