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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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AM³Net: Adaptive Mutual-Learning-Based Multimodal Data Fusion Network

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IEEE Transactions on Circuits and Systems for Video Technology 2022年第8期32卷 5411-5426页

作者： Wang, Jinping Li, Jun Shi, Yanli Lai, Jianhuang Tan, Xiaojun Sun Yat-sen University School of Intelligent Systems Engineering Guangzhou510006 China Sun Yat-sen University School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China Zhuhai519082 China

Multimodal data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, plays an important role in object recognition and classification tasks. However, existing methods pay little attention to the specificity of HSI spectral channels and the complementarity of HSI and LiDAR spatial information. In addition, the utilized feature extraction modules tend to consider the feature transmission processes among different modalities independently. Therefore, a new data fusion network named AM3Net is proposed for multimodal data classification;it includes three parts. First, an involution operator slides over the input HSI's spectral channels, which can independently measure the contribution rate of the spectral channel of each pixel to the spectral feature tensor construction. Furthermore, the spatial information of HSI and LiDAR data is integrated and excavated in an adaptively fused, modality-oriented manner. Second, a spectral-spatial mutual-guided module is designed for the feature collaborative transmission among spectral features and spatial information, which can increase the semantic relatedness connection through adaptive, multiscale, and mutual-learning transmission. Finally, the fused spatial-spectral features are embedded into a classification module to obtain the final results, which determines whether to continue updating the network weights. Experimental evaluations on HSI-LiDAR datasets indicate that AM3Net possesses a better feature representation ability than the state-of-the-art methods. Additionally, AM3Net still maintains considerable performance when its input is replaced with multispectral and synthetic aperture radar data. The result indicates that the proposed data fusion framework is compatible with diversified data types. © 1991-2012 IEEE.

关键词： Feature extraction

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Proposing,QP-Unification and Verification of DLSM Based MKE-IIWT Scheme for Redundant Robot Manipulators 3

Proposing,QP-Unification and Verification of DLSM Based MKE-...

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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 Min Yang Dechao Chen Weibing Li Dongsheng Guo Xiaogang Yan 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

To remedy the problem of joint-variable oscillation and instability as well as the non-zero final joint-velocity phenomenon arising in the conventional inertia-inverse weighted torque(IIWT) minimizati

ISBN: (纸本)9781509053643;9781509053636

关键词： Manipulators Torque Minimization Acceleration Kinetic energy Performance analysis

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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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Discrete-Time Control and Simulation of Ship Course Tracking Using ZD Method and ZFD Formula 4NgSFD 3

Discrete-Time Control and Simulation of Ship Course Tracking...

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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 Huinan Xiao Jinjin Wang Jian Li Pei Chen 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

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

ISBN: (纸本)9781509053643;9781509053636

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

关键词： Marine vehicles Trajectory Electronic mail Process control Convergence Mathematical model

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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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Expert with Outlier Exposure for Continual Learning of New Diseases

Expert with Outlier Exposure for Continual Learning of New D...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xu, Zhengjing Chen, Kanghao Zheng, Wei-Shi Tan, Zhijun Yang, Xiaobo Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE China State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou China Peng Cheng Laboratory Department of Network Intelligence Shenzhen China

ISBN: (纸本)9781665468190

Current intelligent diagnosis systems struggle to continually learn to diagnose more and more diseases due to catastrophic forgetting of old knowledge when learning new knowledge. Although storing small old data for subsequent continual learning can effectively help alleviate the forgetting issue, the heavy data imbalance between old classes and to-be-learned new classes in classifier training often causes biased prediction towards the new classes just learned by the updated classifier. In this study, an outlier detection technique is novelly applied to train an additional expert classifier for new classes to help alleviate the class imbalance issue and discriminate the learned new classes from old classes during inference (instead of the training phase). Specially, the stored small data of old classes are considered as outliers during training the expert classifier, such that the output probability distributions from the expert classifier are expected to be obviously different between test data of the old classes and those of the new classes. Such difference between old classes and new classes can be used to fine-tune the original output from the updated classifier which is responsible for prediction of all learned (old and new) classes. During inference, a novel ensemble strategy is proposed to combine the predictions from the updated classifier, the expert classifier, and the previously learned old classifier. The proposed learning and inference framework can be easily combined with existing continual learning strategies. Empirical evaluations on three medical image datasets and one natural image dataset show that the proposed framework can effectively improve continual learning performance. © 2022 IEEE.

