3 citations · 9 across the 14 of their papers we have counts for
14 papers
Triple Point Masking
Jiaming Liu, Linghe Kong, Yue Wu +5
Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point…
Neural Gaussian Similarity Modeling for Differential Graph Structure Learning
Xiaolong Fan, Maoguo Gong, Yue Wu +2
Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains. However, constructing an…
M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking
Jiaming Liu, Yue Wu, Maoguo Gong +3
3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving. Sparse and occluded data in scene point clou…
PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration
Yue Wu, Yongzhe Yuan, Xiaolong Fan +3
We propose a new framework that formulates point cloud registration as a denoising diffusion process from noisy transformation to object transformation. During training stage, obje…
One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration
Yongzhe Yuan, Yue Wu, Maoguo Gong +2
The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially ov…
Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration
Wu Yue, Peiran Gong, Maoguo Gong +5
Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of…
Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification
Mingyang Zhang, Ziqi Di, Maoguo Gong +3
In recent years, research on hyperspectral image (HSI) classification has continuous progress on introducing deep network models, and recently the graph convolutional network (GCN)…
Towards Consistency and Complementarity: A Multiview Graph Information Bottleneck Approach
Xiaolong Fan, Maoguo Gong, Yue Wu +3
The empirical studies of Graph Neural Networks (GNNs) broadly take the original node feature and adjacency relationship as singleview input, ignoring the rich information of multip…
Entropy Induced Pruning Framework for Convolutional Neural Networks
Yiheng Lu, Ziyu Guan, Yaming Yang +3
Structured pruning techniques have achieved great compression performance on convolutional neural networks for image classification task. However, the majority of existing methods…
SBPF: Sensitiveness Based Pruning Framework For Convolutional Neural Network On Image Classification
Yiheng Lu, Maoguo Gong, Wei Zhao +2
Pruning techniques are used comprehensively to compress convolutional neural networks (CNNs) on image classification. However, the majority of pruning methods require a well pre-tr…
Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism
Yue Wu, Yibo Liu, Maoguo Gong +5
Multi-view point cloud registration is fundamental in 3D reconstruction. Since there are close connections between point clouds captured from different viewpoints, registration per…
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Maoguo Gong, Yuan Gao, Yue Wu +1
Distributed multi-party learning provides an effective approach for training a joint model with scattered data under legal and practical constraints. However, due to the quagmire o…
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
Xiaolong Fan, Maoguo Gong, Yue Wu +1
Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture inf…
Multi-Party Dual Learning
Maoguo Gong, Yuan Gao, Yu Xie +3
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed partie…