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arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Character-Level Street View Text Spotting Based on Deep Multisegmentation Network for Smarter Autonomous Driving

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 297-308页

作者： Zhang, Chongsheng Tao, Yuefeng Du, Kai Ding, Weiping Wang, Bin Liu, Ji Wang, Wei Henan Provincial Key Laboratory of Big Data Analysis and Processing Laboratory of the Yellow River Cultural Heritage Henan University Kaifeng475001 China School of Information Science and Technology Nantong University Nantong226019 China School of Electrical Engineering Zhejiang University Hangzhou310058 China Big Data Lab Baidu Inc. Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Urban scenes are full of street entities with sign boards. Therefore, in autonomous driving, street view text spotting techniques will play a significant role in the precise understanding of surrounding scenes during driving, because texts contained in the images usually provide important clues for accurate image understanding, while it is often ambiguous for existing computer vision algorithms to understand scene images without texts. In this work, we propose a Multi-Segmentation network for character-level scene Text Detection (MSTD). The MSTD introduces a densely connected atrous spatial pyramid pooling module to enlarge the receptive field of the feature extraction layer, so as to localize long as well as large-sized text instances. Moreover, it devises a double segmentation subnetwork to utilize two independent but inherently complementary losses to co-optimize the network and increase the reliability of the confidence scores in predicting the text/nontext areas. With the character instances detected by the MSTD, one can easily perform scene text spotting with classic object recognition networks such as ResNet and DenseNet. We carried out extensive experiments on nine scene text datasets to demonstrate the outstanding performance of the MSTD on character-level and line-level text instance localization and scene text recognition, where the MSTD significantly outperforms the state-of-the-art scene text detection methods and the sequence-to-sequence-learning-based scene text recognizers. © 2020 IEEE.

关键词： Character recognition

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Laser tracking leader-follower automatic cooperative navigation system for UAVs

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International Journal of Agricultural and Biological Engineering 2022年第2期15卷 165-176页

作者： Rui Ming Zhiyan Zhou Zichen Lyu Xiwen Luo Le Zi Cancan Song Yu Zang Wei Liu Rui Jiang College of Engineering South China Agricultural University/Guangdong Laboratory for Lingnan Modern AgricultureGuangzhou 510642China College of Computer and Control Engineering Minjiang University/Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlFuzhou 350108China Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence(GDKL-AAI) Guangzhou 510642China Guangdong Engineering Research Center for Agricultural Aviation Application(ERCAAA) Guangzhou 510642China Key Laboratory of Key Technology on Agricultural Machine and Equipment South China Agricultural UniversityMinistry of Education of P.R.ChinaGuangzhou 510642China

Currently,small payload and short endurance are the main problems of a single UAV in agricultural applications,especially in large-scale *** is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative *** study proposed a laser tracking leader-follower automatic cooperative navigation system for *** leader in the cluster fires a laser beam to irradiate the follower,and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking *** on the existing kernel correlation filter(KCF)tracking algorithm,an improved KCF real-time spot tracking method was *** with the traditional KCF tracking algorithm,the recognition and tracking rate of the optimized algorithm was increased from 70%to 95%in indoor environment,and was increased from 20%to 90%in outdoor *** navigation control method was studied from two aspects:the distance coordinate transformation model based on micro-gyroscope and navigation control *** error of spot position was reduced from the maximum(3.12,−3.66)cm to(0.14,0.12)cm by correcting the deviation distance of the spot at different angles through a coordinate correction *** image coordinate conversion model was established for a complementary metal-oxide-semiconductor(CMOS)camera and laser receiving device at different mounting *** laser receiving device was divided into four regions,S0-S3,and the speed of the four regions is calculated using an uncontrollable discrete Kalman *** outdoor flight experiments of two UAVs were carried out outdoors using this *** experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm,and the coefficient of variation is *** average flight error on the Z-axis is 7.3 cm,and the coefficient of variation is *** study demonstrated the possibility

关键词： two-UAVs cooperative visual navigation laser tracking

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Escalator passenger posture anomaly detection based on improved SSD model 12

Escalator passenger posture anomaly detection based on impro...