关键词： Forecasting

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Continual learning of new diseases with dual distillation and ensemble strategy 23rd

Continual learning of new diseases with dual distillation an...

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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Li, Zhuoyun Zhong, Changhong Wang, Ruixuan Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783030597092

Most intelligent diagnosis systems are developed for one or a few specific diseases, while medical specialists can diagnose all diseases of certain organ or tissue. Since it is often difficult to collect data of all diseases, it would be desirable if an intelligent system can initially diagnose a few diseases, and then continually learn to diagnose more and more diseases with coming data of these new classes in the future. However, current intelligent systems are characterised by catastrophic forgetting of old knowledge when learning new classes. In this paper, we propose a new continual learning framework to alleviate this issue by simultaneously distilling both old knowledge and recently learned new knowledge and by ensembling the class-specific knowledge from the previous classifier and the learned new classifier. Experiments showed that the proposed method outperforms state-of-the-art methods on multiple medical and natural image datasets. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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A cooperative spectrum sensing method based on clustering algorithm and signal feature 4th

A cooperative spectrum sensing method based on clustering al...

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4th International Conference on Cloud computing and Security, ICCCS 2018

作者： Zhang, Shunchao Wang, Yonghua Wan, Pin Zhang, Yongwei Li, Xingcheng School of Automation Guangdong University of Technology Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9783030000059

To solve the problem that the threshold is difficult to calculate in the spectrum sensing method of random matrix, this paper proposes a spectrum sensing method based on the combination of clustering algorithm and signal features. At the same time, in order to improve the performance of feature estimation and the detection performance under the condition of small number of cooperative users, a concept based on stochastic matrix splitting and reorganization is introduced to logically increase the number of cooperative users. In order to further obtain the information from the signal matrix and improve the feature accuracy, the signal perceived by each secondary user (SU) is decomposed into I and Q (IQ) components. Firstly, the signal matrix is split and reassembled and IQ decomposed. Then the covariance matrices of split matrix and matrix after IQ decomposition are calculated respectively and the corresponding eigenvalues are obtained. Then, the features are formed into a feature vector. Finally, the algorithm classifies these feature vector. The simulation experiments under different signal characteristics and different clustering algorithms show that the proposed method can effectively improve the performance of spectrum sensing. © Springer Nature Switzerland AG 2018.

关键词： Clustering algorithms

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A Novel Framework for Maternal ECG Removal from Single-Channel Abdominal Recording

A Novel Framework for Maternal ECG Removal from Single-Chann...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Zhong Zhongping Cao Xuemei Guo Guoli Wang Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781728118680

Objective: Abdominal ECG (AECG) recorded at the maternal abdomen is significantly affected by the maternal ECG (MECG), making the extraction of FECG a challenging task. This paper presents a new MECG elimination method based on Short Time Fourier Transform (STFT) and Convolutional Auto-encoder (CAE). Methods: First, the STFT is used to transform the AECG from one-dimensional (1D) time domain into two-dimensional(2D) time-frequency domain. Next, the CAE model is applied to estimate the 2D-STFT coefficients of MECG. Finally, after the inverse STFT of MECG, we can extract the FECG by subtracting the MECG from the AECG in the time domain. Different from the methods estimated the MECG in the 1D time domain, the novelty of the proposed method relies on estimating the MECG in the 2D time-frequency domain. Specifically, the CAE model learns the end-to-end mappings from the 2D-STFT coefficients of AECG to the MECG. Results: Experimental results show that the proposed method is effective in removing the MECG. Significance: This work enhances the clinical applications of FECG in the early detection of fetal heart diseases.

关键词： diseases electrocardiography encoding Fourier transforms medical signal detection medical signal processing time-frequency analysis medical signal detection Electrocardiography medical signal processing STFT Time frequency analysis Fourier Transform coding maladjustment TIME-DOMAIN College of advanced education cyclophosphamide/doxorubicin/etoposide computer aided engineering electrochemical grinding

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