检索结果-内蒙古大学图书馆

Asia-Pacific Signal and information processing Association Annual Summit and Conference (APSIPA)

作者： Changsheng Chen Xijin Li Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Recently, document images have been widely used in various online applications. Digital watermarking is an important forensic technique to verify the authenticity of a document image. However, the recapturing operation leads to a significant risk in document images since the extraction accuracy of recaptured digital watermarking drops significantly. In this work, we propose robust watermark against halftone distortion by utilizing surrogate models and end-to-end watermarking frameworks. Firstly, we employ differentiable surrogate models to generate the halftone distortion. Then, surrogate models are incorporated into end-to-end watermarking frameworks to enhance the robustness of the watermark in the print-camera scenario. To evaluate the robustness of the proposed method, we conduct a series of experiments in real-world scenarios. The experimental results confirm that our method can improve the robustness of the watermark across different devices, datasets, printing sizes, and watermark capacities.

关键词： Printing Training Performance evaluation Accuracy Forensics Asia Watermarking information processing Distortion Robustness

来源：评论

学校读者我要写书评

暂无评论

Image-to-Class Metric based on Category Traversal for Few-shot Learning 10

Image-to-Class Metric based on Category Traversal for Few-sh...

引用

10th IEEE Joint International information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Yuan, Sihao Li, Qishen Huang, Hua Li, Qiufeng School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Jiangxi China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

The purpose of few-shot learning is to learn a classification model with good generalization performance. When there is only one sample or several samples, the model can also be quickly generalized to new categories. In this paper, we use the method based on metric learning to complete the few-shot learning image classification task. In this method, we introduce a feature learning module to improve the representation ability of the feature extraction network. Through category traversal, the module extracts the intra-class common features and inter-class unique features of images. Finally, by comparing the similarity between the input image and the local descriptors of each category, the classification task is completed by the image-to-class measure. This metric is achieved by k-NN search in the local descriptors, in this way, we can make full use of all the local features of a category, thus expressing the distribution of this class more richly and effectively. © 2022 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Natural image matting based on image inpainting 2

Natural image matting based on image inpainting

引用

2nd International Conference on Computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Zhang, Yuan Tan, Mian Zhou, Zhulian Yang, Yuan Liang, Yihui Feng, Fujian Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang550025 China University of Electronic Science and Technology China School of Computer Science Zhongshan528400 China

ISBN: (纸本)9781665492508

Deep image matting is a hot problem with applications in computer vision and image processing. It has been widely used in image composition, film production and video editing etc. The current matting method based on image inpainting uses a deep neural network to inpaint the foreground target and background region to further improve the accuracy of alpha matte. However, when the trimap contains large unknown regions, the excessive inpainting of foreground and background produce a lot of redundant information, which leads to a degradation of alpha matte quality. Therefore, to address this issue, a natural image matting method based on image inpainting is designed. This method involves the refinement process for trimap, which improves the quality of the trimap, enlarges the foreground and background regions providing additional information for image matting. Extensive experimental results on the composition-1k dataset demonstrate that the presented method provide high-quality alpha mattes not only in the case that the trimap contains small unknown regions, but also in the case that the trimap contains large unknown regions. © 2022 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Detection of Photoplethysmography Manipulation in Video Forgery 31

Detection of Photoplethysmography Manipulation in Video Forg...

引用

31st European Signal processing Conference, EUSIPCO 2023

作者： Lin, Yuzhen Li, Bin Maiorana, Emanuele Campisi, Patrizio Guangdong Key Lab of Intelligent Information Processing Shenzhen Key Lab of Media Security Shenzhen University Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen China Dept. of Industrial Electronic and Mechanical Engineering Roma Tre University Rome Italy

ISBN: (纸本)9789464593600

Recent studies have shown that physiological signals related to blood pressure and heart rate can be estimated in a contactless modality from facial videos using remote photoplethysmography (rPPG). This has paved the way to the development of techniques that can acquire and manipulate the rPPG signals recoverable from facial videos without affecting their visual appearance. The goal of this paper is to analyze the detectability of this new kind of forgery, here referred to as rPPG deepfake videos. Specifically, we propose a two-stream method based on the analysis of rPPG deepfake videos at both spatial and temporal levels. The experimental results obtained from tests performed on samples taken from two distinct databases demonstrate that our method performs better than popular deep learning methods in the rPPG deepfake detection task. © 2023 European Signal processing Conference, EUSIPCO. All rights reserved.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Tackling Sparse Facts for Temporal Knowledge Graph Completion 25

Tackling Sparse Facts for Temporal Knowledge Graph Completio...

