检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Pengju Dai, Dawei Zou, Kai Yang, xionghui Wang, Guoyin Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Normal University College of Computer and Information Science Chongqing401331 China

ISBN: (纸本)9798350386226

Knee Osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is commonly evaluated by observing radiographs of the knee joint However, this approach heavily relies on a doctor's clinical experience and exhibits a certain degree of subjectivity. In previous studies, various advanced deep convolutional neural network (CNN) models have been used to diagnose KOA. As known, CNN models often focus on learning the local detailed features for decision-making and lack attention to global structural information. In this study, we propose a dual network called GraphConvNet that integrates a visual graph neural network with a deep CNN to enhance representation learning by leveraging both local detailed features and global structural information. Our proposed method was evaluated using an Osteoarthritis Initiative (OAI) dataset and achieved an overall accuracy, recall, precision, and mean absolute error (MAE) of 75.24%, 77.75%, 74.33% and 0.283, respectively. Experiments demonstrate that our proposed method significantly improves the performance and achieves state-of-the-art performance. All codes are available at https://***/ddw2AIGROUP2CQUPT/GraphConvNet. © 2024 IEEE.

关键词： Joints (anatomy)

来源：评论

学校读者我要写书评

暂无评论

A Feature Fusion Network with Multiscale Adaptively Attentional for Object Detection in Complex Traffic Scenes

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on intelligent Vehicles 2024年

作者： Cai, Fei Qu, Zhong Yin, Xuehui Chongqing Key Laboratory of Computational Intelligence The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education The Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Currently, small object detection in complex traffic scenarios remains a major challenge due to variations in object shape, scales, and external environment, as well as a high inter-class similarity. Although the latest algorithms achieve high accuracy, the results for small objects are still unsatisfactory. To obtain satisfactory small object detection results, the YOLOv8 algorithm is used to construct a feature fusion network with multiscale adaptively attentional (MAAFFNet). Firstly, the C2f module is modified to replace the simple summation operation with Multiscale Iterative Attentional Feature Fusion (MSIAFF), which enhances the ability of multi-scale aggregation of the C2f module and suppresses the interference of irrelevant information, resulting in a richer semantic feature map. Secondly, the adaptive spatial feature fusion (ASFF) module is introduced to deal with the feature maps of different scales, allocate the weights of spatial features of different levels, and enhance the consistency of semantic information between each layer of the feature maps, so as to improve the detail richness of the network. Finally, to achieve more accurate classification of small objects, a Slide loss function is introduced to achieve object classification and localization. Our proposed algorithm has been extensively experimented on KITTI, COCO-traffic and VOC 2012 datasets, respectively, comparing the proposed method to popular two-stage and single-stage object detection algorithms, the proposed method performs well in terms of accuracy. © 2024 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

OSFS-Vague: Online streaming feature selection algorithm based on vague set

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1451-1466页

作者： Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu School of Physics and Electronic Science Zunyi Normal UniversityZunyiChina Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Computer Science Old Dominion UniversityNorfolkVirginiaUSA College of Computer and Information Science Southwest UniversityChongqingChina

Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional *** real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation ***,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic *** make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named *** main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous ***-Vague also improves the calculation method of correlation between features and ***,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant ***,the relevant features and weakly redundant features are filtered for an optimal feature *** evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 *** results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关键词： feature selection online feature selection three-way decision vague set

来源：评论

学校读者我要写书评

暂无评论

Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

来源：评论

学校读者我要写书评

暂无评论

Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in Talking Head Generation

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Guanwen Jin, Siyu Qian, Zhihao Li, Yunan Miao, Qiguang School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi'an Key Laboratory of Big Data and Intelligent Vision Xidian University Shaanxi Xi'an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9798350368741

Despite significant progress in NeRF-based talking head generation, problems like poor lip synchronization and inefficient resource usage remain. To solve these, we propose KAN-Face, a lightweight framework. In preprocessing, we introduce a Lip-Sync Enhancement Module that uses Wav2Lip to extract high-resolution audio features and map them to an explicit intermediate representation, ensuring precise lip movement alignment with the speaker's identity. These predicted lip-sync features are combined with fundamental audio-extracted lip features and injected into the rendering module to improve synchronization. For rendering, we introduce FastKAN to map spatial points to color values. As a variant of KAN, FastKAN's sensitivity to 3D scenes and efficient structure enable precise, fast color prediction. Our framework reduces resource consumption while enhancing lip-sync accuracy and facial reconstruction, making it ideal for talking head generation tasks in resource-limited settings. Project: https://***/KAN-Face/. © 2025 IEEE.

关键词： Face Animation KAN Network Low Resource Neural Radiance Fields Talking Head Generation

来源：评论

学校读者我要写书评

暂无评论

From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第07期47卷 6073-6087页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Sciences United Kingdom Linkoping University Computer Vision Laboratory Sweden Nanyang Technological University College of Computing & Data Science Singapore China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： Computer aided design

来源：评论

学校读者我要写书评

暂无评论

DFGR: Deep Feature Graph Representation for Predicting Knee Osteoarthritis 4

DFGR: Deep Feature Graph Representation for Predicting Knee ...

引用

4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Xu, Tao Dai, Dawei Wang, Yuqi Li, Gen Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China Shanghai Jiaotong University School of Medicine Department of Orthopedics Ruijin Hospital Shanghai China

ISBN: (纸本)9798350394375

Knee osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is evaluated by observing the X-ray images of the knee joint, which is highly dependent on the subjective experience of the doctor. In this study, we developed a novel deep CNN model that integrates structural information processing to predict KOA severity automatically. Specifically, (1) The knee joint regions of the original X-ray images are segmented using automatic detection;(2) We employed a pre-trained deep CNN models to extract deep features of key regions, and construct their corresponding graph representations;(3) A graph attention network (GAT) was designed as a fine-tuning module to build a KOA prediction model. Experiments demonstrate that our method significantly improves the performance in multiple aspects. In addition, our method demonstrates good interpretability. Codes are available at https://***/ddw2AIGROUP2CQUPT/DFGR © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

On RNN-Based k-WTA Models With Time-Dependent Inputs

引用

IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2034-2036页

作者： Mei Liu Mingsheng Shang the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China

Dear editor,This letter identifies two weaknesses of state-of-the-art k-winnerstake-all(k-WTA)models based on recurrent neural networks(RNNs)when considering time-dependent inputs,i.e.,the lagging error and the infeas... 详细信息

关键词： letter convergence finite

来源：评论

学校读者我要写书评

暂无评论

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning 25

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on ...

引用

34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：