Search

Your search keyword '"Shen, Junge"' showing total 41 results
Start Over Author "Shen, Junge"
41 results on '"Shen, Junge"'

3. Concatenate and Shuffle Network: A Real-Time Underwater Object Detector for Small and Dense Objects

Author

Jiang, Xuyang, Mao, Zhaoyong, Shen, Junge, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Zhang, Junjie James, Series Editor, Wu, Meiping, editor, Niu, Yifeng, editor, Gu, Mancang, editor, and Cheng, Jin, editor

Published
2022
Full Text
View/download PDF

14. The Evolution of Research on Multimedia Travel Guide Search and Recommender Systems

Author

Shen, Junge, Cheng, Zhiyong, Shen, Jialie, Mei, Tao, Gao, Xinbo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gurrin, Cathal, editor, Hopfgartner, Frank, editor, Hurst, Wolfgang, editor, Johansen, Håvard, editor, Lee, Hyowon, editor, and O’Connor, Noel, editor

Published
2014
Full Text
View/download PDF

21. Bi-Unet: A Dual Stream Network for Real-Time Highway Surface Segmentation

Author

Sun, Jian, Shen, Junge, Wang, Xin, Mao, Zhaoyong, and Ren, Jing

Abstract

Highway surface segmentation consists of extracting road surface at pixel-level from the surveillance camera view. Since the intelligent traffic event detection task does not require the detection of off-road scene, the segmentation of highway surface is of great demand. However, it is challenging to accurately extract road surface in real time scenarios. To cope with the above issues, Bi-Unet, a dual stream lightweight network is proposed. Firstly, the dual stream structure enhances segmentation performance on the narrow remote end of the highway and preserves the detailed border information. Then, to perform real-time segmentation, a novel lightweight module (LSM) is introduced to lighten the model and provide higher segmentation accuracy. Moreover, to ensure road segmentation for complex scenes, a Road Attention Network (RAN) module is proposed. Lastly, due to the lack of a suitable benchmark dataset serve for the highway segmentation problem, a new large and high-quality segmentation dataset named Highway-Surface-Free (HSF) is proposed in this paper, which is collected from the perspective of highway surveillance cameras under all-day and all-weather conditions. Compared with the state of arts, the extensive experimental results verify that our proposed Bi-Unet achieves the best overall performance on our proposed HSF dataset.

Published
2023
Full Text
View/download PDF

38. Attraction recommendation: Towards personalized tourism via collective intelligence.

Author

Shen, Junge, Deng, Cheng, and Gao, Xinbo

Subjects
*TOURISM, *SWARM intelligence, *INFORMATION theory, *COMPUTER users, *DATA structures
Abstract

Travel recommendation systems can tackle the problem of information overload and recommend proper attractions on the basis of users׳ preferences. Most existing travel recommendation systems utilized travel history, yet neglected the low frequency of tourism and the flexible styles of attractions in different cities, which will cause the inaccuracy in both collaborative filtering recommendation and content-based recommendation. To deal with this issue, we propose a novel personalized travel recommendation framework by leveraging explicit user interaction and multi-modality travel information. As far as we known, it is the first time that attractions are recommended by user interaction and collective intelligence in a unified framework. Specifically, we first collect heterogeneous travel information by multi-user sharing, which is regarded as collective intelligence to provide reliable references by other travelers. Second, valuable knowledge is mined from collective intelligence in order to filter out the noisy data and make travel information structured. Then, personalized attraction similarity (PAS) model is designed to suggest attractions through fusing heterogeneous information with weighted adaptation and simultaneously considering explicit user interaction. Finally, context information such as the user׳s location is well adopted to refine the recommendation that may influence the user׳s choice at a particular moment. Experimental results on pseudo-relevance data and real-world data demonstrate that our method gains promising performance in terms of effectiveness as well as efficiency. [ABSTRACT FROM AUTHOR]

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results