检索结果-内蒙古大学图书馆

2020 5th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Zhang, Zuoming Wang, Hanwen Shu, Man Wang, Xin College of Software Engineering Jilin University Changchun Jilin130000 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin130000 China

In this paper, we design a hybrid (semi-direct) approach to simultaneous localization and mapping (SLAM) for monocular cameras and apply it to augmented reality (AR) for monocular cameras. We combine the advantagesof the direct method and the feature point method. We use both photometric bundle adjustment which is robust to camera exposure time and motion bundle adjustment which is geometrically robust based on feature points to do tracking process. This approach can maintain an intuitive direct local map as well as a reusable global sparse feature point map. Through the processing of point clouds, such as PCA plane detection and grid reconstruction, we greatly improve the effect of the augmented reality system. © Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal Transformer for Data Inference and Long Prediction in Sparse Mobile CrowdSensing

Spatiotemporal Transformer for Data Inference and Long Predi...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： En Wang Weiting Liu Wenbin Liu Chaocan Xiang Bo Yang Yongjian Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science Chongqing University China

Mobile CrowdSensing (MCS) is a data sensing paradigm that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has been further proposed for large-scale and fine-grained sensing task with the advantage of collecting only a few data to infer unsensed data. However, in many real-world scenarios, such as early prevention of epidemic, people are interested in not only the data at the current, but also in the future or even long-term future, and the latter may be more important. Long-term prediction not only reduces sensing cost, but also identifies trends or other characteristics of the data. In this paper, we propose a spatiotemporal model based on Transformer to infer and predict the data with sparse sensed data by utilizing spatiotemporal relationships. We design a spatiotemporal feature embedding to embed the prior spatiotemporal information of sensing map into the model to guide model learning. Moreover, we also design a novel multi-head spatiotemporal attention mechanism to dynamically capture spatiotemporal relationships among data. Extensive experiments have been conducted on three types of typical urban sensing tasks, which verify the effectiveness of our proposed algorithms in improving the inference and long-term prediction accuracy with the sparse sensed data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Application of Monocular Direct Vision Odometry in Augmented Reality 5

Application of Monocular Direct Vision Odometry in Augmented...

引用

2020 5th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Zhang, Zuoming Wang, Zixuan Wang, Hanwen Wang, Xin College of Software Engineering Jilin University Changchun Jilin130000 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin130000 China

In recent years, the unlabeled augmented reality system has been gradually applied to various mobile devices, among which stable, accurate, and fast registration is the key to realizing this function. For this technique, this paper introduces camera exposure parameters and puts the data association and pose estimation into a unified nonlinear optimization problem. Moreover, the direct monocular vision odometer is transplanted into the augmented reality system through the position adjustment module. We compare it with the traditional visual odometry method that matches the feature points. The results show that this improved method can be used to track more quickly and build a more visual semi-dense point cloud map, which can be used to support the registration and tracking of virtual objects in augmented reality. © Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection 29

A Coarse-to-fine Cascaded Evidence-Distillation Neural Netwo...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Yang, Zhiwei Ma, Jing Chen, Hechang Lin, Hongzhan Luo, Ziyang Chang, Yi College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Hong Kong Baptist University Hong Kong School of Artificial Intelligence International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Optimizing Human Pose Estimation Through Focused Human and Joint Regions

arXiv

引用

arXiv 2025年

作者： Jiao, Yingying Wang, Zhigang Liu, Zhenguang Fan, Shaojing Wu, Sifan Wu, Zheqi Xu, Zhuoyue College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China School of Computing National University of Singapore Singapore

Human pose estimation has given rise to a broad spectrum of novel and compelling applications, including action recognition, sports analysis, as well as surveillance. However, accurate video pose estimation remains an open challenge. One aspect that has been overlooked so far is that existing methods learn motion clues from all pixels rather than focusing on the target human body, making them easily misled and disrupted by unimportant information such as background changes or movements of other people. Additionally, while the current Transformer-based pose estimation methods has demonstrated impressive performance with global modeling, they struggle with local context perception and precise positional identification. In this paper, we try to tackle these challenges from three aspects: (1) We propose a bilayer Human-keypoint Mask module that performs coarse-to-fine visual token refinement, which gradually zooms in on the target human body and keypoints while masking out unimportant figure regions. (2) We further introduce a novel deformable cross attention mechanism and a bidirectional separation strategy to adaptively aggregate spatial and temporal motion clues from constrained surrounding contexts. (3) We mathematically formulate the deformable cross attention, constraining that the model focuses solely on the regions centered at the target person body. Empirically, our method achieves state-of-the-art performance on three large-scale benchmark datasets. A remarkable highlight is that our method achieves an 84.8 mean Average Precision (mAP) on the challenging wrist joint, which significantly outperforms the 81.5 mAP achieved by the current state-of-the-art method on the PoseTrack2017 dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Incomplete Graph Learning: A Comprehensive Survey

arXiv

引用

arXiv 2025年

作者： Xia, Riting Liu, Huibo Li, Anchen Liu, Xueyan Zhang, Yan Zhang, Chunxu Yang, Bo College of Computer Science Inner Mongolia University Hohhot010021 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields.1 Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Noisetrans: Point Cloud Denoising with Transformers

SSRN

引用

SSRN 2022年

作者： Hou, Guangzhe Qin, Guihe Sun, Minghui Liang, Yanhua Yan, Jie Zhang, Zhonghan Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University 2699 Chaoyang District Changchun Jilin130012 China

