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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Tang, Mingqian Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current few-shot action recognition methods reach impressive performance by learning discriminative features for each video via episodic training and designing various temporal alignment strategies. Nevertheless, they are limited in that (a) learning individual features without considering the entire task may lose the most relevant information in the current episode, and (b) these alignment strategies may fail in misaligned instances. To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric. The purpose of the hybrid relation module is to learn task-specific embeddings by fully exploiting associated relations within and cross videos in an episode. Built upon the task-specific features, we reformulate distance measure between query and support videos as a set matching problem and further design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. By this means, the proposed HyRSM can be highly informative and flexible to predict query categories under the few-shot settings. We evaluate HyRSM on six challenging benchmarks, and the experimental results show its superiority over the state-of-the-art methods by a convincing margin. Project page: https://***/.

关键词： Measurement Training Semantics Benchmark testing Pattern recognition Kinetic theory Task analysis

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A Bayesian Approach for Joint Discriminative Dictionary and Classifier Learning

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第12期52卷 7858-7870页

作者： Zhou, Wei Wu, Yue Li, Junlin Wang, Maolin Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China

Sparse representation has been widely applied to image classification, where the key issue is to extract a suitable discriminative dictionary. To this end, we propose a joint dictionary and classifier learning algorithm based on a parameterized Bayesian model. Therein, the Gaussian priors of a dictionary endow it with the capability of discrimination and representation. Moreover, we introduce a multivariate Gaussian prior for the sparse codes to achieve group sparsity, thereby substantially improving the classification performance. Furthermore, the sparse codes are estimated by a group-sparse Bayesian learning (GSBL) method, and the dictionary atoms are updated sequentially by maximizing a posterior. Moreover, to avoid manual parameter adjustment, the hyperparameters are optimized by an evidence maximization method. Accordingly, we develop a classification scheme via GSBL. Finally, extensive experiments are conducted on six benchmark datasets of face classification, object recognition, handwritten recognition, and scene categorization to substantiate the effectiveness and superiority of the proposed method.

关键词： Dictionaries Bayes methods Codes Machine learning Sparse matrices Training Face recognition Dictionary learning image classification sparse Bayesian learning sparse representation (SR)

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Novel fixed-time stability criteria of nonlinear systems and applications in fuzzy competitive neural network and Chua's oscillator

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NEURAL COMPUTING & APPLICATIONS 2023年第22期35卷 16527-16542页

作者： Ren, Fangmin Wang, Xiaoping Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol KeyLaboratory Image Proc & Intelligent Control ofE Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain inspired Intelligent Syst Wuhan 430074 Peoples R China

Since the fixed-time stability forms of nonlinear systems satisfy strict conditions, there are few general forms for nonlinear systems to achieve fixed-time stability. This work proposes a new class of more general fixed-time stability criteria. It is worth mentioning that, compared with the traditional method of estimating the convergence time, this paper obtains a more conservative stable time estimation formula through the integration method of the generalized integral mean theorem. In addition, given that the fixed-time stabilization of neural networks and chaotic oscillators have attracted extensive attention in recent years, and there are still many fixed-time stabilizations of nonlinear systems that have not been studied. Therefore, a discontinuous controller is designed in this paper. The above stability theory results are applied to the fixed-time stabilization of the Takagi-Sugeno (T-S) fuzzy competitive neural network and chaotic system (coupled Chua's oscillator). Finally, the validity and applicability of the theoretical results are verified by examples.

关键词： Fixed-time stability Competitive neural network Chua's oscillator Discontinuous controller

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VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 909-925页

作者： Hao, Xiaoyang Yang, Shuyuan Liu, Ruoyu Feng, Zhixi Peng, Tongqing Huang, Bincheng Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China AVIC Xian Flight Automat Control Res Inst AVIC FAC Software Engn Dept Xian 710076 Peoples R China MeiTuan Grocery Retail Dept Beijing 100029 Peoples R China China Elect Technol Grp Corp Inst Informat Sci & Technol Beijing 100036 Peoples R China

