检索结果-内蒙古大学图书馆

2022 2nd International Conference on Artificial Intelligence, big data and Algorithms, CAIBDA 2022

作者： Hang, Ruishan Gu, Shenming Tang, Zhibo Tan, Xiaoqiu School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9783800760268

Single image dehazing is to remove haze from degraded images and recover clean scenes. The researches of dehazing have academic significance and application value. In this paper, we design a Multi-Pyramid Dehazing Network (MPDN) with Residue Channel Enhancement (RCE). A RCE module was designed to help the network model remove a large amount of homogeneous haze. To further improve the effect of eliminating heterogeneous haze, the Pyramid Feature Extraction (PFE) module is used. The two modules complement each other to achieve excellent haze removal effect. Finally, we do a lot of experiments to verify the model, and compare it with the state-of-the-art (SOTA) method on the benchmark datasets. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Demulsification

来源：评论

学校读者我要写书评

暂无评论

Using Machine Learning with Pyspark for Solving a big data Problem: Searching for Particles in High Energy Physics with big Mass (HEPMASS) 7th

Using Machine Learning with Pyspark for Solving a Big Dat...

引用

7th International Conference on Smart City Applications, SCA 2022

作者： Azhari, Mourad Ahaji, Khalid Abarda, Abdallah Ettaki, Badia Zerouaoui, Jamal Center of Guidance and Planning Rabat Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and Knowledge Engineering School of Information Sciences Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031268519

The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the big data problem in the exotic particle area using the Apache Spark environment with the MLlib library. For this purpose, we analyze and discuss the performance of the following methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). The results are very encouraging and prove that these proposed methods run efficiently with an Accuracy exceeding (83%). While the Gradient Boosted Tree (GBT) reaches a high AUC score 85%, better than Decision Tree (DT) and Random Forest. In this manuscript, we attempt to solve a big data problem using the "HEPMASS" dataset. Our data includes 10.5 million examples collected from the UCI website for the experimental phase. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

StyleMI: An Image Processing-Based Method for Detecting Unauthorized Style Mimicry in Fine-Tuned Diffusion Models in a More Realistic Scenario

引用

IEEE Access 2025年 13卷 97364-97375页

作者： Xinjun Zhang Shixin Zuo Yu Sang School of Electronics and Information Engineering Liaoning Technical University Huludao China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao Liaoning China

The rapid development of diffusion models and model fine-tuning methods have enabled widespread applications in artistic style mimicry while also leading to significant concerns about copyright infringement. Existing approaches protect artists from unauthorized style mimicry by introducing adversarial examples, watermarks, or membership inference attacks. However, the former two are vulnerable to image pre-processing and unable to be applied to published artworks, while the latter requires access to the target model, which is typically inaccessible in real-world scenarios. To this end, the task of detecting unauthorized style mimicry without access to the target model is formulated into a novel membership inference problem. The key insight is that images generated by a diffusion model inherently carry information of its training dataset. A style comparison-based method is proposed, which consists of a LoRA-based style transfer module and a CLIP-based style extraction and comparison module. The style transfer module first learns the artist’s artistic style, then removes the style of the given image to avoid its influence, and finally transfers the artist’s style to the given image. The style extraction and comparison module utilizes a diffusion model to extract the content information of an image and then remove it in a latent semantic space with the pre-trained CLIP model to acquire style-related features and compare them. Experiments on Stable Diffusion demonstrate the effectiveness of the proposed method. The best result achieved a True Positive Rate of 85%, a False Positive Rate of 0%, an Attack Success Rate of 99.29%, a Precision of 100%, and an Area Under the Curve of 1.