引用

34th ACM Web Conference, WWW 2025

作者： Zhang, Yuchao Kong, Xiangjie Ye, Kailun Shen, Guojiang Zheng, Shangfei Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Zhejiang Key Laboratory of Visual Information Intelligent Processing Hangzhou China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798400712746

Temporal knowledge graph completion (TKGC) seeks to develop more comprehensive knowledge representations by addressing missing relationships and entities within temporal knowledge graphs (TKGs), thereby enhancing reasoning and predictive capabilities in downstream tasks. Nonetheless, real-world knowledge—such as the progression of social network interactions and the unfolding of news events—is inherently dynamic, resulting in substantial sparsity issues in TKGs that profoundly impair the performance of TKGC models. To overcome this challenge, we introduce the Adaptive Neighborhood Enhancement Layer (ANEL), a novel module that can be effortlessly integrated into existing TKGC models to substantially elevate the representation quality of sparse entities. ANEL first derives initial entity embeddings through a base model and then uncovers concealed semantic relationships between entities via a latent relation module, enriching the explicit relationships within the knowledge graph. Furthermore, ANEL incorporates an adaptive latent information adjustment component, which dynamically calibrates the influence of latent information based on the entity’s relational structure: entities with fewer connections derive greater benefit from latent information, while entities with denser connections become less dependent on latent augmentation, ensuring precise and resilient representations. We conducted comprehensive experiments on four prominent benchmark datasets, and the results underscore the effectiveness and superiority of ANEL in TKGC tasks. © 2025 Copyright held by the owner/author(s).

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Object-Centric Learning from Multiple Unspecified Viewpoints

arXiv

引用

arXiv 2024年

作者： Yuan, Jinyang Chen, Tonglin Shen, Zhimeng Li, Bin Xue, Xiangyang Key Laboratory of Intelligent Information Processing the School of Computer Science Fudan University Shanghai200433 China

Visual scenes are extremely diverse, not only because there are infinite possible combinations of objects and backgrounds but also because the observations of the same scene may vary greatly with the change of viewpoints. When observing a multi-object visual scene from multiple viewpoints, humans can perceive the scene compositionally from each viewpoint while achieving the so-called "object constancy" across different viewpoints, even though the exact viewpoints are untold. This ability is essential for humans to identify the same object while moving and to learn from vision efficiently. It is intriguing to design models that have a similar ability. In this paper, we consider a novel problem of learning compositional scene representations from multiple unspecified (i.e., unknown and unrelated) viewpoints without using any supervision and propose a deep generative model which separates latent representations into a viewpoint-independent part and a viewpoint-dependent part to solve this problem. During the inference, latent representations are randomly initialized and iteratively updated by integrating the information in different viewpoints with neural networks. Experiments on several specifically designed synthetic datasets have shown that the proposed method can effectively learn from multiple unspecified viewpoints. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

FacialFlowNet: Advancing Facial Optical Flow Estimation with a Diverse Dataset and a Decomposed Model

arXiv

引用

arXiv 2024年

作者： Lu, Jianzhi He, Ruian Zhou, Shili Tan, Weimin Yan, Bo Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Facial movements play a crucial role in conveying altitude and intentions, and facial optical flow provides a dynamic and detailed representation of it. However, the scarcity of datasets and a modern baseline hinders the progress in facial optical flow research. This paper proposes FacialFlowNet (FFN), a novel large-scale facial optical flow dataset, and the Decomposed Facial Flow Model (DecFlow), the first method capable of decomposing facial flow. FFN comprises 9,635 identities and 105,970 image pairs, offering unprecedented diversity for detailed facial and head motion analysis. DecFlow features a facial semantic-aware encoder and a decomposed flow decoder, excelling in accurately estimating and decomposing facial flow into head and expression components. Comprehensive experiments demonstrate that FFN significantly enhances the accuracy of facial flow estimation across various optical flow methods, achieving up to an 11% reduction in Endpoint Error (EPE) (from 3.91 to 3.48). Moreover, DecFlow, when coupled with FFN, outperforms existing methods in both synthetic and real-world scenarios, enhancing facial expression analysis. The decomposed expression flow achieves a substantial accuracy improvement of 18% (from 69.1% to 82.1%) in micro-expressions recognition. These contributions represent a significant advancement in facial motion analysis and optical flow estimation. Codes and datasets can be found here. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical flows

