检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

ACDR-CRAFF Net: A Multi-Scale Network Based on Adaptive Channel and Coordinate Relational Attention Network for Remote Sensing Scene Classification

引用

IET Image Processing 2025年第1期19卷

作者： Dai, Wei Xu, Haixia Shi, Furong Yuan, Liming Wang, Xinyu Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Accurate classification of remote sensing scene images is crucial for diverse applications, from environmental monitoring to urban planning. While convolutional neural networks (CNNs) have dramatically improved classification accuracy, challenges remain due to the complex distribution of small objects, varied spatial configurations, and intra-class multimodality in remote sensing images. In this work, we make three key contributions to address these challenges. (1) We propose the adaptive channel and coordinate relational attention network (ACDR-CRAFF), a novel multi-scale feature fusion framework designed to enhance feature representation across scales. (2) We introduce two innovative modules: the adaptive channel dimensionality reduction (ACDR) module, which dynamically adjusts channel representations to retain essential low-dimensional features, and the coordinate relational attention multi-scale feature fusion (CRAFF) module, which effectively captures and transfers spatial information between feature levels. (3) By integrating ACDR and CRAFF, our model achieves a progressive fusion of local to global features, ensuring robust feature expressiveness at multiple scales. Experimental results on four widely used benchmark datasets demonstrate that ACDR-CRAFF consistently outperforms several state-of-the-art methods, achieving significant improvements in classification accuracy and setting a new benchmark for complex remote sensing scene classification tasks. These results underscore the effectiveness of our approach in addressing the limitations of existing methods and advancing the state of the art in remote sensing image analysis. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

An end-to-end instance segmentation method based on improved ConvNeXt V2

引用

International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

来源：评论

学校读者我要写书评

暂无评论

Monocular Depth Estimation with Boundary Attention Mechanism and Shifted Window Adaptive Bins

SSRN

引用

SSRN 2024年

作者： Liang, Mengnan Wang, Congcong Zhao, Meng Shi, Fan Zhang, Chao Han, Yilin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Laboratory of Computer Vision System of Ministry of Education Tianjin University of Technology Tianjin300384 China

Monocular depth estimation is a classic research topic in computer vision. In recent years, development of Convolutional Neural Networks (CNNs) has facilitated significant breakthroughs in this field. However, there still are two challenges: 1) The network struggles to effectively fuse edge features in the feature fusion stage, which ultimately results in the loss of structure or distorted boundaries of objects in the scene. 2) Classification based studies typically depend on Transformers for global modeling, a process that often introduces substantial performance overhead. In this paper, we propose two modules to address the aforementioned issues. The first module is the Boundary Attention Module (BAM), which leverages the attention mechanism to enhance the ability of the network to perceive object boundaries during the feature fusion stage. In addition, to mitigate the performance overhead resulting from predicting adaptive bins, we propose a Shift Window Adaptive Bins (SWAB) module to reduce the amount of calculation in global modeling. The proposed method is evaluated on two public datasets, NYU Depth V2 and KITTI, and achieves state-of-the-art (SOTA) performance. © 2024, The Authors. All rights reserved.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

A Speed Measurement Method of Rail Transit Based on Millimeter-Wave Radar 1

引用

5th International Symposium for Intelligent Transportation and Smart City, ITASC 2022

作者： Shen, Tuo Xie, Lanxin Luo, Jinnan Zeng, Xiaoqing Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Shanghai201804 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Institute of Rail Transit Tongji University Shanghai China

ISBN: (数字)9789819922529

ISBN: (纸本)9789819922512

The growing use of millimeter-wave radars in the intelligent transportation field and increasing accuracy provide new possibilities for train speed measurement. This paper proposes a measuring train speed method by using millimeter-wave radar to detect static objects. The millimeter-wave radar was used to obtain and process objects data. Then, using the RANSAC algorithm to extract the static objects group, and calculated the relative speed of the millimeter-wave radar and the static objects group. Finally, we completed the comparative experiment of using millimeter-wave radar and odometer to measure train speed in a real track environment. The results show that the measurement error of this method is less than 1.5 m/s, and it has the characteristics of low cost, fast speed measurement, and high precision. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Mixed Color Channels (MCC): A Universal Module for Mixed Sample Data Augmentation Methods

Mixed Color Channels (MCC): A Universal Module for Mixed Sam...