关键词： Diffusion models Training Feature extraction data mining Image synthesis data models Copyright protection Computational modeling Watermarking Perturbation methods

来源：评论

学校读者我要写书评

暂无评论

Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification 39

Bidirectional Logits Tree: Pursuing Granularity Reconcilemen...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

ISBN: (纸本)157735897X

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis

arXiv

引用

arXiv 2023年

作者： Haoran, Sun Yang, Wang Haipeng, Liu Biao, Qian Meng, Wang Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei China Department of Computer Science and Information Engineering Hefei University of Technology Hefei China

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel Fine-grained text-image Fusion based Generative Adversarial Networks, dubbed FF-GAN, which consists of two modules: Fine-grained text-image Fusion Block (FF-Block) and Global Semantic Refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given *** is available at htt s:// ithub com/haoranhfut/FF-GAN Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strips

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strip...

引用

2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： He, Chao Yang, Nan Leung, Kwok Wa Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Kowloon Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China

ISBN: (纸本)9798350355581

A four-port multi-input multi-output (MIMO) dielectric resonator (DR) antenna (DRA) is presented in this paper. Combing four rectangular DRs together can form a single DR element. With shape modification and conformal strips, the four ports can be decoupled. To verify the design idea, a transparent prototype can be designed and fabricated for 2.4-GHz band applications. The measured results show an impedance bandwidth of 5.8% and an isolation level of 24 dB. © 2024 IEEE.

关键词： Microwave antennas

来源：评论

学校读者我要写书评

暂无评论

VIPNet: Combining Viewpoint information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

Real-time stealth transmission via dither-based bias control

Real-time stealth transmission via dither-based bias control

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wang, Yuanxiang Shao, Weidong Zhong, Linsheng Dai, Xiaoxiao Yang, Qi Deng, Lei Liu, Deming Cheng, Mengfan Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

We report a real-time 1 kbps stealthy transmission in the 10 Gbps QPSK public communication. The stealth data is embedded in dither signals of bias control. The scheme is compatible with existing optical transmission ... 详细信息

ISBN: (纸本)9781957171258

关键词： Light transmission

来源：评论

学校读者我要写书评

暂无评论

Deep Clustering Model for Time-Series data Based on Recurrence Plot and Variational Auto-Encoder 3

Deep Clustering Model for Time-Series Data Based on Recurren...

引用

3rd International Conference on Digital Signal and computer Communications, DSCC 2023

作者： Xu, Cheng Chen, Junjie College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Huhhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Hohhot010018 China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

Time-series data are trendy and periodic, and have high data dimensionality, the current clustering methods cannot effectively target these characteristics. A recurrence plot variational auto-encoder deep clustering (RPVAEDC) model based on recurrence plot and variational auto-encoder is proposed to address the characteristics of time-series data. The time-series data are first transformed into recurrence plots to reveal their trends and periodicity;then the recurrence plots are fed into a deep clustering model for feature extraction and dimensionality reduction, and the distribution of the transformed data is normalized by variational auto-encoder;then the clustering results are obtained by adding a clustering layer to combine the auto-encoder reconstruction loss and clustering loss. It is experimentally demonstrated that the silhouette coefficient scores are achieved significantly better than other clustering algorithms on the public data sets. © 2023 SPIE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

A Straw Coverage Rate Calculation Method Based on Single Image Processing 2

A Straw Coverage Rate Calculation Method Based on Single Ima...

引用

2nd International Conference on Artificial Intelligence, Human-computer Interaction and Robotics, AIHCIR 2023

作者： Wu, Shiqi Guo, Xi Li, Jie Wulamu, Aziguli Li, Tian Zou, Qiping Xia, Chenglong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Beijing Agricultural Machinery Manager IoT Technology Co. Ltd Beijing China Hechi University Key Laboratory of Ai and Information Processing Hechi China

ISBN: (纸本)9798350360363

This paper proposes a straw coverage rate calculation method based on single-image processing. This method uses a convolutional neural network (CNN) to extract features from images of fields covered with straw, classifies the image based on the features, and then uses an image binarization method to distinguish the straw and the land parts in the image and calculate the straw coverage rate based on the proportion of the straw part. This method has been applied in the land classification project in a particular region. These results demonstrate that this method effectively calculates straw coverage rates and can be used in other fields of precision agriculture. © 2023 IEEE.

关键词： Image recognition

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：