Zobrazeno 1 - 10 of 14 pro vyhledávání: '"Weiqun Wu"'
1
Akademický článek
A New Convolutional Neural Network-Based Steganalysis Method for Content-Adaptive Image Steganography in the Spatial Domain
Autor: Zhili Xiang, Jun Sang, Qian Zhang, Bin Cai, Xiaofeng Xia, Weiqun Wu
Publikováno v: IEEE Access, Vol 8, Pp 47013-47020 (2020)
Convolutional neural network-based methods are attracting increasing attention in steganalysis. However, steganalysis for content-adaptive image steganography in the spatial domain is still a difficult problem. In this paper, a new convolutional neur
Externí odkaz: https://doaj.org/article/c4efd17e6d204c46a787da34b5e25fa9
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2
Akademický článek
Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network
Autor: Jun Sang, Weiqun Wu, Hongling Luo, Hong Xiang, Qian Zhang, Haibo Hu, Xiaofeng Xia
Publikováno v: IEEE Access, Vol 7, Pp 24411-24419 (2019)
Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion. To overcome such problems, scale-adaptive convolutional neural network (SaCNN)
Externí odkaz: https://doaj.org/article/88ccbc57398d4b0693947363c16d346e
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3
Akademický článek
A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
Autor: Hongling Luo, Jun Sang, Weiqun Wu, Hong Xiang, Zhili Xiang, Qian Zhang, Zhongyuan Wu
Publikováno v: Applied Sciences, Vol 8, Iss 12, p 2367 (2018)
In recent years, the trampling events due to overcrowding have occurred frequently, which leads to the demand for crowd counting under a high-density environment. At present, there are few studies on monitoring crowds in a large-scale crowded environ
Externí odkaz: https://doaj.org/article/94f50a2238bc4fbcbcad341ee7a932c1
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5
Density-aware and background-aware network for crowd counting via multi-task learning
Autor: Weiqun Wu, Xinyue Liu, Kai Liu, Jun Sang, Xiaofeng Xia, Qi Liu
Publikováno v: Pattern Recognition Letters. 150:221-227
In this paper, we propose a density-aware and background-aware network via multi-task learning (MTL-DB) for crowd counting. It aims to enable the model to capture the high-level semantic information of density and background via multi-task joint trai
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3776fd93c6462978079e4b2fef8d36ef
https://doi.org/10.1016/j.patrec.2021.07.013
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6
A New Convolutional Neural Network-Based Steganalysis Method for Content-Adaptive Image Steganography in the Spatial Domain
Autor: Jun Sang, Bin Cai, Xiaofeng Xia, Weiqun Wu, Qian Zhang, Zhili Xiang
Publikováno v: IEEE Access, Vol 8, Pp 47013-47020 (2020)
Convolutional neural network-based methods are attracting increasing attention in steganalysis. However, steganalysis for content-adaptive image steganography in the spatial domain is still a difficult problem. In this paper, a new convolutional neur
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0ea45eeaee77c27aa7f2aed105445f5
https://ieeexplore.ieee.org/document/9026970/
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8
Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network
Autor: Qian Zhang, Haibo Hu, Weiqun Wu, Jun Sang, Hong Xiang, Hongling Luo, Xiaofeng Xia
Publikováno v: IEEE Access, Vol 7, Pp 24411-24419 (2019)
Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion. To overcome such problems, scale-adaptive convolutional neural network (SaCNN)
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ec75a568ad50e34e4bf8389c779f17e
https://ieeexplore.ieee.org/document/8643345/
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9
Weight-dependent Gates for Network Pruning
Autor: Yun Li, Zechun Liu, Weiqun Wu, Haotian Yao, Xiangyu Zhang, Chi Zhang, Baoqun Yin
In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint. This paper argues that the pruning decision should depend on the convol
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::418da6633d7eef88cf65cef44ceb19aa
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10
Weight-Dependent Gates for Differentiable Neural Network Pruning
Autor: Weiqun Wu, Yun Li, Baoqun Yin, Chi Zhang, Haotian Yao, Xiangyu Zhang, Zechun Liu
Publikováno v: Computer Vision – ECCV 2020 Workshops ISBN: 9783030682378
ECCV Workshops (5)
In this paper, we propose a simple and effective network pruning framework, which introduces novel weight-dependent gates to prune filter adaptively. We argue that the pruning decision should depend on the convolutional weights, in other words, it sh
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d823caf198f065b4581953f0ba13fb9d
https://doi.org/10.1007/978-3-030-68238-5_3
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