引用

12th International Conference on information technology in Medicine and Education, ITME 2022

作者： Xu, Teng Chen, Jian Li, Zuoyong Cai, Yuanzheng Fujian University of Technology School of Electronic Electrical Engineering and Physics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

With the widespread use of escalators in daily life, escalator-related injury factors are threatening public health. To secure escalator system, most of existing studies focus on post hoc statistics and trend to fix these deficiencies to reduce escalator-related potential risks. However, they are not designed for early-stage detection of impending escalator unsafe factors. To solve this problem, this paper proposes an improved Single Shot MultiBox Detector (SSD) algorithm for anomaly detection of passenger posture on escalators. Firstly, we replace the widely used VGG16 backbone of SSD network by simpler MobileNetV2 to accelerate model inference. Secondly, the distance intersection over Union (DIoU) loss function is introduced to further improve performance. Based on the characteristics of IoU and considering the shortcomings of GIoU, DIoU directly regresses the Euclidean distance between the center points of the two boxes to accelerate the convergence. The experimental results show that the improved SSD escalator passenger posture anomaly detection algorithm can efficiently identify the dangerous posture of escalator passengers and start relevant safety protection measures. According to inference of model, the escalator system can be controlled in practice to formulate the corresponding safety protection measures, and improve the safety performance of the escalator operation. © 2022 IEEE.

关键词： Anomaly detection

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LSU-NET: LIGHTWEIGHT AUTOMATIC ORGANS SEGMENTATION NETWORK FOR MEDICAL IMAGES

arXiv

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arXiv 2025年

作者： Ding, Yujie Teng, Shenghua Li, Zuoyong Chen, Xiao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou350121 China

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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Topology Uncertainty Modeling For Imbalanced Node Classification on Graphs

Topology Uncertainty Modeling For Imbalanced Node Classifica...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jiayi Gao Jiaxing Li Ke Zhang Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Most existing graph neural networks work under a class-balanced assumption, while ignoring class-imbalanced scenarios that widely exist in real-world graphs. Although there are many methods in other fields that can alleviate this issue, they do not consider the special topology of the non-Euclidean graph. Hence, we propose Graph Topology Uncertainty (GraphTU), a novel probabilistic class-imbalanced solution specifically for graphs. Firstly, an invisible "uncertain gap" between under-represented minorities in training set and authentic minorities in unseen set is modeled by estimating statistical variances in topology. We extend the training distribution for minorities by sampling in this gap through a non-parametric way. Moreover, a gradient-guided mask is introduced to prevent biased statistics. Extensive experiments demonstrate the superior performance of GraphTU.

关键词： Training Uncertainty Network topology Signal processing Probabilistic logic Graph neural networks Acoustics

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Image Segmentation via Fischer-Burmeister Total Variation and Thresholding

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Advances in Applied Mathematics and Mechanics 2022年第4期14卷 960-988页

作者： TingtingWu Yichen Zhao Zhihui Mao Li Shi Zhi Li Yonghua Zeng School of Science Nanjing University of Posts and TelecommunicationsNanjingJiangsu 210023China The Department of Computer Science and Technology Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghai 200241China College of Field Engineering PLA Army Engineering UniversityNanjingJianhsu 210007China

Image segmentation is a significant problem in image *** this paper,we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation(FBTV).The first stage of our method is to calculate a smooth solution from the FBTV Mumford-Shah ***,we design a new difference of convex algorithm(DCA)with the semi-proximal alternating direction method of multipliers(sPADMM)*** the second stage,we make use of the smooth solution and the K-means method to obtain the segmentation *** simulate images more accurately,a useful operator is introduced,which enables the proposed model to segment not only the noisy or blurry images but the images with missing pixels *** demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods,especially on the images with missing pixels.

