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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wu, Faming Hu, Yanxu Wang, Jinpeng Ma, Andy J. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Few-shot object detection is an important but challenging task where only a few instances of novel categories are available. The widely used approach is to pretrain a detector on base classes with abundant samples and then fine-tune it for novel classes. Due to the extreme data imbalance between base and novel classes, the detection performance of novel classes degrades with the distribution bias. To overcome this limitation, we propose a distribution calibration strategy and a class discrimination regularization method for better few-shot detection. Based on theoretical analysis on decision margins of base and novel classes, the decision area of novel classes is enlarged to balance the prediction probability. On the other hand, to increase the separability of inter-class distributions, the similarity between class-specific representations is minimized. Extensive experiments on PASCAL VOC and MS COCO datasets verify the effectiveness and generalization ability of our method to improve few-shot object detection. © 2022 IEEE.

关键词： Object detection

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Region-Interactive Proposal Network and Class-Interactive Feature Learning for Few-Shot Object Detection

Region-Interactive Proposal Network and Class-Interactive Fe...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Hu, Yanxu Wu, Faming Wang, Jinpeng Ma, Andy J. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Few-shot object detection is a promising approach to solving the problem of detecting novel objects with only limited annotated data for training. Most existing methods are developed based on the progress in few-shot classification, which pay little attention to improving the localization module and modelling class interrelation. To address these issues, this paper proposes two novel modules, namely Region-interactive Proposal Network (Ri-PN) and Class-interactive Feature Learning (Ci-FL), for better localization and classification performance, respectively. In the Ri-PN, regions of novel classes are interacted with base classes via graph convolution instead of background due to the stronger relevance between base and novel classes together with the guidance of supervised regions loss. On the other hand, the Ci-FL refines class-specific features in prototypical learning by attentive graph convolutional network. Experimental results on PASCAL VOC and MS COCO datasets verify the superiority of our method for few-shot object detection. © 2022 IEEE.

关键词： Object detection

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Type-z0g3 Controller of Output Tracking for Fractional Power System 3

Type-z0g3 Controller of Output Tracking for Fractional Power...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Fangzheng Lai Penghao He Jinjin Wang Dechao Chen School of Information Science and Technology Sun Yat-sen University(SYSU) SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Because most systems are nonlinear in practical applications,output tracking for nonlinear system is of great interest to engineers and *** a specific class of nonlinear system,fractional po

ISBN: (纸本)9781509053643;9781509053636

Because most systems are nonlinear in practical applications,output tracking for nonlinear system is of great interest to engineers and *** a specific class of nonlinear system,fractional po

关键词： Trajectory Nonlinear systems Simulation Power system stability Sun

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IMPROVING WEAKLY SUPERVISED OBJECT LOCALIZATION BY UNCERTAINTY ESTIMATION OF PSEUDO SUPERVISION

IMPROVING WEAKLY SUPERVISED OBJECT LOCALIZATION BY UNCERTAIN...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Chen, Xi Ma, Andy J. Guo, Nanxi Chen, Jiajia School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

Pseudo bounding box supervision is a promising approach for weakly supervised object localization (WSOL) with only image-level labels. However, the generated pseudo bounding boxes may be inaccurate or even completely non-overlapped with the objects of interest. In this paper, we propose to estimate the uncertainty of pseudo bounding boxes such that the negative impact caused by inaccurate estimation of pseudo supervision could be alleviated for better WSOL. The refined bounding boxes and corresponding variance uncertainties are learned by training a neural network regressor to penalize the erroneous estimations. To the best of our knowledge, this is the first work to incorporate uncertainty information of pseudo bounding boxes for WSOL. Experimental results show that our method not only outperforms previous state-of-the-art methods in CUB-200-2011 and ILSVRC datasets but also gives more precise bounding box prediction when the IoU threshold is higher. © 2021 IEEE