Most existing wideband signal detection and recognition (WSDR) methods rely on diverse, large-scale, and well-labeled training data, which are often difficult to obtain in practical application scenarios such as non-cooperative environments and novel signaling regimes. In this article, we propose a method for constructing a virtual signal large model (VSLM) and applying it to tackle the WSDR challenge under few-shot or even cross-domain few-shot scenarios. Firstly, we design two plug-and-play modules, virtual sample generation (VSG) and virtual category generation (VCG), for VSLM, respectively. VSG simulates the local and overall relationship between the burst signal and the constant signal, which is mainly completed by extracting time-frequency meta-block and data enhancement. Based on VSG and the multi-label concept, we further create virtual novel categories by injecting customizable semantic information into meta-blocks. Then, we further propose a dual decoupled network (DDN) to train the VSLM. DDN enhances signal details by decoupling low gray values (DLGV) in time-frequency representation, and alleviates conflicts during multi-task joint optimization by decoupling spectrum localization and signal classification. Finally, based on the wideband spectrogram dataset, extensive experiments have validated that our proposed methods can significantly improve the performance of WSDR under few-shot conditions.

关键词： Wideband Signal detection Time-frequency analysis Feature extraction Wireless communication Modulation Narrowband Computational modeling Accuracy Spectrogram Wideband signal detection and recognition (WSDR) few-shot dual decoupling network (DDN) virtual sample generation (VSG) virtual category generation (VCG)

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Optimizing Pinning-Synchronization and Mining Pinned-Nodes of Directed Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年

作者： Liu, Hui Lu, Manqiao Zhang, Xi Li, Zengyang Chen, Guanrong Zeng, Zhigang Lu, Jinhu Huazhong Univ Sci & Technol MOE Engn Res Ctr Autonomous Intelligent Unmanned S Sch ArtificialIntelligence & Automat Key Lab Image Proc & Intelligent Control Educ Mini Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Pinning control provides an effective approach to controlling large-scale networks and conserving control resources. This article presents a solution to pinning synchronization in directed networks with a precise index that measures the pinning synchronization capability of directed networks, capturing full topological information about the networks. Building upon this index, the article utilizes matrix analysis tools, such as the non-negative matrix theory and strongly connected decomposition to analyze the impact of network structures and controller parameters on the network synchronizability. Specifically, the study investigates the influence of the in-degree of unpinned nodes, the difference between in-degrees and out-degrees of nodes, strong connectivity components, and the linear feedback control gains on the network synchronizability. Moreover, the article addresses the challenge of optimally selecting pinned nodes by using a graph partitioning algorithm and a greedy node selection algorithm, which can be applied to effectively select pinned nodes in a large-scale network. Extensive simulations on a range of real-world directed networks validate the efficiency of the proposed algorithms and demonstrate their superiority over seven baseline algorithms.

关键词： Synchronization Vectors Laplace equations Partitioning algorithms Eigenvalues and eigenfunctions Complex networks Symmetric matrices Indexes Couplings Automation Directed network pinning control spectral graph theory synchronization

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Dirt Detection and Segmentation Network for Autonomous Washing Robots 5th

Dirt Detection and Segmentation Network for Autonomous Washi...

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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Gu, Shangbin Peng, Gang Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9783031189128;9783031189135

In the field of industrial, robots are becoming a modern method to perform automatic washing on large industrial components while detecting dirt. Therefore, the detection and segmentation of dirt have a great impact on optimizing the effects and improving quality of washing by modern washing robots. We propose DDSN (Dirt Detection and Segmentation Network) in this paper which improves the SVDD (Support Vector Data Description), using neural networks to obtain the optimal kernel function of SVDD, so that training images with no dirt can be mapped to the smallest hypersphere in the feature space. In dirt detection and segmentation, the distance between the test images and the centers of the corresponding hypersphere is defined as the feature value, and the dirt scores of the pixels can be obtained. Before training of dirt dataset, we use a larger anomaly detection dataset named MVTecAD which is also the one-class classification to pretrain the feature extraction network, which makes up for the lack of samples in the dirt dataset and speeds up the convergence of the model. Afterwards, we transfer the feature extraction network to training the dirt dataset of large industrial components. The results show that the methods proposed in this paper performs well in detection and segmentation of both MVTecAD dataset and dirt dataset of large industrial components.