Introduction: Point clouds obtained from capture devices or 3D reconstruction techniques are often noisy and interfere with downstream ***: The paper aims to recover the underlying surface of noisy point ***: We design a novel model, NoiseTrans, which uses transformer encoder architecture for point cloud denoising. Specifically, we obtain structural similarity of point-based point clouds with the assistance of the transformer's core self-attention mechanism. By expressing the noisy point cloud as a set of unordered vectors, we convert point clouds into point embeddings and employ Transformer to generate clean point clouds. To make the Transformer preserve details when sensing the point cloud, we design the Local Point Attention to prevent the point cloud from being over-smooth. In addition, we also propose sparse encoding, which enables the Transformer to better perceive the structural relationships of the point cloud and improve the denoising ***: Experiments show that our model outperforms state-of-the-art methods in various datasets and noise environments. © 2022, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Generative Classifier for Domain Generalization

arXiv

引用

arXiv 2025年

作者： Long, Shaocong Zhou, Qianyu Li, Xiangtai Ying, Chenhao Tong, Yunhai Ma, Lizhuang Luo, Yuan Tao, Dacheng Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Nanyang Technological University Singapore Peking University Beijing China

Domain generalization (DG) aims to improve the generalizability of computer vision models toward distribution shifts. The mainstream DG methods predominantly focus on learning domain invariance across domains, however, such methods overlook the untapped potential inherent in domain-specific information. While the prevailing practice of discriminative linear classifier has been tailored to domain-invariant features, it struggles when confronted with diverse domain-specific information, e.g., intra-class shifts, that exhibits multi-modality. To address these issues, we explore the theoretical implications of relying on domain-invariant features, revealing the crucial role of domain-specific information in mitigating the target risk for DG. Drawing from these insights, we propose Generative Classifier-driven Domain Generalization (GCDG), introducing a generative paradigm for the DG classifier based on Gaussian Mixture Models (GMMs) for each class across domains. GCDG consists of three key modules: Heterogeneity Learning Classifier (HLC), Spurious Correlation Blocking (SCB), and Diverse Component Balancing (DCB). Concretely, HLC attempts to model the feature distributions and thereby capture valuable domain-specific information via GMMs. SCB identifies the neural units containing spurious correlations and perturbs them, mitigating the risk of HLC learning irrelevant spurious patterns. Meanwhile, DCB ensures a balanced contribution of components within HLC, preventing the underestimation or neglect of critical components. In this way, GCDG excels in capturing the nuances of domain-specific information characterized by diverse distributions. GCDG demonstrates the potential to reduce the target risk and encourage flat minima, improving the model’s generalizability. Extensive experiments show GCDG’s comparable performance on five DG benchmarks and one face anti-spoofing dataset, seamlessly integrating into existing DG methods with consistent improvements. Code will be avail

关键词： Discriminant analysis

来源：评论

学校读者我要写书评

暂无评论

Texture Features and Machine Learning Based Environmental Microorganism Microscopic Image Classification

Texture Features and Machine Learning Based Environmental Mi...

引用

International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Ouyang, Xinyu Yuan, Huaqian Zhou, Shangqi Fu, Yanyu Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

In recent years, with the continuous development of molecular biology technology, the effective use of environmental microorganisms to solve the problem of pollution in the ecological environment has been widely used. However, the original microbial cultivation, environmental application and monitoring have the disadvantages of high degree of specialization, long time and high cost. In this case, the use of computer image processing and machine learning techniques for microbial identification and classification becomes a more efficient, economical and necessary method. This paper presents an environmental microorganism microscopic image classification method based on texture features and machine learning. In the research and experiments, microscopic images of microorganisms are cropped, grayed, data augmented, target areas segmented and extracted four types of texture features. The training set and test set are divided in a 1:1 ratio, and different recognition results are obtained based on different texture feature vectors and classifier models. Ultimately, the gradient-gray co-occurrence matrix feature combined with the Random Forest algorithm classifier achieves an F1-measure value of nearly 40%, which improves the work efficiency of environmental microbial researchers and related field workers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microorganisms

来源：评论

学校读者我要写书评

暂无评论

Hybrid features and semantic reinforcement network for image forgery detection

Hybrid features and semantic reinforcement network for image...

引用

作者： Chen, Haipeng Chang, Chaoqun Shi, Zenan Lyu, Yingda College of Computer Science and Technology Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Public Computer Education and Research Center Jilin University Changchun130012 China

Image forgery detection focuses more on tampering regions than image content of semantic segmentation, it is revealed that wealthier features need to be learned. Moreover, insufficient semantic information causes low efficiency of forgery detection. To address these issues, we propose a hybrid features and semantic reinforcement network (HFSRNet) for image forgery detection, which is an encoding and decoding based network. Specifically, long-short term memory with resampling features has been applied to capture traces from the image patches for finding manipulating artifacts. Consolidated features extracted from rotating residual units are further leveraged to amplify the discrepancy between un-tampered and tampered regions. We then hybridize features from them through a concatenation to further incorporate spatial co-occurrence of these two modalities. In addition, for achieving the semantic consistency between two same level features associated by across layers, semantic reinforcement is implemented on the decoding stage. HFSRNet is an end-to-end architecture that handles multiple types of image forgery including copy-move, splicing, removal. Experiments on three standard image manipulation datasets (NIST16, COVERAGE and CASIA) demonstrate that HFSRNet obtains state-of-the-art performance compared to existing models and baselines. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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：