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318 results on '"Fu, Zhihong"'

2. SparseTT: Visual Tracking with Sparse Transformers

Author

Fu, Zhihong, Fu, Zehua, Liu, Qingjie, Cai, Wenrui, and Wang, Yunhong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking. Furthermore, we introduce a double-head predictor to boost the accuracy of foreground-background classification and regression of target bounding boxes, which further improve the tracking performance. Extensive experiments show that, without bells and whistles, our method significantly outperforms the state-of-the-art approaches on LaSOT, GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the training time of our method is reduced by 75% compared to that of TransT. The source code and models are available at https://github.com/fzh0917/SparseTT., Comment: Accepted by IJCAI2022 as a long oral presentation

Published
2022

3. A numerical study on thermal bonding with preheating technique for polypropylene microfluidic device

Author

Almezgagi Esam Abdulrahman, Fu Zhihong, Huang Gongjian, and Zhang Xianyue

Subjects
thermal bonding, microfluidic, numerical simulation, preheated bonding area, optimizing bonding parameters, Polymers and polymer manufacture, TP1080-1185
Abstract

This study presents a method for producing microfluidic chips from polypropylene using a pre-heated bonding area and thermal bonding technology. ANSYS was utilized to investigate the effects of bonding parameters and microchannel deformation and predict the bonding strength. Results show that careful control of these parameters is critical to achieve a strong and durable bond between the microfluidic chip layers. Higher bonding temperatures were found to lead to greater microchannel deformation, with deformation increasing significantly, as the temperature approached the material’s melting point. Increased bonding pressure after 1 MPa and a time of 300 s also led to greater microchannel deformation. The study’s analysis of stresses revealed that the maximum principle compressive stress on the edges of the bonding area increased significantly with pressure. Tensile testing showed that bonding strength was near failure at a tensile force of 5,500 N, indicating a bonding strength close to 1.5 MPa.

Published
2023
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4. STMTrack: Template-free Visual Tracking with Space-time Memory Networks

Author

Fu, Zhihong, Liu, Qingjie, Fu, Zehua, and Wang, Yunhong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time tracking and practical applications. In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame. Furthermore, the pixel-level similarity computation of the memory network enables our tracker to generate much more accurate bounding boxes of the target. Extensive experiments and comparisons with many competitive trackers on challenging large-scale benchmarks, OTB-2015, TrackingNet, GOT-10k, LaSOT, UAV123, and VOT2018, show that, without bells and whistles, our tracker outperforms all previous state-of-the-art real-time methods while running at 37 FPS. The code is available at https://github.com/fzh0917/STMTrack., Comment: Accepted by CVPR 2021

Published
2021

5. HiFace: Hybrid Task Learning for Face Reconstruction from Single Image

Author

Xu, Wei, Fu, Zhihong, Chen, Zhixing, Deng, Qili, Fu, Mingtao, Zhang, Xijin, Gao, Yuan, Du, Daniel K., Zheng, Min, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Karlinsky, Leonid, editor, Michaeli, Tomer, editor, and Nishino, Ko, editor

Published
2023
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6. The Tenth Visual Object Tracking VOT2022 Challenge Results