关键词： Robot vision Dirt detection Dirt segmentation

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A Quality Prediction and Parameter Optimization Approach for Turbine Blade Multistage Manufacturing

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第1期29卷 3-15页

作者： Liu, Qianjun Vassiliadis, Vassilios S. Wu, Yudie Zhang, Jiajun Cheng, Cheng Yuan, Ye Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Intelligent Control & Image Proc Wuhan 430074 Peoples R China Univ Cambridge Cambridge CB2 1TN England Wuxi Turbine Blade Co Ltd Wuxi 214000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

As core components of an aircraft engine, turbine blades are featured by complex curved surface with strict precision requirements in multistage manufacturing processes (MMPs). Hence, reliable quality prediction and parameter optimization strategy on MMPs have long been in great demand. However, most techniques focus on the precision improving of single-stage single-product manufacturing systems;these improvements are difficult to implement in MMPs with coordinate transformations among multiple machines. In this article, we propose a noninvasive feature-based quality prediction and parameter optimization framework for high-precision MMPs. In particular, rather than retrofitting sensors onto machinery after installation, this study presents an executable and noninvasive solution by using coordinate measuring machine (CMM) data as input features for prediction. Based on features of different stages of machining, a sequential multistep deep learning architecture is developed to predict the final product quality. A preheating optimization algorithm, modified from a local gradient search method, is used for the optimization of key initial machining features, which results are further improved by the L-BFGS method, thus increasing the final production quality. To estimate the effectiveness of the proposed framework, experiments are carried out on two series of aviation turbine blades with complex curved surfaces from the Wuxi Turbine Blade Company Ltd. China. CMM data at key stages (i.e., blade root milling and comprehensive accurate milling) are used as inputs to predict the final geometrical errors. Comparison results verify that the proposed framework achieves the smallest root mean square error at 0.030 mm and 0.032 mm for two blades, respectively.

关键词： Blades Optimization Turbines Production Milling Prediction algorithms Manufacturing Deep learning intelligence optimization multistage manufacturing processes (MMPs) quality prediction turbine blade machining

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Fully Distributed Observer Design for Mobile Targets

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第3期10卷 1696-1708页

作者： Wang, Xiaoling Fan, Zhen Wang, Lin Su, Housheng Lam, James Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing 210023 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Ctr IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China Univ of Hong Kong Dept Mech Engn Hong Kong Peoples R China

In this paper, a sensor network is used to implement the distributed observer design problem for a linear time-invariant mobile target system with constant or time-varying velocity. Each sensor can only access a part of the output information of the target. Two types of interacting dynamics are endowed to each sensor, one is a consensus-based algorithm for the state estimation using the local measurements, while the other is a leader-following flocking-like algorithm such that the mobile sensors can avoid collision, maintain communication, and track the target. By adopting adaptive coupling gains on the consensus-based term in the state estimation algorithm, a fully distributed observer which is independent of the communication topology associated with the sensor network is constructed. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed theoretical results.

关键词： Observers Heuristic algorithms Topology Target tracking Network topology Couplings Linear systems Consensus distributed observer flocking mobile sensor network multi-agent system

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MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects 17th

MPIB: An MPI-Based Bokeh Rendering Framework for Realistic P...

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17th European Conference on Computer Vision (ECCV)

作者： Peng, Juewen Zhang, Jianming Luo, Xianrui Lu, Hao Xian, Ke Cao, Zhiguo Huazhong Univ Sci & Technol Sch AIA Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Adobe Res San Jose CA USA

ISBN: (纸本)9783031200670;9783031200687

Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background. However, it is challenging for existing bokeh rendering methods to simulate realistic partial occlusion effects due to the missing information of the occluded area in an all-in-focus image. Inspired by the learnable 3D scene representation, Multiplane image (MPI), we attempt to address the partial occlusion by introducing a novel MPI-based high-resolution bokeh rendering framework, termed MPIB. To this end, we first present an analysis on how to apply the MPI representation to bokeh rendering. Based on this analysis, we propose an MPI representation module combined with a background inpainting module to implement high-resolution scene representation. This representation can then be reused to render various bokeh effects according to the controlling parameters. To train and test our model, we also design a ray-tracing-based bokeh generator for data generation. Extensive experiments on synthesized and real-world images validate the effectiveness and flexibility of this framework.

关键词： Bokeh rendering Multiplane image Partial occlusion

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Juewen Peng Zhiyu Pan Chengxin Liu Xianrui Luo Huiqiang Sun Liao Shen Ke Xian Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology S-Lab Nanyang Technological University

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge.

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