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340 results on '"Yang, Gongping"'

53. Fast Iris Localization Based on Improved Hough Transform

Author

Wang, Lu, Yang, Gongping, Yin, Yilong, 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, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Yu, Jian, editor, Greco, Salvatore, editor, Lingras, Pawan, editor, Wang, Guoyin, editor, and Skowron, Andrzej, editor

Published
2010
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57. The Communication Model of Migrating Workflow System

Author

Lu, Zhaoxia, Liu, Dongming, Zeng, Guangzhou, Yang, Gongping, 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Shi, Zhong-Zhi, editor, and Sadananda, Ramakoti, editor

Published
2006
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72. Enhancing Multiscale Representations With Transformer for Remote Sensing Image Semantic Segmentation

Author

Xiao, Tao, Liu, Yikun, Huang, Yuwen, Li, Mingsong, and Yang, Gongping

Abstract

Semantic segmentation is an extremely challenging task in high-resolution remote sensing (HRRS) images as objects have complex spatial layouts and enormous variations in appearance. Convolutional neural networks (CNNs) have an excellent ability to extract local features and have been widely applied as a feature extractor for various vision tasks. However, due to the inherent inductive bias of convolution operation, CNNs inevitably have limitations in modeling long-range dependencies. Transformer can capture global representations well but unfortunately ignores the details of local features and has high computational and spatial complexity in processing high-resolution feature maps. In this article, we propose a novel hybrid architecture for HRRS image segmentation, termed Enhancing Multiscale Representations with Transformer (EMRT), to exploit the advantages of convolution operations and Transformer to enhance multiscale representation learning. We incorporate the deformable self-attention mechanism in the Transformer to automatically adjust the receptive field and design an encoder–decoder architecture accordingly to achieve efficient context modeling. Specifically, CNN is constructed to extract feature representations. In the encoder, local features and global representations at different resolutions are extracted by the CNN and Transformer, respectively, and fused in an interactive manner. Moreover, a separate spatial branch is designed to extract multiscale contextual information as queries, and global dependencies between features at different scales are efficiently established by the decoder. Extensive experiments on three public remote sensing datasets demonstrate the superiority of EMRT and indicate that the overall performance of our method outperforms state-of-the-art methods. Code is available at https://github.com/peach-xiao/EMRT.

Published
2023
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99. Multi-task MIML learning for pre-course student performance prediction.

Author

Ma, Yuling, Cui, Chaoran, Yu, Jun, Guo, Jie, Yang, Gongping, and Yin, Yilong

Abstract

In higher education, the initial studying period of each course plays a crucial role for students, and seriously influences the subsequent learning activities. However, given the large size of a course’s students at universities, it has become impossible for teachers to keep track of the performance of individual students. In this circumstance, an academic early warning system is desirable, which automatically detects students with difficulties in learning (i.e., at-risk students) prior to a course starting. However, previous studies are not well suited to this purpose for two reasons: 1) they have mainly concentrated on e-learning platforms, e.g., massive open online courses (MOOCs), and relied on the data about students’ online activities, which is hardly accessed in traditional teaching scenarios; and 2) they have only made performance prediction when a course is in progress or even close to the end. In this paper, for traditional classroom-teaching scenarios, we investigate the task of pre-course student performance prediction, which refers to detecting at-risk students for each course before its commencement. To better represent a student sample and utilize the correlations among courses, we cast the problem as a multi-instance multi-label (MIML) problem. Besides, given the problem of data scarcity, we propose a novel multi-task learning method, i.e., MIML-Circle, to predict the performance of students from different specialties in a unified framework. Extensive experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our approach over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Published
2019
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