关键词： Uncertainty analysis

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ZG Controllers Handling VIP System with Nonzero Link Mass,Large Friction Coefficient and Different Initial Pendulum Angle 3

ZG Controllers Handling VIP System with Nonzero Link Mass,La...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Jinjin Wang Binbin Qiu Deyang Zhang Yang Shi School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

The vehicular inverted pendulum(VIP) system is one of the most important issues in dynamics and control theory,and has been widely used as a benchmark for testing control ***,the tracki

ISBN: (纸本)9781509053643;9781509053636

The vehicular inverted pendulum(VIP) system is one of the most important issues in dynamics and control theory,and has been widely used as a benchmark for testing control ***,the tracki

关键词： Trajectory Vehicle dynamics Mathematical model Friction Electronic mail Heuristic algorithms

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Uncertainty Guided Refinement for Fine-Grained Salient Object Detection

arXiv

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arXiv 2025年

作者： Yuan, Yao Gao, Pan Dai, Qun Qin, Jie Xiang, Wei Collge of Computer Science and Technology Nanjing University of Aeronautics and Astronautics the Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC3086 Australia

Recently, salient object detection (SOD) methods have achieved impressive performance. However, salient regions predicted by existing methods usually contain unsaturated regions and shadows, which limits the model for reliable fine-grained predictions. To address this, we introduce the uncertainty guidance learning approach to SOD, intended to enhance the model’s perception of uncertain regions. Specifically, we design a novel Uncertainty Guided Refinement Attention Network (UGRAN), which incorporates three important components, i.e., the Multilevel Interaction Attention (MIA) module, the Scale Spatial-Consistent Attention (SSCA) module, and the Uncertainty Refinement Attention (URA) module. Unlike conventional methods dedicated to enhancing features, the proposed MIA facilitates the interaction and perception of multilevel features, leveraging the complementary characteristics among multilevel features. Then, through the proposed SSCA, the salient information across diverse scales within the aggregated features can be integrated more comprehensively and integrally. In the subsequent steps, we utilize the uncertainty map generated from the saliency prediction map to enhance the model’s perception capability of uncertain regions, generating a highly-saturated fine-grained saliency prediction map. Additionally, we devise an adaptive dynamic partition (ADP) mechanism to minimize the computational overhead of the URA module and improve the utilization of uncertainty guidance. Experiments on seven benchmark datasets demonstrate the superiority of the proposed UGRAN over the state-of-the-art methodologies. Codes will be released at https://***/I2-Multimedia-Lab/UGRAN. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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Point of interest recommendation with social and geographical influence 4

Point of interest recommendation with social and geographica...

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4th IEEE International Conference on Big Data, Big Data 2016

作者： Zhang, Da-Chuan Li, Mei Wang, Chang-Dong School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781467390040

Point of interest (POI) recommendation, a service which can help people discover useful and interesting locations has emerged rapidly with the development of location-based social networks (LBSNs), like Foursquare, Gowalla and Wechat. The large number of check-in histories make it possible to mine the preference of each user and then to provide accurate personalized POI recommendation. In real-world applications, apart from check-in data, there are some other useful information available for making better POI recommendation, such as social relationship among users and geographical influence. In this paper, a new POI recommendation method called Social and Geographical Fusing Model (SGFM) is designed. The basic idea is summarized as follows. Firstly, the users' check-in records and social influence are integrated in a combinative model. Then the global user impact factors generated by the PageRank algorithm are used to improve the combinative model. Secondly, a geographical influence measurement is used to capture the users' physical check-in characters. Finally, the enhanced combinative model and geographical influence are combined together to form a new framework. Extensive experiments have been conducted on a famous dataset, namely Gowalla. The comparison results confirm that the proposed framework outperforms state-of-the-art POI recommendation methods significantly. © 2016 IEEE.

