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arXiv 2025年

作者： Li, Zhenglai Wang, Jun Tang, Chang Zhu, Xinzhong Zhang, Wei Liu, Xinwang Faculty of data science City University of Macau 999078 China School of computer National University of Defense Technology Changsha410073 China School of computer China University of Geosciences Wuhan430074 China College of Mathematics Physics and Information Engineering Zhejiang Normal University Jinhua China Research Institute of Ningbo Cixing Co. Ltd Ningbo China Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250000 China

Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches on eight benchmark MvC datasets. The demo code of this work is publicly available at https://***/guanyuezhen/BMvC. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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A New Semantic Segmentation Diagram for Intelligent Transportation Based on Heterogeneous Knowledge Base

A New Semantic Segmentation Diagram for Intelligent Transpor...

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IEEE Conference on Wireless Communications and Networking

作者： Jingyuan Tang Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Semantic segmentation is regarded as an important technology for future communication and sensing networks due to its promising ability to extract features of transmit data. It integrates the functionality of computer vision and can realize high-fidelity transmission in the channel with lower bandwidth. Knowledge Base (KB) is a key component for the semantic segmentation framework. In this paper, we consider the scenario of intelligent transportation and propose a heterogeneous KB to extract the to-be-transmitted features and information at multiple levels, i.e., the raw level, symbol level, feature level and image level. The compressed features are obtained by a deep-learning-based framework. The proposed KB is shared with the transmitter and the receiver, by a service-oriented multi-stream transmission algorithm to meet various requirements of services. Experiments results demonstrate significant performance gain in terms of quality of service and encoding efficiency.

关键词： Semantic segmentation Semantics Knowledge based systems Transportation Symbols Object detection Streaming media

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PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples

arXiv

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arXiv 2022年

作者： Hu, Shengshan Zhang, Junwei Liu, Wei Hou, Junhui Li, Minghui Zhang, Leo Yu Jin, Hai Sun, Lichao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China Cluster and Grid Computing Lab China National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Services Computing Technology and System Lab China City University of Hong Kong Hong Kong Deakin University Australia Lehigh University United States

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modification for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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ACDR-CRAFF Net: A Multi-Scale Network Based on Adaptive Channel and Coordinate Relational Attention Network for Remote Sensing Scene Classification

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IET Image Processing 2025年第1期19卷

作者： Dai, Wei Xu, Haixia Shi, Furong Yuan, Liming Wang, Xinyu Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Accurate classification of remote sensing scene images is crucial for diverse applications, from environmental monitoring to urban planning. While convolutional neural networks (CNNs) have dramatically improved classification accuracy, challenges remain due to the complex distribution of small objects, varied spatial configurations, and intra-class multimodality in remote sensing images. In this work, we make three key contributions to address these challenges. (1) We propose the adaptive channel and coordinate relational attention network (ACDR-CRAFF), a novel multi-scale feature fusion framework designed to enhance feature representation across scales. (2) We introduce two innovative modules: the adaptive channel dimensionality reduction (ACDR) module, which dynamically adjusts channel representations to retain essential low-dimensional features, and the coordinate relational attention multi-scale feature fusion (CRAFF) module, which effectively captures and transfers spatial information between feature levels. (3) By integrating ACDR and CRAFF, our model achieves a progressive fusion of local to global features, ensuring robust feature expressiveness at multiple scales. Experimental results on four widely used benchmark datasets demonstrate that ACDR-CRAFF consistently outperforms several state-of-the-art methods, achieving significant improvements in classification accuracy and setting a new benchmark for complex remote sensing scene classification tasks. These results underscore the effectiveness of our approach in addressing the limitations of existing methods and advancing the state of the art in remote sensing image analysis. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Photointerpretation

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Coordinate Attention-Based Convolution Neural Network for In-loop Filter of AVS3

Coordinate Attention-Based Convolution Neural Network for In...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Ruotong Wu Songlin Sun Jiaqi Zou Shaokang Wang Zhilei Ling School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Employing deep learning is a promising solution for reducing encoding bit rate in future video encoding systems. This paper proposes a neural network-based in-loop filter for the third generation of Audio Video Coding Standard (AVS3). The proposed network introduces a coordinate attention mechanism-based convolutional neural network in-loop filter (AMCNNLF) with a flexible attention module and a residual feature aggregation (RFA) module. Specifically, the attention module focuses on salient features to capture the visual structure, while the RFA module takes full advantage of the local refinement features. By leveraging the encoding parameters, we introduce the Quantizer Parameter (QP) values as auxiliary features to enable the proposed network suitable for processing encoded videos with multiple QPs. Experimental results indicate that the proposed network reduces the average Bjøntegaard-Delta rate (BD-Rate) of luma component about 0.4% under all intra configuration compared with the benchmark.

