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31 results on '"Xiong, Junjun"'

1. Study on Vibration and Temperature Aging Characteristics of Fiber-Optic Current Transformer

Author

Mu, Xiaojing, Liu, Yong, Xiong, Junjun, Liu, Bin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2025
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4. Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting

Author

Liu, Xiyang, Yang, Jie, Ding, Wenrui, Wang, Tieqiang, Wang, Zhijin, Xiong, Junjun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vedaldi, Andrea, editor, Bischof, Horst, editor, Brox, Thomas, editor, and Frahm, Jan-Michael, editor

Published
2020
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5. Deep Learning with S-shaped Rectified Linear Activation Units

Author

Jin, Xiaojie, Xu, Chunyan, Feng, Jiashi, Wei, Yunchao, Xiong, Junjun, and Yan, Shuicheng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions., Comment: Accepted by AAAI-16

Published
2015

8. Pairing Contour Fragments for Object Recognition

Author

Zheng, Wei, Zhang, Qian, Li, Zhixuan, Xiong, Junjun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tian, Qi, editor, Sebe, Nicu, editor, Qi, Guo-Jun, editor, Huet, Benoit, editor, Hong, Richang, editor, and Liu, Xueliang, editor

Published
2016
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20. Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting

Author

Wang Zhijin, Tieqiang Wang, Xiong Junjun, Wenrui Ding, Yang Jie, and Xiyang Liu

Subjects
Source code, Computer science, business.industry, media_common.quotation_subject, Frame (networking), Context (language use), Pattern recognition, Interval (mathematics), Regression, Image (mathematics), Task (computing), Artificial intelligence, business, Focus (optics), media_common
Abstract

The crowd counting task aims at estimating the number of people located in an image or a frame from videos. Existing methods widely adopt density maps as the training targets to optimize the point-to-point loss. While in testing phase, we only focus on the differences between the crowd numbers and the global summation of density maps, which indicate the inconsistency between the training targets and the evaluation criteria. To solve this problem, we introduce a new target, named local counting map (LCM), to obtain more accurate results than density map based approaches. Moreover, we also propose an adaptive mixture regression framework with three modules in a coarse-to-fine manner to further improve the precision of the crowd estimation: scale-aware module (SAM), mixture regression module (MRM) and adaptive soft interval module (ASIM). Specifically, SAM fully utilizes the context and multi-scale information from different convolutional features; MRM and ASIM perform more precise counting regression on local patches of images. Compared with current methods, the proposed method reports better performances on the typical datasets. The source code is available at https://github.com/xiyang1012/Local-Crowd-Counting.

Published
2020
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