关键词： Collaborative filtering

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Identification method of forest fire risk factors and their coupling relationship driven by attribute dependence

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International Journal of Disaster Risk Reduction 2025年 125卷

作者： Enhui Zhao Ning Wang Shibo Cui Rui Zhao Yongping Yu School of Economics and Management Dalian University of Technology Dalian Liaoning Province 116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education Dalian University of Technology Dalian 116024 China DUT Institute for Advanced Intelligence Dalian University of Technology Dalian 116024 China

Identifying forest fire risk factors and their coupling relationships is crucial for revealing the mechanism of fire occurrence and assessing risks. However, previous studies on forest fire risk factors often ignore the differences and coupling effects between risk factors, resulting in inaccurate risk prediction. Given this, this study introduces the attribute dependence of fuzzy rough set into forest fires and constructs a new method to identify key risk factors and measure their coupling effects. Taking the forest fire risk in the Thompson area of Canada from 1984 to 2023 as the research object, the influence of each risk factor on forest fire is quantified by the attribute dependence function. Then, according to the influence degree of each risk factor, the weighted processing is carried out, and the coupling effect of risk factors is measured by combining the overlapping idea of fuzzy mutual information. The results show that climate factors have a significant impact on forest fires, while topographic factors, vegetation factors, and human factors are relatively weak. The coupling effect of topographic factors, vegetation factors, and human factors is also weak, but the combination of climate factors and topographic factors, vegetation factors, and human factors significantly enhances its influence.

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Inequality-Type Zhang Equivalency of Great-Than-or-Equal-to (GTOET) Sign in Matrix-Vector,Matrix,and Integral Situations Applied to Obstacle Avoidance 41

Inequality-Type Zhang Equivalency of Great-Than-or-Equal-to ...

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第41届中国控制会议

作者： Yunong Zhang Jianzhen Xiao School of Computer Science and Engineering Sun Yat-sen University Research Institute of Sun Yat-sen University in Shenzhen Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the concept of Zhang equivalency(ZE) and equality-type ZE are *** the basis of equalitytype ZE,we introduce the inequality-type ZE of great-than-or-equal-to(GTOET) sign and thus propose the order-1,order-2,and order-3 general formulas of the inequality-type ZE of GTOET ***,we investigate the inequality-type ZE of GTOET sign in three different and appealing situations,including matrix-vector situation,matrix situation,and integral ***,the corresponding formulas are *** distance between a certain object and an obstacle can be considered as the point-to-point distance,and thus the obstacle avoidance scheme is described as an *** by that thought,we apply the inequality-type ZE of GTOET sign to the matrix-form obstacle avoidance.

关键词： Zhang equivalency Great-than-or-equal-to(GTOET) sign Inequalities Obstacle avoidance

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Learning High-Performance Spiking Neural Networks with Multi-Compartment Spiking Neurons 12th

Learning High-Performance Spiking Neural Networks with Mult...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Li, Xinjie Tang, Jianxiong Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783031463075

In recent years, spiking neural networks (SNNs) have gained significant attention due to bio-inspired working mechanism. The VGG-like and ResNet-like architectures are widely used for SNNs modeling. However, the spiking features of such architectures are generated by the layer-wise Integrate-and-Fire (IF) dynamics. The firing of deep neurons is independent of the neurons in the shallow layers. In this paper, we propose Multi-Compartment Spiking Neural Network (MC-SNN) that integrates the MPs of the shallow neurons to fire the deep neurons. Specifically, the MC-SNN is modeled by a Multi-Compartment Leaky Integrate-and-Fire (MC-LIF) neuron and an Adaptive Gating Unit (AGU). The MC-LIF neuron models the IF dynamics using the MPs from both deep and shallow layers, and the AGU adaptively scales the MPs of the MC-LIF neuron. These increase the information interaction between spiking neurons and improve the performance of SNNs. Besides, we design the Binarized Synaptic Encoder (BSE) to reduce the computation cost for the input of SNNs. Experimental results show that the MC-SNN performs well on the neuromorphic datasets, reaching 79.52% and 81.24% on CIFAR10-DVS and N-Caltech101, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurons

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