关键词： Image segmentation Fischer-Burmeister total variation difference of convex algorithm sPADMM K-means method.

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Topgformer: Topological-Based Graph Transformer for Mapping Brain Structural Connectivity to Functional Connectivity

Topgformer: Topological-Based Graph Transformer for Mapping ...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dalu Guo Ke Zhang Jiaxing Li Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Exploring the mapping between structural connectivity (SC) and functional connectivity (FC) is of essential importance to understanding the working mechanism of the human brain. Traditional methods are difficult to represent the complex relationship of high-order interaction between SC and FC. Recent learning-based methods can not well capture the important long-range interactions and edge information of brain connectivity. To address the issue, we propose a novel Topological-based Graph Transformer (TopGFormer) to generate functional connectivity from the structure connectivity with sufficient consideration of topological properties of brain connectivity. We propose a Topological Multi-Head Attention block that simultaneously uses centrality encoding and adjacency matrix to capture node and edge importance respectively. The centrality encoding reflects the importance of brain regions while the adjacency matrix captures the linking relationship of different regions. Experiments on HCP resting and emotion task datasets demonstrate the performance of the proposed method.

关键词： Learning systems Brain Correlation Signal processing Transformers Encoding Acoustics

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Fully Differentiable Correlation-Driven 2D/3D Registration for X-Ray to CT Image Fusion

Fully Differentiable Correlation-Driven 2D/3D Registration f...

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IEEE International Symposium on Biomedical Imaging

作者： Minheng Chen Zhirun Zhang Shuheng Gu Zhangyang Ge Youyong Kong School of Computer Science and Engineering Southeast University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University China Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. In recent years, some learning-based fully differentiable methods have produced beneficial outcomes while the process of feature extraction and gradient flow transmission still lack controllability and interpretability. To alleviate these problems, in this work, we propose a novel fully differentiable correlation-driven network using a dual-branch CNN-transformer encoder which enables the network to extract and separate low-frequency global features from high-frequency local features. A correlation-driven loss is further proposed for low-frequency feature and high-frequency feature decomposition based on embedded information. Besides, a training strategy that learns to approximate a convex-shape similarity function is applied in our work. We test our approach on a in-house dataset and show that it outperforms both existing fully differentiable learning-based registration approaches and the conventional optimization-based baseline. Our code is available at https://***/m1nhengChen/cdreg.

关键词： Training Correlation Computed tomography Surgery Process control Feature extraction Controllability

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DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Huang, Yuxian Yang, Geng Yuan, Dong Yu, Shui Nanjing University of Posts and Telecommunication College of Computer Science and Software China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China University of Sydney School of Electrical and Information Engineering Australia University of Technology Sydney School of Computer Science Australia

Recently, self-supervised learning has garnered significant attention for its ability to extract high-quality features from unlabeled data. However, existing research indicates that backdoor attacks can pose significant threats to self-supervised learning. Additionally, due to the substantial training overhead, traditional backdoor detection methods in supervised learning, such as meta-learning, are not well-suited for self-supervised learning. Current backdoor detection methods for self-supervised learning can only defend against backdoor attacks triggered by patch. To address this challenge, we proposes DBSSL, a scheme that can efficiently detect backdoor attacks in self-supervised learning models. In DBSSL, we connect backdoor attacks with adversarial attacks and use adversarial perturbations for detection. Specifically, we first conduct targeted adversarial attacks on the samples, classifying them into different classes. Then, we employ the Z-Score method to analyze these adversarial perturbations. If outliers are present, we can infer that the self-supervised learning model has been backdoored. Unlike previous works, our scheme is better suited for downstream users with constrained computation resources and exhibits excellent detection capabilities for backdoor attacks caused by patch -type or global triggers. Abundant theoretical analysis has demonstrated the feasibility of our scheme. Moreover, extensive experiments show that our method performs well in detecting prevalent backdoor attacks in self-supervised learning, achieving detection accuracy exceeding 95%. © 2004-2012 IEEE.

关键词： Self-supervised learning

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