Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Dou Quan"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 8003-8019 (2024)
With the development of deep learning technology, the application of convolutional neural network (CNN) and vision transformer (ViT) for polarimetric synthetic aperture radar (PolSAR) image classification has been deepened. However, the PolSAR image
Externí odkaz:
https://doaj.org/article/db30d2a4cf2a40988296d35d5eb95d95
Publikováno v:
Remote Sensing, Vol 16, Iss 22, p 4280 (2024)
Deep learning methods have shown significant advantages in polarimetric synthetic aperture radar (PolSAR) image classification. However, their performances rely on a large number of labeled data. To alleviate this problem, this paper proposes a PolSA
Externí odkaz:
https://doaj.org/article/15811c15a45a400cbbd97d17e6cbe2de
Autor:
Licheng Jiao, Zhongjian Huang, Xiaoqiang Lu, Xu Liu, Yuting Yang, Jiaxuan Zhao, Jinyue Zhang, Biao Hou, Shuyuan Yang, Fang Liu, Wenping Ma, Lingling Li, Xiangrong Zhang, Puhua Chen, Zhixi Feng, Xu Tang, Yuwei Guo, Dou Quan, Shuang Wang, Weibin Li, Jing Bai, Yangyang Li, Ronghua Shang, Jie Feng
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 10084-10120 (2023)
The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a large dataset and can be efficiently adapted to various downstream applic
Externí odkaz:
https://doaj.org/article/1225f33b10d14949bfa7995f7c951676
Autor:
Licheng Jiao, Xin Zhang, Xu Liu, Fang Liu, Shuyuan Yang, Wenping Ma, Lingling Li, Puhua Chen, Zhixi Feng, Yuwei Guo, Xu Tang, Biao Hou, Xiangrong Zhang, Jing Bai, Dou Quan, Junpeng Zhang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 1-45 (2023)
Transformer has shown excellent performance in remote sensing field with long-range modeling capabilities. Remote sensing video (RSV) moving object detection and tracking play indispensable roles in military activities as well as urban monitoring. Ho
Externí odkaz:
https://doaj.org/article/6c32c77b7fdb454e8ac081b0ca96d781
Autor:
Dou Quan, Huiyuan Wei, Shuang Wang, Yi Li, Jocelyn Chanussot, Yanhe Guo, Biao Hou, Licheng Jiao
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 4739-4754 (2023)
Deep convolutional networks are powerful for local feature learning and have shown advantages in image matching and registration. However, the significant differences between cross-modal images increase the challenge of image registration. The deep n
Externí odkaz:
https://doaj.org/article/3b77c662ffc14c939c5566a54f0fd54d
Autor:
Licheng Jiao, Zhongjian Huang, Xu Liu, Yuting Yang, Mengru Ma, Jiaxuan Zhao, Chao You, Biao Hou, Shuyuan Yang, Fang Liu, Wenping Ma, Lingling Li, Puhua Chen, Zhixi Feng, Xu Tang, Yuwei Guo, Xiangrong Zhang, Dou Quan, Shuang Wang, Weibin Li, Jing Bai, Yangyang Li, Ronghua Shang, Jie Feng
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 2992-3033 (2023)
Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This article summarizes and analyzes the essen
Externí odkaz:
https://doaj.org/article/d72a341db72f4e2d8b0df418aab625b4
Publikováno v:
Frontiers in Neurorobotics, Vol 17 (2023)
Speech emotion recognition is challenging due to the subjectivity and ambiguity of emotion. In recent years, multimodal methods for speech emotion recognition have achieved promising results. However, due to the heterogeneity of data from different m
Externí odkaz:
https://doaj.org/article/024476a0deac48198766139ec68fbd57
Autor:
Lyu, Jun, Qin, Chen, Wang, Shuo, Wang, Fanwen, Li, Yan, Wang, Zi, Guo, Kunyuan, Ouyang, Cheng, Tänzer, Michael, Liu, Meng, Sun, Longyu, Sun, Mengting, Li, Qin, Shi, Zhang, Hua, Sha, Li, Hao, Chen, Zhensen, Zhang, Zhenlin, Xin, Bingyu, Metaxas, Dimitris N., Yiasemis, George, Teuwen, Jonas, Zhang, Liping, Chen, Weitian, Zhao, Yidong, Tao, Qian, Pang, Yanwei, Liu, Xiaohan, Razumov, Artem, Dylov, Dmitry V., Dou, Quan, Yan, Kang, Xue, Yuyang, Du, Yuning, Dietlmeier, Julia, Garcia-Cabrera, Carles, Hemidi, Ziad Al-Haj, Vogt, Nora, Xu, Ziqiang, Zhang, Yajing, Chu, Ying-Hua, Chen, Weibo, Bai, Wenjia, Zhuang, Xiahai, Qin, Jing, Wu, Lianmin, Yang, Guang, Qu, Xiaobo, Wang, He, Wang, Chengyan
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and e
Externí odkaz:
http://arxiv.org/abs/2404.01082
Autor:
DOU Quan-qi
Publikováno v:
Gong-kuang zidonghua, Vol 37, Iss 12, Pp 95-97 (2011)
In view of defect that location way of sampling head of traditional bridge-type train coal sampler is inaccuracy, an automatic location system of sampling head of bridge-type train coal sampler was designed based on PLC and shaft encoder according to
Externí odkaz:
https://doaj.org/article/e0de101a40414c0dbc1fafb1ae58960e
Autor:
Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Datwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gomez, Catalina, Arbelaez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuan-han, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Lin-min, AK, Murat, Rosas-Gonzalez, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh H., Nyholm, Tufve, Lofstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-Andre, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko1, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 1 (2022)
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e
Externí odkaz:
http://arxiv.org/abs/2112.10074