Maoguo Gong
Xidian University
506 Papers
1.4K Citations
Maoguo Gong is an academic researcher from Xidian University. The author has contributed to research in topics: Computer science & Evolutionary algorithm. The author has an hindex of 56, co-authored 377 publications. Previous affiliations of Maoguo Gong include Shandong University of Science and Technology & Chinese Ministry of Education.
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Papers
Multiparty Dual Learning
TL;DR: In this paper , the authors proposed a multiparty dual learning (MPDL) framework to alleviate the problem of limited data with poor quality in an isolated party, and explicitly exploited the probabilistic correlation and structural relationship between dual tasks to regularize the training process.
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Bayesian image denoising using two complementary discontinuity measures
TL;DR: In this article, the authors proposed a Bayesian denoising framework using two complementary discontinuity measures, namely local-inhomogeneity and spatial-gradient, to detect significant discontinuities from noisy images.
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An adaptive coevolutionary memetic algorithm for examination timetabling problems
TL;DR: An adaptive coevolutionary memetic algorithm (ACMA) for examination timetabling problems that obtains competitive results and outperforms the compared approaches on some benchmark instances.
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Inter-Modal Masked Autoencoder for Self-Supervised Learning on Point Clouds
Jiaming Liu,Yue-Fen Wu,Maoguo Gong,Zhixiao Liu,Qiguang Miao,Wenping Ma +5 more
TL;DR: Inspired by multimodality, the proposed Inter-MAE generates pre-trained models that are effective and exhibit superior results in various downstream tasks, and establishes for the first time the feasibility of applying image modality to masked point clouds.
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A Novel Deep Framework for Change Detection of Multi-source Heterogeneous Images
Hongying Liu,Wang Zhongshu,Fanhua Shang,Mingyang Zhang,Maoguo Gong,Feihang Ge,Licheng Jiao +6 more
- 01 Nov 2019
TL;DR: A novel change detection framework based on meta-learning, called MLCD, is proposed, which mainly consists of a modified convolutional neural network and a graph neural network, and it is capable of learning to compare samples in the embedded feature space.
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