来源：评论

学校读者我要写书评

暂无评论

An Efficient WRF Framework for Discovering Risk Genes and Abnormal Brain Regions in Parkinson's Disease Based on Imaging Genetics Data

引用

Journal of Computer Science & Technology 2021年第2期36卷 361-374页

作者： Xia-An Bi Zhao-Xu Xing Rui-Hui Xu Xi Hu College of Information Science and Engineering Hunan Normal UniversityChangsha 410006China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha 410006China

As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data *** data mining method after fusion and the overall analysis framework are *** this study,we propose a weighted random forest(WRF)model as the feature screening *** interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis *** implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of *** experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are *** fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of *** also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.

关键词： multimodal fusion feature Parkinson's disease pathogenic factor detection sample classification weighted random forest model

来源：评论

学校读者我要写书评

暂无评论

Observer-Based Consensus of Nonlinear Multi-Agent Systems With Input Delay via Delay-Dependent Event-Triggered Control

IEEE Transactions on Industrial Cyber-Physical Systems

引用

IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 157-166页

作者： Wei Zhang Bingyan Gong Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronics and Information Engineering Southwest University Chongqing China

The distributed control for leader-following consensus problem of nonlinear multi-agent systems with input delay is investigated in this paper. Based on observer state information, an improved distributed truncated prediction control strategy is proposed in order to address the issue of input delay. Furthermore, a delay-dependent event-triggered control protocol is designed to alleviate the use of computation resources and communication bandwidth. Unlike the existing event-triggered protocol, the protocol in the article is delay-dependent and specially designed for systems with input delay. Moreover, the Zeno behavior does not occur. It should be pointed out that, our observer-based control is significant for practical applications where the state of agents cannot be directly obtained. Utilizing the Lyapunov-Krasoviskii function theory, the sufficient conditions of the consensus under above two control methods are obtained. Lastly, we illustrate the feasibility of our results by illustrative examples.

关键词： Delays Protocols Observers Multi-agent systems Linear matrix inequalities Sufficient conditions Cyber-physical systems

来源：评论

学校读者我要写书评

暂无评论

DLS-HCAN: Duplex Label Smoothing Based Hierarchical Context-Aware Network for Fine-grained 3D Shape Classification

引用

IEEE Transactions on Multimedia 2025年

作者： Bai, Shaojin Zheng, Liang Bai, Jing Ma, Xiangyu North Minzu University School of Computer Science and Engineering Yinchuan750021 China Liupanshan Laboratory Yinchuan750021 China North Minzu University Key Laboratory of Images Processing and Pattern Recognition LaboratoryCommission: IPPRLab Yinchuan750021 China

Fine-grained 3D shape classification (FGSC) has garnered significant attention recently and has made notable advancements. However, due to high inter-class similarity and intra-class diversity, it is still a challenge for existing methods to capture subtle differences between different subcategories for FGSC. On the one hand, one-hot labels in loss function are too hard to describe the above data characteristics, and on the other hand, local details are submerged in the global features extraction process and final network constraints, impacting classification results. In this paper, we propose a duplex label smoothingbased hierarchical context-aware network for fine-grained 3D shape classification, named DLS-HCAN. Specifically, DLS-HCAN firstly employs a hierarchical context-aware network (HCAN), in which the intra-view context attention mechanism (intra-ATT) and the inter-view context multilayer perceptron (inter-MLP) are designed to focus on and discern the beneficial local details. Subsequently, we propose a novel duplex label smoothing (DLS) regularization in which shape-level and view-level smooth labels are separately applied in two improved loss functions, adapting to the fine-grained data characteristics and considering the varying uniqueness of different views. Notably, our approach does not require additional annotation information. Experimental results and comparison with state-of-the-art methods demonstrate the superiority of our proposed DLS-HCAN for FGSC. In addition, our approach also achieves comparable performance for the coarse-grained dataset on ModelNet40. © 2025 IEEE.

关键词： Coarse-grained modeling

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：