Search Results for author: Maoguo Gong

Found 21 papers, 5 papers with code

Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism

no code implementations6 May 2022 Yue Wu, Yibo Liu, Maoguo Gong, Hao Li, Zedong Tang, Qiguang Miao, Wenping Ma

The intra-task knowledge sharing introduces aiding tasks that are much simpler to solve, and useful information is shared within tasks, accelerating the search process.

3D Reconstruction Point Cloud Registration

ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning

no code implementations29 Oct 2021 Maoguo Gong, Yuan Gao, Yue Wu, A. K. Qin

Inspired by the idea of dropout in neural networks, we introduce a network sampling strategy in the multi-party setting, which distributes different subnets of the central model to clients for updating, and the differentiable sampling rates allow each client to extract optimal local architecture from the supernet according to its private data distribution.

Self-adaptive Multi-task Particle Swarm Optimization

no code implementations9 Oct 2021 Xiaolong Zheng, Deyun Zhou, Na Li, Yu Lei, Tao Wu, Maoguo Gong

In the focus search strategy, if there is no knowledge source benefit the optimization of a task, then all knowledge sources in the task's pool are forbidden to be utilized except the task, which helps to improve the performance of the proposed algorithm.

Transfer Learning

Hierarchical Multimodal Transformer to Summarize Videos

no code implementations22 Sep 2021 Bin Zhao, Maoguo Gong, Xuelong Li

To integrate the two kinds of information, they are encoded in a two-stream scheme, and a multimodal fusion mechanism is developed based on the hierarchical transformer.

Frame Machine Translation +3

Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer

1 code implementation2 Aug 2021 Junyu Gao, Maoguo Gong, Xuelong Li

To this end, we propose a Dilated Convolutional Swin Transformer (DCST) for congested crowd scenes.

Crowd Counting Representation Learning

SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature

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.

Dimensionality Reduction

SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate Curvature

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.

Dimensionality Reduction

AudioVisual Video Summarization

no code implementations17 May 2021 Bin Zhao, Maoguo Gong, Xuelong Li

Motivated by this, we propose to jointly exploit the audio and visual information for the video summarization task, and develop an AudioVisual Recurrent Network (AVRN) to achieve this.

Video Summarization

Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations

no code implementations14 May 2021 Xiaolong Fan, Maoguo Gong, Yue Wu, Hao Li

Specifically, we first utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs.

Graph Representation Learning

Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images

no code implementations IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021 Hao Li, Maoguo Gong, Mingyang Zhang, Yue Wu

Change detection in heterogeneous remote sensing images is a challenging problem because it is hard to make a direct comparison in the original observation spaces, and most methods rely on a set of manually labeled samples.

Change Detection

Multi-Party Dual Learning

no code implementations14 Apr 2021 Maoguo Gong, Yuan Gao, Yu Xie, A. K. Qin, Ke Pan, Yew-Soon Ong

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data.


Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework

no code implementations14 Apr 2021 Yuan Gao, Jiawei Li, Maoguo Gong, Yu Xie, A. K. Qin

Since the existing naive model parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogy multi-party learning as a many-to-one knowledge sharing problem.

Feature Weighted Non-negative Matrix Factorization

no code implementations24 Mar 2021 Mulin Chen, Maoguo Gong, Xuelong Li

Non-negative Matrix Factorization (NMF) is one of the most popular techniques for data representation and clustering, and has been widely used in machine learning and data analysis.

AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy

no code implementations24 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).

Differentially Private ADMM Algorithms for Machine Learning

no code implementations31 Oct 2020 Tao Xu, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Longjie Shen, Maoguo Gong

For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with performance guarantee of $(\epsilon,\delta)$-differential privacy ($(\epsilon,\delta)$-DP).

Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations

1 code implementation12 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.

General Classification Graph Classification +1

Nucleus Neural Network: A Data-driven Self-organized Architecture

no code implementations8 Apr 2019 Jia Liu, Maoguo Gong, Haibo He

In this paper, we propose a nucleus neural network (NNN) and corresponding connecting architecture learning method.

Semi-supervised Multimodal Hashing

no code implementations9 Dec 2017 Dayong Tian, Maoguo Gong, Deyun Zhou, Jiao Shi, Yu Lei

As unsupervised multimodal hashing methods are usually inferior to supervised ones, while the supervised ones requires too much manually labeled data, the proposed method in this paper utilizes a part of labels to design a semi-supervised multimodal hashing method.


Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

4 code implementations22 Aug 2017 Yu Zhao, Rennong Yang, Guillaume Chevalier, Maoguo Gong

Human activity recognition (HAR) has become a popular topic in research because of its wide application.

Activity Recognition

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