Author

Kristan, Matej, Leonardis, Aleš, Matas, Jiří, Felsberg, Michael, Pflugfelder, Roman, Kämäräinen, Joni-Kristian, Chang, Hyung Jin, Danelljan, Martin, Zajc, Luka Čehovin, Lukežič, Alan, Drbohlav, Ondrej, Björklund, Johanna, Zhang, Yushan, Zhang, Zhongqun, Yan, Song, Yang, Wenyan, Cai, Dingding, Mayer, Christoph, Fernández, Gustavo, Ben, Kang, Bhat, Goutam, Chang, Hong, Chen, Guangqi, Chen, Jiaye, Chen, Shengyong, Chen, Xilin, Chen, Xin, Chen, Xiuyi, Chen, Yiwei, Chen, Yu-Hsi, Chen, Zhixing, Cheng, Yangming, Ciaramella, Angelo, Cui, Yutao, Džubur, Benjamin, Dasari, Mohana Murali, Deng, Qili, Dhar, Debajyoti, Di, Shangzhe, Nardo, Emanuel Di, Du, Daniel K., Dunnhofer, Matteo, Fan, Heng, Feng, Zhenhua, Fu, Zhihong, Gao, Shang, Gorthi, Rama Krishna, Granger, Eric, Gu, Q. H., Gupta, Himanshu, He, Jianfeng, He, Keji, Huang, Yan, Jangid, Deepak, Ji, Rongrong, Jiang, Cheng, Jiang, Yingjie, Lawin, Felix Järemo, Kang, Ze, Kiran, Madhu, Kittler, Josef, Lai, Simiao, Lan, Xiangyuan, Lee, Dongwook, Lee, Hyunjeong, Lee, Seohyung, Li, Hui, Li, Ming, Li, Wangkai, Li, Xi, Li, Xianxian, Li, Xiao, Li, Zhe, Lin, Liting, Ling, Haibin, Liu, Bo, Liu, Chang, Liu, Si, Lu, Huchuan, Cruz, Rafael M. O., Ma, Bingpeng, Ma, Chao, Ma, Jie, Ma, Yinchao, Martinel, Niki, Memarmoghadam, Alireza, Micheloni, Christian, Moallem, Payman, Nguyen-Meidine, Le Thanh, Pan, Siyang, Park, ChangBeom, Paudel, Danda, Paul, Matthieu, Peng, Houwen, Robinson, Andreas, Rout, Litu, Shan, Shiguang, Simonato, Kristian, Song, Tianhui, Song, Xiaoning, Sun, Chao, Sun, Jingna, Tang, Zhangyong, Timofte, Radu, Tsai, Chi-Yi, Gool, Luc Van, Verma, Om Prakash, Wang, Dong, Wang, Fei, Wang, Liang, Wang, Liangliang, Wang, Lijun, Wang, Limin, Wang, Qiang, Wu, Gangshan, Wu, Jinlin, Wu, Xiaojun, Xie, Fei, Xu, Tianyang, Xu, Wei, Xu, Yong, Xu, Yuanyou, Xue, Wanli, Xun, Zizheng, Yan, Bin, Yang, Dawei, Yang, Jinyu, Yang, Wankou, Yang, Xiaoyun, Yang, Yi, Yang, Yichun, Yang, Zongxin, Ye, Botao, Yu, Fisher, Yu, Hongyuan, Yu, Jiaqian, Yu, Qianjin, Yu, Weichen, Ze, Kang, Zhai, Jiang, Zhang, Chengwei, Zhang, Chunhu, Zhang, Kaihua, Zhang, Tianzhu, Zhang, Wenkang, Zhang, Zhibin, Zhang, Zhipeng, Zhao, Jie, Zhao, Shaochuan, Zheng, Feng, Zheng, Haixia, Zheng, Min, Zhong, Bineng, Zhu, Jiawen, Zhu, Xuefeng, Zhuang, Yueting, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Karlinsky, Leonid, editor, Michaeli, Tomer, editor, and Nishino, Ko, editor

Published
2023
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16. Experimental Study on Bending Stress of Spur Gear with Tooth Profile Modification and Finite Element Verification

Author

Chen Xueshi, Tang Jinyuan, Chen Siyu, and Fu Zhihong

Subjects
Gear, Modification, Bending stress, Measurement, Finite element, Mechanical engineering and machinery, TJ1-1570
Abstract

The study of static and dynamic meshing characteristics of gears has high theoretical value for improving gear carrying capacity and dynamics performance. The influence of tooth profile modification on bending stress is studied by experiment and finite element. The standard FZG gear drive test rig is used to measure the bending stress of the tooth root,and carbon slip rings is used to measure the bending stress of the tooth root dynamically. The main conclusions from the test and finite element analysis are as follow: the tooth profile modification can obviously reduce the tooth bending stress of the gear pair during the gear transmission,the higher the rotational speed of the gear pair,the more obvious the improvement of the bending stress of the tooth profile modification.

Published
2018
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18. Amelioration of intestinal barrier function and reduction of blood lead level in adult women with recurrent spontaneous abortion by a novel product of dietary fiber mixture, Holofood

Author

Tian, Ye, primary, Pan, Zhiyuan, additional, Lan, Liling, additional, Chang, Yuxiao, additional, Zhao, Ting, additional, Fu, Zhihong, additional, Wu, Shuhua, additional, Deng, Tianqin, additional, Cao, Meilan, additional, Wang, Weizhou, additional, Bi, Yujing, additional, Yang, Ruifu, additional, Yang Lee, B. J., additional, and Liu, Qingzhi, additional

Published
2023
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37. The Ninth Visual Object Tracking VOT2021 Challenge Results

