检索结果-内蒙古大学图书馆

Edge-aware Feature Aggregation Network for Polyp Segmentation

Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

来源：评论

学校读者我要写书评

暂无评论

A survey on cross-user federated recommendation

引用

science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

来源：评论

学校读者我要写书评

暂无评论

Deep learning based on hand pose estimation methods: a systematic literature review

引用

Multimedia Tools and Applications 2025年 1-38页

作者： Roumaissa, Bekiri Mohamed Chaouki, Babahenini Computer Science Department LESIA Laboratory Mohamed Khider University Biskra07000 Algeria

Estimating hand pose is a challenge that has significantly benefited from using deep learning-based algorithms. This study area holds critical significance across various computer vision and robotics domains, including applications in sign language interpretation, computer-Aided Design (CAD), and 3D humanoid reconstruction systems. These technologies flourish in augmented reality systems, facilitating immersive interactions within virtual reality contexts. The complexity of human hand anatomy and its intricate range of motions intensifies the difficulty of accurate pose estimation, posing significant academic and technical hurdles. Recent advancements have seen the emergence of rapid and comprehensive methods for hand pose estimation, driven by advancements in-depth camera technology and Deep Neural Networks (DNNs). The previous surveys studied most of these hand pose issues, including hand parsing, data labeling technologies, hand motion, fingertip detection, hand localization, and self-occlusion. This paper addresses the aforementioned challenges in hand pose estimation. Further, we propose a novel taxonomy based on deep-learning-based approaches that aim to gather the previous research advances systems that tackled these actual challenges. We provide an overview of existing research, discussing their strengths and limitations. Additionally, we identify various benchmark datasets, their characteristics and prevalent evaluation metrics used to assess these approaches. Finally, we explore potential research directions focusing on speed, accuracy, and type of deep learning architecture in this rapidly evolving field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Local Binary Pattern (LBP) and Transfer Learning Based Approach to Classify Lung and Colon Cancer

引用

SN computer science 2024年第6期5卷 783页

作者： Singh, Onkar Singh, Koushlendra Kumar Department of Computer Science and Engineering Manipal University Jaipur Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur

Several genetic disorders and other metabolic abnormalities work together to generate the lethal disease known as cancer. Today’s most contributing factors to mortality and disability in patients are lung and colon cancer. A World Health Organization (WHO) 2020 report listed cancer as one of the leading causes of mortality globally. About 2.735 million of these fatalities were caused by lung and colon cancer combined. A critical component of the patient’s treatment is the diagnosis of lung cancer by histopathology. Therefore, one of the leading research priorities, mostly in the domain of biomedical health information systems, is the identification and categorization of lung and colon cancer. The present article encompasses the Local Binary Pattern (LBP) and transfer learning-based approaches to classify lung as well as colon cancer. LBP has been used for extracting features, and transfer learning has been used for the classification of lung and colon cancer. Histopathological (LC25000) lung and colon datasets are used to validate the proposed methodology. The results of the proposed method have also been compared with different existing methods and reported in the article. Our proposed method has an average accuracy of 99.00% and a F1 score of 99.2%, whereas precision and recall have 99.4% for lung and colon cancer detection. The results of the investigation demonstrate that our suggested technique greatly outperforms current models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Histopathology images Local binary pattern (LBP) Lung cancer Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Rail Line Detection Algorithm Based on Improved CLRNet

引用

Journal of Shanghai Jiaotong University (science) 2024年 1-12页

作者： Zhou, Bowei Xing, Guanyu Liu, Yanli College of Computer Science Sichuan University Chengdu610065 China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu610065 China

In smart driving for rail transit, a reliable obstacle detection system is an important guarantee for the safety of trains. Therein, the detection of the rail area directly affects the accuracy of the system to identify dangerous targets. Both the rail line and the lane are presented as thin line shapes in the image, but the rail scene is more complex, and the color of the rail line is more difficult to distinguish from the background. By comparison, there are already many deep learning-based lane detection algorithms, but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection. To address this, this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models. This dataset contains rich rail images including single-rail, multi-rail, straight rail, curved rail, crossing rails, occlusion, blur, and different lighting conditions. To address the problem of the lack of deep learning-based rail line detection algorithms, we improve the CLRNet algorithm which has an excellent performance in lane detection, and propose the CLRNet-R algorithm for rail line detection. To address the problem of the rail line being thin and occupying fewer pixels in the image, making it difficult to distinguish from complex backgrounds, we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines. To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm, we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints, improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results. Through expe

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Clustered Reinforcement Learning

引用

Frontiers of computer science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

