检索结果-内蒙古大学图书馆

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Zhang, Xing Guo, Yucan Jin, Xiaolong Liang, Haozhe Guan, Saiping School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Cas Key Laboratory of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Australia

ISBN: (纸本)9798350358780

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE. © 2023 IEEE.

关键词： Medical computing

来源：评论

学校读者我要写书评

暂无评论

Learning to see speckle in the weak laser field through multimode fiber

引用

Optoelectronics Letters 2025年

作者： JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the Key Laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer Science and Engineering， Tianjin University of Technology

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dense Video Captioning through Convolutional-Transformer Integration 4

Dense Video Captioning through Convolutional-Transformer Int...

引用

4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Cao, Jiaxi Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9798350349252

Dense video captioning, a task that has recently garnered attention in the field of computer vision, focuses on generating detailed captions for multiple events along with their temporal locations within untrimmed videos. Existing transformer-based methods have achieved notable success. However, some methods utilizing deformable transformer face challenges in processing long videos, as the encoder may struggle to capture the long-term dependencies of events in the video. This issue is partly due to not integrating Convolutional Neural Networks (CNN) with the deformable transformer architecture. In this work, we introduce Dense Video Captioning through Convolutional-Transformer Integration (CTDVC), which links Layer Normalization, the Scalable-Granularity Perception (SGP), and Group Normalization into a network, replacing the encoder part of the deformable transformer. This model better accounts for the sequential relationship between events, especially those events in the video that span over a longer duration, allowing it to generate video captions that are not only more coherent but also more precise in their narrative. We evaluated our method on ActivityNet Captions and YouCook2 datasets. Experiments demonstrate that our proposed method CTDVC achieves an improvement in performance. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

STA+: Spatiotemporal Adaptation with Adaptive Model Selection for Video Action Recognition 4

STA+: Spatiotemporal Adaptation with Adaptive Model Selectio...

引用

4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Li, Minghui Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artifical Intelligence Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9798350349252

Recent breakthroughs in video models have achieved remarkable success by integrating vision transformers into the video domain through adaptation. However, prevalent approaches in existing literature often entail a single branch, incorporating additional spatiotemporal learning modules. This approach cannot fully satisfy the unique and targeted learning requirements of temporal and spatial representations. It lacks a well-balanced spatiotemporal understanding of videos, particularly in recognizing human actions, which requires a comprehensive understanding of space and time. In this study, we introduce a novel two-stream architecture named SpatioTemporal Adaptation with Adaptive Model Selection (STA+) to achieve synchronized spatiotemporal understanding of video with only RGB inputs. We propose an adaptive model selection method based on the intrinsic categorization judgment of the samples to enhance overall performance. We validate the effectiveness of our proposed method through rigorous experiments conducted on public benchmarks such as Diving-48 and Something-Something- V2. Our method consistently achieves competitive performance on these datasets. © 2024 IEEE.

关键词： adaptation spatiotemporal Video action recognition

来源：评论

学校读者我要写书评

暂无评论

Finite Dimensional Koopman Form of Polynomial Nonlinear systems*

引用

IFAC-PapersOnLine 2023年第2期56卷 6423-6428页

作者： Lucian C. Iacob Maarten Schoukens Roland Tóth Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process using so-called observable functions. While there is an extensive theory on infinite dimensional representations in the operator sense, there are few constructive results on how to select the observables to realize them. When it comes to the possibility of finite Koopman representations, which are highly important from a practical point of view, there is no constructive theory. Hence, in practice, often a data-based method and ad-hoc choice of the observable functions is used. When truncating to a finite number of basis, there is also no clear indication of the introduced approximation error. In this paper, we propose a systematic method to compute the finite dimensional Koopman embedding of a specific class of polynomial nonlinear systems in continuous-time, such that the embedding can fully represent the dynamics of the nonlinear system without any approximation.

关键词： Nonlinear systems Koopman operator Linear embedding

来源：评论

学校读者我要写书评

暂无评论

Adversarial attack method with adaptive step size for ID card text recognition 4

Adversarial attack method with adaptive step size for ID car...