Author

Kristan, Matej, primary, Matas, Jiri, additional, Leonardis, Ales, additional, Felsberg, Michael, additional, Pflugfelder, Roman, additional, Kamarainen, Joni-Kristian, additional, Chang, Hyung Jin, additional, Danelljan, Martin, additional, Zajc, Luka Cehovin, additional, Lukezic, Alan, additional, Drbohlav, Ondrej, additional, Kapyla, Jani, additional, Hager, Gustav, additional, Yan, Song, additional, Yang, Jinyu, additional, Zhang, Zhongqun, additional, Fernandez, Gustavo, additional, Abdelpakey, Mohamed, additional, Bhat, Goutam, additional, Cerkezi, Llukman, additional, Cevikalp, Hakan, additional, Chen, Shengyong, additional, Chen, Xin, additional, Cheng, Miao, additional, Cheng, Ziyi, additional, Chiu, Yu-Chen, additional, Cirakman, Ozgun, additional, Cui, Yutao, additional, Dai, Kenan, additional, Dasari, Mohana Murali, additional, Deng, Qili, additional, Dong, Xingping, additional, Du, Daniel K., additional, Dunnhofer, Matteo, additional, Feng, Zhen-Hua, additional, Feng, Zhiyong, additional, Fu, Zhihong, additional, Ge, Shiming, additional, Gorthi, Rama Krishna, additional, Gu, Yuzhang, additional, Gunsel, Bilge, additional, Guo, Qing, additional, Gurkan, Filiz, additional, Han, Wencheng, additional, Huang, Yanyan, additional, Lawin, Felix Jaremo, additional, Jhang, Shang-Jhih, additional, Ji, Rongrong, additional, Jiang, Cheng, additional, Jiang, Yingjie, additional, Juefei-Xu, Felix, additional, Jun, Yin, additional, Ke, Xiao, additional, Khan, Fahad Shahbaz, additional, Hak Kim, Byeong, additional, Kittler, Josef, additional, Lan, Xiangyuan, additional, Lee, Jun Ha, additional, Leibe, Bastian, additional, Li, Hui, additional, Li, Jianhua, additional, Li, Xianxian, additional, Li, Yuezhou, additional, Liu, Bo, additional, Liu, Chang, additional, Liu, Jingen, additional, Liu, Li, additional, Liu, Qingjie, additional, Lu, Huchuan, additional, Lu, Wei, additional, Luiten, Jonathon, additional, Ma, Jie, additional, Ma, Ziang, additional, Martinel, Niki, additional, Mayer, Christoph, additional, Memarmoghadam, Alireza, additional, Micheloni, Christian, additional, Niu, Yuzhen, additional, Paudel, Danda, additional, Peng, Houwen, additional, Qiu, Shoumeng, additional, Rajiv, Aravindh, additional, Rana, Muhammad, additional, Robinson, Andreas, additional, Saribas, Hasan, additional, Shao, Ling, additional, Shehata, Mohamed, additional, Shen, Furao, additional, Shen, Jianbing, additional, Simonato, Kristian, additional, Song, Xiaoning, additional, Tang, Zhangyong, additional, Timofte, Radu, additional, Torr, Philip, additional, Tsai, Chi-Yi, additional, Uzun, Bedirhan, additional, Van Gool, Luc, additional, Voigtlaender, Paul, additional, Wang, Dong, additional, Wang, Guangting, additional, Wang, Liangliang, additional, Wang, Lijun, additional, Wang, Limin, additional, Wang, Linyuan, additional, Wang, Yong, additional, Wang, Yunhong, additional, Wu, Chenyan, additional, Wu, Gangshan, additional, Wu, Xiao-Jun, additional, Xie, Fei, additional, Xu, Tianyang, additional, Xu, Xiang, additional, Xue, Wanli, additional, Yan, Bin, additional, Yang, Wankou, additional, Yang, Xiaoyun, additional, Ye, Yu, additional, Yin, Jun, additional, Zhang, Chengwei, additional, Zhang, Chunhui, additional, Zhang, Haitao, additional, Zhang, Kaihua, additional, Zhang, Kangkai, additional, Zhang, Xiaohan, additional, Zhang, Xiaolin, additional, Zhang, Xinyu, additional, Zhang, Zhibin, additional, Zhao, Shaochuan, additional, Zhen, Ming, additional, Zhong, Bineng, additional, Zhu, Jiawen, additional, and Zhu, Xue-Feng, additional

Published
2021
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40. The Ninth Visual Object Tracking VOT2021 Challenge Results