引用

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wei, Yang Ma, Jianfeng Jiang, Zhongyuan Xiao, Bin School of Cyber Engineering Xidian University China Shaanxi Key Laboratory of Network and System Security China School of Computer Science and Technology Chongqing University of Posts and Telecommunication China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Color invariance is critical for computer vision systems since it significantly increases the robustness and effectiveness of the system. MSDA approaches (e.g., FMix and CutMix) have attracted considerable attention in recent years since they are simple, effective, and do not require extra computation con-sumption. By mixing samples, these approaches extend the distribution of training samples. However, the color information of these mixed samples is not changed, which makes it still difficult for trained models to achieve color invariance. To address this issue, we propose a universal module called Mixed Color Channels (MCC) that implements color changes by mixing the sample and its color variants, which enables trained models to achieve color invariance. In the experimen-tal section, we insert MCC into four state-of-the-art MSDA approaches, evaluate its effectiveness, and embed MCC into a non-MSDA method to demonstrate its extensibility. © 2022 IEEE.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Ultrahigh-coherence microcomb generation and low-noise amplification using integrated photonics

Ultrahigh-coherence microcomb generation and low-noise ampli...

引用

2024 IEEE Photonics Conference, IPC 2024

作者： Zhang, Xuguang Zhou, Zixuan Guo, Yijun Zhuang, Minxue Jin, Warren Shen, Bitao Chen, Yujun Huang, Jiahui Hu, Weiwei Shu, Haowen Bowers, John E. Wang, Xingjun Chang, Lin University of California Santa Barbara Department of Electrical and Computer Engineering Santa BarbaraCA United States Peking University State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Beijing China Peking University Frontiers Science Center for Nano-Optoelectronics Beijing China

ISBN: (纸本)9798350361957

We demonstrate a high-coherence parallel integrated light source utilizing a self-injection locked microcomb and injection locking amplification. Our strategy exhibits a record high on-chip gain of 60 dB, nearly 0 dB noise factor, which enable more than 30 highly coherent channels in integrated photonics whose linewidths down to 10 Hz, power exceeding 20 dBm per line and a wall plug efficiency of 19%. © 2024 IEEE.

关键词： Photonic integrated circuits

来源：评论

学校读者我要写书评

暂无评论

Robust UAV Policy Learning for Urban Infrastructure Surface Screening*

Robust UAV Policy Learning for Urban Infrastructure Surface ...

引用

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Bingqing Du Uddin Md. Borhan Tianxing Chen Jianyong Chen Jianqiang Li Jie Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Unmanned Aerial Vehicles (UAVs) have emerged as pivotal tools for infrastructure inspection, mitigating risks to human life and enhancing operational efficiency. Despite advancements, achieving autonomous surface coverage inspection in GPS-denied environments remains a significant challenge. This paper presents a novel approach to enhance UAV policy learning for surface screening of urban infrastructure in environments lacking GPS. Utilizing Deep Reinforcement Learning (DRL), the methodology enables UAVs to autonomously conduct surface coverage inspections more robustly and efficiently. A key innovation is the Optimal Speed Decision (OSD) model, which allows UAVs to adjust flight speed dynamically based on scene perception, enhancing efficiency and safety. Additionally, an Off-Policy Critic-Based Reward Shaping (OPCBRS) method is introduced to improve learning efficiency and performance by addressing the issue of sparse rewards using offline data. Simulation results confirm the effectiveness of these methods, with the OSD model demonstrating significant improvements in navigating complex scenarios and maintaining a balance between efficiency and robustness over traditional uniform speed policies. This research offers a robust framework for UAV-based infrastructure inspection in GPS-denied environments, advancing autonomous UAV applications.

关键词： Technological innovation Navigation Simulation Inspection Autonomous aerial vehicles Deep reinforcement learning Robustness Real-time systems Safety Robots

来源：评论

学校读者我要写书评

暂无评论

VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting

arXiv

引用

arXiv 2024年

作者： Yang, Yingnan Zhu, Qingling Chen, Jianyong College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Multivariate time series (MTS) forecasting has been extensively applied across diverse domains, such as weather prediction and energy consumption. However, current studies still rely on the vanilla point-wise self-attention mechanism to capture cross-variable dependencies, which is inadequate in extracting the intricate cross-correlation implied between variables. To fill this gap, we propose Variable Correlation Transformer (VCformer), which utilizes Variable Correlation Attention (VCA) module to mine the correlations among variables. Specifically, based on the stochastic process theory, VCA calculates and integrates the cross-correlation scores corresponding to different lags between queries and keys, thereby enhancing its ability to uncover multivariate relationships. Additionally, inspired by Koopman dynamics theory, we also develop Koopman Temporal Detector (KTD) to better address the non-stationarity in time series. The two key components enable VCformer to extract both multivariate correlations and temporal dependencies. Our extensive experiments on eight real-world datasets demonstrate the effectiveness of VCformer, achieving top-tier performance compared to other state-of-the-art baseline models. Code is available at this repository: https://***/CSyyn/VCformer. Copyright © 2024, The Authors. All rights reserved.

关键词： Random processes

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：