引用

4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Feng, Weidong Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9798350349252

In the field of text recognition models, existing adversarial attack algorithms mainly target white-box scenarios, which are usually limited to single-color backgrounds and short text length. However, with the complex color textures of ID card images and long text length, these methods are not satisfactory when performing adversarial attacks on ID card text data. To overcome these challenges, this paper introduces a novel black-box attack method tailored for ID card text recognition models. The proposed approach accounts for the complexities of intricate image backgrounds, extended text data, and the limitations inherent in black-box conditions. The proposed algorithm introduces an adaptive step-size search method based on line search, aiming to overcome the limitations of traditional methods that rely on fixed step sizes and projection techniques for perturbation generation. This refinement aims to enhance the success rate of black-box attacks while maintaining efficacy in white-box scenarios. Spatial transformations and random noise are incorporated to fortify the robustness of adversarial samples. Experimental results, conducted under various norms, demonstrate that the fusion of the traditional method with the adaptive step-size search method yields superior attack performance. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Greedy Sparse Underdetermined Blind Separation Algorithm for LEO Satellite Communication System

A Novel Greedy Sparse Underdetermined Blind Separation Algor...

引用

2022 International Symposium on Networks, computers and Communications, ISNCC 2022

作者： Li, Chengjie Zhu, Lidong Guo, Xianfeng Zhang, Zhen Yang, Ying Southwest Minzu University The Key Laboratory for Computer Systems of State Ethnic Affairs Commission School of Computer Science and Technology Chengdu China University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications China Sichuan University China

ISBN: (数字)9781665485449

ISBN: (纸本)9781665485449

An important characteristic of a low earth orbit (LEO) satellite communication system is a large number of satellites. Therefore, it is common that the number of receiving antennas is less than that of transmitting antennas. In this scenario, to save resources and improve spectral efficiency, an underdetermined blind separation algorithm can be considered. In this paper, under the underdetermined condition, the number of source signals is greater than that of the mathematical constraint equation, and the prior knowledge or characteristics of the source signal are usually used to add constraints. In order to obtain better signal processing performance, and combined with the information transmission characteristics of LEO satellite communication system, a greedy optimization algorithm can be executed. In order to improve the algorithm performance, during algorithm iteration, the sampling points with the highest cost performance are selected and added to the sample set at each time. The simulation results show that the algorithm has good performance. © 2022 IEEE.

关键词： Satellite communication systems

来源：评论

学校读者我要写书评

暂无评论

Space-Filling Input Design for Nonlinear State-Space Identification ⁎

引用

IFAC-PapersOnLine 2024年第15期58卷 562-567页

作者： Máté Kiss Roland Tóth Maarten Schoukens Control System group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modeling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this paper focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals. The presented experiment design approach can straightforwardly be extended to a wider range of system classes (e.g., NARMAX). The effectiveness of the proposed approach is illustrated on the experiment design for a nonlinear mass-spring-damper system, using a multisine input signal.

关键词： Experiment Design Nonlinear System Identification State-Space

来源：评论

学校读者我要写书评

暂无评论

Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution Supplementary Material

引用

IEEE Transactions on Image Processing 2025年 34卷 3013-3024页

作者： Yuan, Jiang Ma, Ji Wang, Bo Hu, Weiming North China Electric Power University School of Control and Computer Engineering Beijing102206 China CASIA State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. How to extract more discriminative degradation representations and fully adapt them to specific image features is the key to this task. In this paper, we propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework following the typical blind SR pipeline. This framework introduces negative-free contrastive learning technique for the first time to model the implicit degradation representation, in which a new cyclic shift sampling strategy is designed to ensure decoupling between content features and degradation features from the data perspective, thereby improving the purity and discriminability of the learned implicit degradation space. In addition, we propose a detail-aware implicit degradation adapting module that can better adapt degradation representations to specific LR features by enhancing the basic adaptation unit's perception of image details, significantly reducing the overall SR model complexity. Extensive experiments on synthetic and real data show that our method achieves highly competitive quantitative and qualitative results in various degradation settings while obviously reducing parameters and computational costs, validating the feasibility of designing practical and lightweight blind SR tools. Codes and models will be available at https://***/Fieldhunter/CdCL. © 1992-2012 IEEE.

关键词： Depression Anxiety disorders Face recognition Schizophrenia Visualization Training Data collection Uncertainty Facial features Emotion recognition

来源：评论

学校读者我要写书评

暂无评论

PointPillars Backbone Type Selection for Fast and Accurate LiDAR Object Detection

PointPillars Backbone Type Selection for Fast and Accurate L...

引用

International Conference on computer Vision and Graphics, ICCVG 2022

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031220241

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars-backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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：