检索结果-内蒙古大学图书馆

32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Chunjie Du, Lina Gao, Zan Zhuo, Li Wang, Meng Shandong Jinan China School of Computer Science and Technology Shandong Jianzhu University Shandong Jinan China Faculty of Information Technology Beijing University Of Technology Beijing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798400706868

Currently, Transformer-based prohibited object detection methods in X-ray images appear constantly, but there are still some shortcomings such as poor performance and high computational complexity for prohibited object detection with heavily occlusion. Therefore, a coarse to fine detection method for prohibited object in X-ray images based on progressive Transformer decoder is proposed in this paper. Firstly, a coarse to fine framework is proposed, which includes two stages: coarse detection and fine detection. Through adaptive inference in stages, the computational efficiency of the model is effectively improved. Then, a position and class object queries method is proposed, which improves the convergence speed and detection accuracy of the model by fusing the position and class information of prohibited object with object queries. Finally, a progressive Transformer decoder is proposed, which distinguishes high and low score queries by decreasing confidence thresholds, so that high-score queries are not affected by low-score queries in the decoding stage, and the model can focus more on decoding low-score queries, which usually correspond to prohibited object with severe occlusion. The experimental results on three public benchmark datasets (SIXray, OPIXray, HiXray) demonstrate that compared with the baseline DETR, the proposed method achieves the state-of-the-art detection accuracy with a 21.6% reduction in model computational complexity. Especially for prohibited objects with heavily occlusion, accurate detection can be carried out. © 2024 ACM.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation 39

SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Fe...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Peng, Shi-Feng Sun, Guolei Li, Yong Wang, Hongsong Xie, Guo-Sen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Computer Vision Laboratory ETH Zurich Switzerland School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)157735897X

The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain disparity between the training and inference phases, which can exist in either the input data or the target classes. Previous models struggle to learn feature representations that generalize to various unknown domains from limited training domain samples. In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability. In this work, we propose a SAM-aware graph prompt reasoning network (GPRN) that fully leverages SAM to guide CD-FSS feature representation learning and improve prediction accuracy. Specifically, we propose a SAM-aware prompt initialization module (SPI) to transform the masks generated by SAM into visual prompts enriched with high-level semantic information. Since SAM tends to divide an object into many sub-regions, this may lead to visual prompts representing the same semantic object having inconsistent or fragmented features. We further propose a graph prompt reasoning (GPR) module that constructs a graph among visual prompts to reason about their interrelationships and enable each visual prompt to aggregate information from similar prompts, thus achieving global semantic consistency. Subsequently, each visual prompt embeds its semantic information into the corresponding mask region to assist in feature representation learning. To refine the segmentation mask during testing, we also design a non-parameter adaptive point selection module (APS) to select representative point prompts from query predictions and feed them back to SAM to refine inaccurate segmentation results. Experiments on four standard CD-FSS datasets demonstrate that our method establishes new state-of-the-art results. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

引用

Frontiers of Structural and Civil engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

来源：评论

学校读者我要写书评

暂无评论

Two-step Thompson Sampling for Multi-Armed Bandit Problem 43

Two-step Thompson Sampling for Multi-Armed Bandit Problem

引用

43rd Chinese Control Conference, CCC 2024

作者： Shang, Zhigang Yang, Lifang Li, Mengmeng Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

ISBN: (纸本)9789887581581

An important feature of sequential decision-making under uncertain conditions is to strike a balance between exploration (obtaining information about the value of actions) and utilization (selecting the best action based on existing knowledge). The multi-armed bandit is a classic task for balancing their relationship. As the agent has learnt the relationship between two options in a 2-armed bandit task, it will no longer explore but instead use them directly. Inspired by the research, an algorithm based on two-step Thompson sampling (T-TS) is proposed to solve multi-armed bandit problem. First, posterior Beta distributions for the reward of each arm and the probability of the superior arm were established respectively. In the first-step TS, Thompson sampling was used to extract the maximum value from the posterior distribution of probability for the superior arm, determining it as the selected arm. In the two-step TS, Thompson sampling was used to sample from the distributions of the reward. During the updating, the posterior distribution of the probability for the superior arm was updated based on observations including actions and reward, and the distribution of the reward was updated based on the reward feedback. We performed the comparative experiments to solve the static multi-bandit tasks with 10, 20, 40, and 80 arms respectively by the state-of-the-art B-UCB algorithm, Thompson sampling and the proposed T-TS. The results shown that our proposed T-TS can learn the superior action more quickly with the best robustness. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Importance sampling

来源：评论

学校读者我要写书评

暂无评论

A CNN-LSTM Network for Classification of Attention Deficit Hyperactivity Disorder from EEG Data 41

A CNN-LSTM Network for Classification of Attention Deficit H...

