no code implementations • 15 Dec 2023 • Xiaolong Fan, Maoguo Gong, Yue Wu, Zedong Tang, Jieyi Liu
Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains.
no code implementations • 12 Dec 2022 • Wu Yue, Peiran Gong, Maoguo Gong, Hangqi Ding, Zedong Tang, Yibo Liu, Wenping Ma, Qiguang Miao
However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, which implies the probability of not falling into local optima and is closely related to the practicality of the algorithm.
no code implementations • 6 May 2022 • Yue Wu, Yibo Liu, Maoguo Gong, Peiran Gong, Hao Li, Zedong Tang, Qiguang Miao, Wenping Ma
The modeling of multi-view point cloud registration as multi-task optimization are twofold.
1 code implementation • CVPR 2021 • Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
1 code implementation • Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 • Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
no code implementations • 24 Dec 2020 • Zedong Tang, Fenlong Jiang, Junke Song, Maoguo Gong, Hao Li, Fan Yu, Zidong Wang, Min Wang
Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generalization performance, compared with Stochastic Gradient Descent (SGD).
1 code implementation • 12 Oct 2020 • Wenfeng Liu, Maoguo Gong, Zedong Tang, A. K. Qin
To enhance node representativeness, the output of each convolutional layer is concatenated with the output of the previous layer's readout to form a global context-aware node representation.