来源：评论

学校读者我要写书评

暂无评论

FilterGNN:Image feature matching with cascaded outlier filters and linearattention

引用

Computational Visual Media 2024年第5期10卷 873-884页

作者： Jun-Xiong Cai Tai-Jiang Mu Yu-Kun Lai Key Laboratory of Pervasive Computing Ministry of EducationDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityWales CF244AGUK

The cross-view matching of local image features is a fundamental task in visual localization and 3D *** study proposes FilterGNN,a transformer-based graph neural network(GNN),aiming to improve the matching efficiency and accuracy of visual *** on high matching sparseness and coarse-to-fine covisible area detection,FilterGNN utilizes cascaded optimal graph-matching filter modules to dynamically reject outlier ***,we successfully adapted linear attention in FilterGNN with post-instance normalization support,which significantly reduces the complexity of complete graph learning from O(N2)to O(N).Experiments show that FilterGNN requires only 6%of the time cost and 33.3%of the memory cost compared with SuperGlue under a large-scale input size and achieves a competitive performance in various tasks,such as pose estimation,visual localization,and sparse 3D reconstruction.

关键词： image matching transformer linear attention visual localization sparse reconstruction

来源：评论

学校读者我要写书评

暂无评论

Smart Anchor Buoy: Design and Implementation 3

Smart Anchor Buoy: Design and Implementation

引用

3rd IEEE International Conference on Signal, Control and Communication, SCC 2023

作者： Likozar, Janus Jaklic, Ales University of Ljubljana Computer Vision Laboratory Faculty of Computer and Information Science Ljubljana Slovenia

ISBN: (纸本)9798350326390

We present the design and implementation of a novel low-cost smart buoy IoUT device for anchor monitoring of recreational vessels to detect anchor drag. All current solutions solve this problem by monitoring the position of the vessel itself, which is not a reliable and timely way to detect anchor drag. Visual monitoring of the anchor requires diving. Our system solves this problem with a buoy attached to the anchor with a cable. The buoy is equipped with a GNSS module that tracks its geographic position in real-time, and an underwater IP camera that also allows visual inspection of the anchor in real-time. We have developed a web and mobile application that allows user-friendly interaction with the system, and we have experimentally evaluated the accuracy of the GNSS module's position data. © 2023 IEEE.

关键词： Buoys

来源：评论

学校读者我要写书评

暂无评论

CSNet:A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting

引用

植物表型组学（英文） 2024年第4期6卷 995-1009页

作者： Yaoxi Li Xingcai Wu Qi Wang Zhixun Pei Kejun Zhao Panfeng Chen Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationGuiyang 550025China

Wheat is the most widely grown crop in the world,and its yield is closely related to global food *** number of ears is important for wheat breeding and yield ***,automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain ***,all existing methods require position-level annotation for training,implying that a large amount of labor is required for annotation,limiting the application and development of deep learning technology in the agricultural *** address this problem,we propose a count-supervised multiscale perceptive wheat counting network(CSNet,count-supervised network),which aims to achieve accurate counting of wheat ears using quantity *** particular,in the absence of location information,CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear *** conduct comparative experiments on a publicly available global wheat head detection dataset,showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error(MAE)and root mean square error(RMSE).This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs,demonstrating its great potential for agricultural counting *** code is available at .

关键词： network counting csnet mlp-mixer multiscale supervised wheat

来源：评论

学校读者我要写书评

暂无评论

VSMCNN-dynamic summarization of videos using salient features from multi-CNN model

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14071-14080页

作者： Nair, Madhu S. Mohan, Jesna Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Nalanchira Kerala Thiruvananthapuram695015 India

A dynamic video summarization system detects key parts of the input video to generate its compact representation. The summaries can be used for efficient management of video data. This paper proposes an approach, Video summarization based on multi-CNN model (VSMCNN), that exploits major aspects of human cognition to generate meaningful summaries from videos. As the method focuses on dynamic summarization, the input video is divided into a set of shots. A multi-CNN model, which is a combination of different pre-trained models of CNN, is used for feature extraction from shots. The salient features are extracted from high dimensional feature vector using an unsupervised feature reduction technique applied in multiple subspaces to rank features in the vector. The distance measure between feature vectors is then thresholded to detect prime parts of the tested video. Experiments are performed on SumMe dataset and the results prove that our approach is successful in detecting portions of the tested video that has an essential message. The analysis shows that the method outperforms the state-of-the-art methods in the literature. Further evaluation on comparison with human-generated summaries in the ground truth proves the effectiveness of the proposed method. The paper also presents a detailed analysis to show which combination of pre-trained models of CNN is best suitable for generating dynamic summaries. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Video recording

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：