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A Blockchain-Based Multi-Cloud Storage Data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing Data centers Costs Blockchains Behavioral sciences

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A Mother-Son Model for Multi-Objective Non-Invasive Load Monitoring

SSRN

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SSRN 2023年

作者： Mei, Huawei Liu, Yanping Cao, Wangbin Yu, Yang North China Electric Power University Baoding071003 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding071003 China Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Baoding071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding071003 China

This paper proposes a mother-son model (MSM) to address the challenges of missing data, complex models, and limited transfer capabilities in non-intrusive load monitoring (NILM). The MSM model designs a novel transfer method based on feature inheritance to dynamically decompose multiple appliances (including unknown appliances). The mother model incorporates a six-parallel feature extraction structure to extract comprehensive features, which are then inherited by the son model. The features inherited from the mother model integrate information from multiple dimensions in the spatial domain and focus on local fine features in the temporal domain. The son model offers the capability to adjust the number of appliances and decompose unknown appliances, catering to the requirements of practical applications of NILM. Furthermore, a novel statistical metric, Ratio Squared Error (RSE), is designed to quantify the proportion of squared error in energy consumption. The experiments are carried out on five households and statistically evaluated using five statistical indicators. The results indicate that the son model exhibits superior decomposition performance and greater resilience compared to the state-of-the-art model. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Defect Detection of Rubber Gloves Based on Normal Samples

Defect Detection of Rubber Gloves Based on Normal Samples

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IEEE International Conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Naigong Yu Honglu Wang Qiao Xu Jia Lin Faculty of Information Technology Ministry of Education Beijing University of Technology Beijing Key Laboratory of Computing Intelligence and Intelligent System Engineering Research Center of Digital Community Beijing China

ISBN: (纸本)9781665423113;9781665446327

Rubber gloves are widely favored in various industries, and the defect detection link in production is very important. This paper proposes a method for detecting defects in rubber gloves based on normal samples (no defects). First, random noise is added to the collected rubber glove images, and the size is standardized and unified. Secondly, establish an improved GANomaly network model. According to the small and difficult defect to distinguish characteristics of black spots and oil defects of rubber gloves, a noise-reducing convolutional autoencoder is built in the network model as a generation network, and the least square loss is introduced for the model of confrontation training. The model only uses normal sample input for training, learning the characteristic distribution of the normal sample and performs image reconstruction, and judges whether it has defects according to the reconstruction effect score of the input sample during testing. The experiment is based on the rubber glove image data set, and the results show that the method can accurately identify whether there are defects on the rubber glove.

关键词： Training Industries Cloud computing Oils Conferences Production Generative adversarial networks

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Generalized Visual Quality Assessment of GAN-Generated Face Images

arXiv

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arXiv 2022年

作者： Tian, Yu Ni, Zhangkai Chen, Baoliang Wang, Shiqi Wang, Hanli Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Tongji University Shanghai 200092 China The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been developed to produce vivid face images towards different application scenarios. However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model. Herein, we make the first attempt to study the subjective and objective quality towards generalized quality assessment of GFIs. More specifically, we establish a large-scale database consisting of GFIs from four GAN algorithms, the pseudo labels from image quality assessment (IQA) measures, as well as the human opinion scores via subjective testing. Subsequently, we develop a quality assessment model that is able to deliver accurate quality predictions for GFIs from both available and unseen GAN algorithms based on meta-learning. In particular, to learn shared knowledge from GFIs pairs that are born of limited GAN algorithms, we develop the convolutional block attention (CBA) and facial attributes-based analysis (ABA) modules, ensuring that the learned knowledge tends to be consistent with human visual perception. Extensive experiments exhibit that the proposed model achieves better performance compared with the state-of-the-art IQA models, and is capable of retaining the effectiveness when evaluating GFIs from the unseen GAN algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector

arXiv

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arXiv 2024年

作者： Mao, Qianchen Li, Qiang Wang, Bingshu Zhang, Yongjun Dai, Tao Chen, C.L. Philip The School of Software Northwestern Polytechnical University Taicang Campus Suzhou215400 China The School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 China

In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves MIoU by 4.7 and has a 7× FPS acceleration compared to the previous state-of-the-art model. The code will be open to the public. © 2024, CC BY.

关键词： Deep learning

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