Author

Kristan, Matej, Matas, Jiri, Leonardis, Ales, Felsberg, Michael, Pflugfelder, Roman, Kamarainen, Joni-Kristian, Chang, Hyung Jin, Danelljan, Martin, Zajc, Luka Cehovin, Lukezic, Alan, Drbohlav, Ondrej, Kapyla, Jani, Häger, Gustav, Yan, Song, Yang, Jinyu, Zhang, Zhongqun, Fernandez, Gustavo, Abdelpakey, Mohamed, Bhat, Goutam, Cerkezi, Llukman, Cevikalp, Hakan, Chen, Shengyong, Chen, Xin, Cheng, Miao, Cheng, Ziyi, Chiu, Yu-Chen, Cirakman, Ozgun, Cui, Yutao, Dai, Kenan, Dasari, Mohana Murali, Deng, Qili, Dong, Xingping, Du, Daniel K., Dunnhofer, Matteo, Feng, Zhen-Hua, Feng, Zhiyong, Fu, Zhihong, Ge, Shiming, Gorthi, Rama Krishna, Gu, Yuzhang, Gunsel, Bilge, Guo, Qing, Gurkan, Filiz, Han, Wencheng, Huang, Yanyan, Järemo-Lawin, Felix, Jhang, Shang-Jhih, Ji, Rongrong, Jiang, Cheng, Jiang, Yingjie, Juefei-Xu, Felix, Jun, Yin, Ke, Xiao, Khan, Fahad Shahbaz, Kim, Byeong Hak, Kittler, Josef, Lan, Xiangyuan, Lee, Jun Ha, Leibe, Bastian, Li, Hui, Li, Jianhua, Li, Xianxian, Li, Yuezhou, Liu, Bo, Liu, Chang, Liu, Jingen, Liu, Li, Liu, Qingjie, Lu, Huchuan, Lu, Wei, Luiten, Jonathon, Ma, Jie, Ma, Ziang, Martinel, Niki, Mayer, Christoph, Memarmoghadam, Alireza, Micheloni, Christian, Niu, Yuzhen, Paudel, Danda, Peng, Houwen, Qiu, Shoumeng, Rajiv, Aravindh, Rana, Muhammad, Robinson, Andreas, Saribas, Hasan, Shao, Ling, Shehata, Mohamed, Shen, Furao, Shen, Jianbing, Simonato, Kristian, Song, Xiaoning, Tang, Zhangyong, Timofte, Radu, Torr, Philip, Tsai, Chi-Yi, Uzun, Bedirhan, Van Gool, Luc, Voigtlaender, Paul, Wang, Dong, Wang, Guangting, Wang, Liangliang, Wang, Lijun, Wang, Limin, Wang, Linyuan, Wang, Yong, Wang, Yunhong, Wu, Chenyan, Wu, Gangshan, Wu, Xiao-Jun, Xie, Fei, Xu, Tianyang, Xu, Xiang, Xue, Wanli, Yan, Bin, Yang, Wankou, Yang, Xiaoyun, Ye, Yu, Yin, Jun, Zhang, Chengwei, Zhang, Chunhui, Zhang, Haitao, Zhang, Kaihua, Zhang, Kangkai, Zhang, Xiaohan, Zhang, Xiaolin, Zhang, Xinyu, Zhang, Zhibin, Zhao, Shaochuan, Zhen, Ming, Zhong, Bineng, Zhu, Jiawen, Zhu, Xue-Feng, Kristan, Matej, Matas, Jiri, Leonardis, Ales, Felsberg, Michael, Pflugfelder, Roman, Kamarainen, Joni-Kristian, Chang, Hyung Jin, Danelljan, Martin, Zajc, Luka Cehovin, Lukezic, Alan, Drbohlav, Ondrej, Kapyla, Jani, Häger, Gustav, Yan, Song, Yang, Jinyu, Zhang, Zhongqun, Fernandez, Gustavo, Abdelpakey, Mohamed, Bhat, Goutam, Cerkezi, Llukman, Cevikalp, Hakan, Chen, Shengyong, Chen, Xin, Cheng, Miao, Cheng, Ziyi, Chiu, Yu-Chen, Cirakman, Ozgun, Cui, Yutao, Dai, Kenan, Dasari, Mohana Murali, Deng, Qili, Dong, Xingping, Du, Daniel K., Dunnhofer, Matteo, Feng, Zhen-Hua, Feng, Zhiyong, Fu, Zhihong, Ge, Shiming, Gorthi, Rama Krishna, Gu, Yuzhang, Gunsel, Bilge, Guo, Qing, Gurkan, Filiz, Han, Wencheng, Huang, Yanyan, Järemo-Lawin, Felix, Jhang, Shang-Jhih, Ji, Rongrong, Jiang, Cheng, Jiang, Yingjie, Juefei-Xu, Felix, Jun, Yin, Ke, Xiao, Khan, Fahad Shahbaz, Kim, Byeong Hak, Kittler, Josef, Lan, Xiangyuan, Lee, Jun Ha, Leibe, Bastian, Li, Hui, Li, Jianhua, Li, Xianxian, Li, Yuezhou, Liu, Bo, Liu, Chang, Liu, Jingen, Liu, Li, Liu, Qingjie, Lu, Huchuan, Lu, Wei, Luiten, Jonathon, Ma, Jie, Ma, Ziang, Martinel, Niki, Mayer, Christoph, Memarmoghadam, Alireza, Micheloni, Christian, Niu, Yuzhen, Paudel, Danda, Peng, Houwen, Qiu, Shoumeng, Rajiv, Aravindh, Rana, Muhammad, Robinson, Andreas, Saribas, Hasan, Shao, Ling, Shehata, Mohamed, Shen, Furao, Shen, Jianbing, Simonato, Kristian, Song, Xiaoning, Tang, Zhangyong, Timofte, Radu, Torr, Philip, Tsai, Chi-Yi, Uzun, Bedirhan, Van Gool, Luc, Voigtlaender, Paul, Wang, Dong, Wang, Guangting, Wang, Liangliang, Wang, Lijun, Wang, Limin, Wang, Linyuan, Wang, Yong, Wang, Yunhong, Wu, Chenyan, Wu, Gangshan, Wu, Xiao-Jun, Xie, Fei, Xu, Tianyang, Xu, Xiang, Xue, Wanli, Yan, Bin, Yang, Wankou, Yang, Xiaoyun, Ye, Yu, Yin, Jun, Zhang, Chengwei, Zhang, Chunhui, Zhang, Haitao, Zhang, Kaihua, Zhang, Kangkai, Zhang, Xiaohan, Zhang, Xiaolin, Zhang, Xinyu, Zhang, Zhibin, Zhao, Shaochuan, Zhen, Ming, Zhong, Bineng, Zhu, Jiawen, and Zhu, Xue-Feng