引用

第41届中国控制会议

作者： Yaru Zhang Xiangxin Tan Yue Gu Key Laboratory of Computer Vision and System (Ministry of Education) School of Computer Science and EngineeringTianjin University of Technology Engineering Research Center of Learning-Based Intelligent system Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Attention deficit hyperactivity disorder(ADHD) is one of the most common neuropsychiatric disorders in children,with core symptoms of inattention,impulsivity,and *** a result,early detection and treatment are *** this point,ADHD can be diagnosed using an electroencephalogram(EEG).This study proposes a model for identifying the EEG of children with *** proposed model is made up of three parts:temporal convolutional blocks,spatial convolutional blocks,and Long Short Term Memory(LSTM),and because EEG contains more temporal information than other data,we build a model that can extract more temporal *** study included 24 subjects,including 12 children with ADHD and12 normal *** experimental results show that our proposed model outperforms Support Vector Machine(SVM) and EEGNet,with our model achieving correct classification rates of 92.29%,92.76%,and 90.91% on the three backs,respectively,and 94.25% on the full data,demonstrating the model's feasibility and applicability to the ADHD recognition problem.

关键词： Attention Deficit Hyperactivity Disorder Electroencephalogram Deep learning Classification

来源：评论

学校读者我要写书评

暂无评论

Decomposition-based Multi-objective Genetic Programming for Feature Learning in Image Classification

Decomposition-based Multi-objective Genetic Programming for ...

引用

2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Zhang, Tuo Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University China State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical and Information Engineering Zhengzhou University Luoyang China School of Electrical and Information Engineering Zhengzhou University School of Electrical Engineering and Automation Henan Institute of Technology State Key Laboratory of Intelligent Agricultural Power Equipment China School of Engineering and Computer Science Centre for Data Science and Artificial Intelligence Victoria University of Wellington New Zealand

ISBN: (纸本)9798400704956

Image classification is challenging due to the high dimensionality and large variations of the image data. As an effective method in image classification, feature learning can be considered a multiobjective problem of maximizing the classification accuracy and minimizing the number of learned features. Existing multi-objective genetic programming (MOGP) methods directly apply the multiobjective techniques to GP without considering the characteristics of feature learning tasks. Therefore, this paper proposes a decomposition-based MOGP approach with a global replacement strategy to feature learning in image classification. The proposed approach is compared with existing MOGP methods and the experimental results demonstrate the effectiveness of the proposed approach. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Striatal Local Field Potential Dynamics During Decision-Making Learning in Pigeons 24

Striatal Local Field Potential Dynamics During Decision-Maki...

引用

16th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2024

作者： Jin, Fuli Wang, Yinghui Shang, Zhigang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

ISBN: (纸本)9798400717666

Decision-making is a complex cognitive process vital for the survival and adaptive behavior of species, and the neural mechanisms behind this behavior have been the focus of extensive *** striatum is a key region in the brain that plays a crucial role in the decision-making process, making it an ideal candidate for investigating the neural correlates of learning and *** study aims to explore the changes in the striatum and its local field potentials (LFPs) during the learning of decision-making, providing potential insights into the role of the striatum in decision-making in *** study simultaneously collected behavior data and striatal LFPs from 4 pigeons during a three-armed bandit *** pigeons' learning process, behavioral accuracy, reward probabilities, and"win-stay-lose-shift" characteristics were *** power changes in six different frequency bands of striatal LFPs between the early and late stages of learning were *** pigeons' learning process significantly modulated the power in the Beta frequency band, indicating its crucial role in ***, the power of the Theta, Alpha, and Beta bands correlated with choices suggesting a high reward probability, emphasizing the relationship between reward probability and the modulation of power in the *** study sheds light on the neural processes underlying decision-making in pigeons, demonstrating the modulation of LFP power in the striatum and its relationship to reward *** findings contribute to our understanding of decision-making processes in animals. © 2024 Copyright held by the owner/author(s).

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

引用

computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Economical generation of high-quality optical vortices with gradual-width Fermat spiral slit mask

引用

science China(Physics,Mechanics & Astronomy) 2023年第4期66卷 75-83页

作者： Qi Huang Xingyuan Lu Hao Zhang Zhuoyi Wang Yuanjie Yang Qiwen Zhan Yangjian Cai ChengLiang Zhao School of Physical Science and Technology Soochow UniversitySuzhou 215006China School of Physics University of Electronic Science and Technology of ChinaChengdu 610054China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shandong Provincial Engineering and Technical Center of Light Manipulations&Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and ElectronicsShandong Normal UniversityJinan 250358China

A general model for creating an optical vortex using a variable-width Fermat spiral slit(FSS)mask was *** generated optical vortex demonstrates much better quality than those produced by slits with a uniform *** depth of focus was nearly doubled,which can be explained by discretizing the gradual-width *** demonstrate the generation robustness,the slits were printed on a transparent slide,and an output vortex beam with greater than 98%intensity purity was *** the high quality and simplicity of this vortex generation method,its analysis and generation scheme are expected to be extended to generation of plasmonic vortices and short-wavelength light sources such as X-rays and electron *** of highquality optical vortex beams at an almost negligible cost will make investigation much more accessible.

关键词： optical vortex gradual-width Fermat spiral slit high quality economical generation

来源：评论

学校读者我要写书评

暂无评论

DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

引用

CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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：