Abstract

The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).

Published
2021
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46. A rapid imaging method of the seismic back‐scattered wavefield for urban road near‐surface anomalous structures.

Author

Lu, Xinglin, Hu, Xuquan, Luo, Chaopeng, Xu, Zhengyu, Liao, Xian, Liu, Longhuan, and Fu, Zhihong

Subjects
GROUND penetrating radar, IMAGING systems in seismology, SEISMIC reflection method, RAYLEIGH waves, PRINCIPAL components analysis, SEISMIC wave velocity, TRACKING radar
Abstract

Urban road near‐surface anomalous structures are buried at shallow depths (≦20 m) and small scale. Current geophysical methods, such as the transient electromagnetic, ground‐penetrating radar and shallow seismic methods with reflection wave, refraction wave and Rayleigh surface wave, do not provide sufficient detection accuracy for near‐surface anomalous structures. Although the back‐scattered method is promising in this task, it still needs improvement. This paper proposes an efficient near‐surface seismic back‐scattered method to realize the rapid lateral imaging of urban road anomalous structures. The proposed method uses a principal component analysis algorithm to directly extract the anomalous structure information. It does not require the knowledge of parameters such as velocity or the seismic wavelet so that the solutions are not affected by these parameters. Moreover, the proposed method retains the high transverse resolution of the back‐scattered wavefield and does not lose the wavefield information. Simulation and experimental operation show that the proposed method is very efficient. Combined with the hardware equipment provided by the towed seismograph, the proposed method can realize rapid detection and data imaging, which has a high engineering application value. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Research on Corrosion Detection Method of Tower Grounding Body Based on Transient Electromagnetic Method

Author

Jia Li, Linshan Zhang, Xiangyu Tan, Zhou Nianrong, Li Bo, Fu Zhihong, and Xiaobin Xu

Subjects
Electromagnetics, Materials science, business.industry, Soil resistivity, Structural engineering, Transient (oscillation), business, Electrical conductor, Tower, Corrosion, Transmission tower, Electromagnetic induction
Abstract

The grounding body of the transmission tower is of great significance to the safe operation of the power system, its corrosion state needs to be accurately detected to ensure a safe discharge channel for lightning current. In this paper, transient electromagnetic method is employed to detect the corrosion of tower grounding body. Based on the distribution characteristics of magnetic induction intensity, the location of grounding conductor with corrosion or breakpoint can be accurately located. The model of tower grounding body is established by COMSOL multiphasic software, and the influence of different corrosion degree, conductor material and soil resistivity on magnetic induction intensity is analyzed in detail. Field experiments are carried out to verify the effectiveness of the method.

Published
2020

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