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刘漳辉

副教授(高校)

计算机与大数据学院、软件学院

Total Results: 83

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Information-controlled graph convolutional network for multi-view semi-supervised classification SCIE
期刊论文 | 2025 , 184 | NEURAL NETWORKS
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Abstract :

Graph convolutional networks have achieved remarkable success in the field of multi-view learning. Unfortunately, most graph convolutional network-based multi-view learning methods fail to capture long-range dependencies due to the over-smoothing problem. Many studies have attempted to mitigate this issue by decoupling graph convolution operations. However, these decoupled architectures lead to the absence of feature transformation module, thus limiting the expressive power of the model. To this end, we propose an information-controlled graph convolutional network for multi-view semi-supervised classification. In the proposed method, we maintain the paradigm of node embeddings during propagation by imposing orthogonality constraints on the feature transformation module. By further introducing a damping factor based on residual connections, we theoretically demonstrate that the proposed method can alleviate the over-smoothing problem while retaining the feature transformation module. Furthermore, we prove that the proposed model can stabilize both forward inference and backward propagation in graph convolutional networks. Extensive experimental results on benchmark datasets demonstrate the effectiveness of the proposed method.

Keyword :

Graph convolutional network Graph convolutional network Layer normalization Layer normalization Multi-view learning Multi-view learning Semi-supervised classification Semi-supervised classification

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GB/T 7714 Shi, Yongquan , Pi, Yueyang , Liu, Zhanghui et al. Information-controlled graph convolutional network for multi-view semi-supervised classification [J]. | NEURAL NETWORKS , 2025 , 184 .
MLA Shi, Yongquan et al. "Information-controlled graph convolutional network for multi-view semi-supervised classification" . | NEURAL NETWORKS 184 (2025) .
APA Shi, Yongquan , Pi, Yueyang , Liu, Zhanghui , Zhao, Hong , Wang, Shiping . Information-controlled graph convolutional network for multi-view semi-supervised classification . | NEURAL NETWORKS , 2025 , 184 .
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Information-controlled graph convolutional network for multi-view semi-supervised classification Scopus
期刊论文 | 2025 , 184 | Neural Networks
Information-controlled graph convolutional network for multi-view semi-supervised classification EI
期刊论文 | 2025 , 184 | Neural Networks
基于Hyperledger Fabric的数据可信共享平台
期刊论文 | 2025 , 46 (1) , 189-199 | 小型微型计算机系统
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Abstract :

现今社会上数据的规模和种类变得越来越庞大和多样化,如何安全可信地共享异构数据资源成为了亟待解决的问题.为实现大数据的可信互联,提出基于Hyperledger Fabric的数据可信共享平台.首先,针对数据异源异构的问题,定义了数据架构的转换规则;然后,以数据提供方和数据需求方之间的数据共享全过程为导向,提出了数据可信追溯机制,保证了数据共享的真实性和完整性;此外,文中设计了一种数据处理即服务的数据共享框架,在确保数据可信的前提下,支撑数据调用、数据训练和数据匹配操作.通过对执行效率和智能合约性能进行验证分析,证明了本平台的有效性和实用性.

Keyword :

Hyperledger Fabric Hyperledger Fabric 区块链 区块链 可信凭证 可信凭证 数据共享 数据共享 智能合约 智能合约

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GB/T 7714 林哲旭 , 陈汉林 , 刘漳辉 et al. 基于Hyperledger Fabric的数据可信共享平台 [J]. | 小型微型计算机系统 , 2025 , 46 (1) : 189-199 .
MLA 林哲旭 et al. "基于Hyperledger Fabric的数据可信共享平台" . | 小型微型计算机系统 46 . 1 (2025) : 189-199 .
APA 林哲旭 , 陈汉林 , 刘漳辉 , 陈星 , 莫毓昌 . 基于Hyperledger Fabric的数据可信共享平台 . | 小型微型计算机系统 , 2025 , 46 (1) , 189-199 .
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Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips SCIE
期刊论文 | 2024 , 36 (3) | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
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Abstract :

Digital microfluidic biochips (DMFBs) have a significant stride in the applications of medicine and the biochemistry in recent years. DMFBs based on micro -electrode -dot -array (MEDA) architecture, as the nextgeneration DMFBs, aim to overcome drawbacks of conventional DMFBs, such as droplet size restriction, low accuracy, and poor sensing ability. Since the potential market value of MEDA biochips is vast, it is of paramount importance to explore approaches to protect the intellectual property (IP) of MEDA biochips during the development process. In this paper, an IP authentication strategy based on the multi-PUF applied to MEDA biochips is presented, called bioMPUF, consisting of Delay PUF, Split PUF and Countermeasure. The bioMPUF strategy is designed to enhance the non -linearity between challenges and responses of PUFs, making the challenge-response pairs (CRPs) on the MEDA biochips are difficult to be anticipated, thus thwarting IP piracy attacks. Moreover, based on the easy degradation of MEDA biochip electrodes, a countermeasure is proposed to destroy the availability of piracy chips. Experimental results demonstrate the feasibility of the proposed bioMPUF strategy against the brute force attack and modeling attack.

Keyword :

Hardware security Hardware security IP protection IP protection MEDA biochips MEDA biochips Modeling attack Modeling attack Multi-PUF Multi-PUF

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GB/T 7714 Dong, Chen , Guo, Xiaodong , Lian, Sihuang et al. Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (3) .
MLA Dong, Chen et al. "Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 36 . 3 (2024) .
APA Dong, Chen , Guo, Xiaodong , Lian, Sihuang , Yao, Yinan , Chen, Zhenyi , Yang, Yang et al. Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (3) .
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Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips Scopus
期刊论文 | 2024 , 36 (3) | Journal of King Saud University - Computer and Information Sciences
一种用于多域对话状态追踪的知识增强与自注意力引导的图神经网络 CSCD PKU
期刊论文 | 2024 , 45 (1) , 108-114 | 小型微型计算机系统
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Abstract :

对话状态追踪是对话系统的重要组成部分,旨在从用户与系统的对话中跟踪用户意图,其通常表示为槽位-槽值对序列.近年来,深度神经网络模型在对话状态追踪问题上取得了较大进展.然而,现有模型在槽位相关性建模方面还存在可拓展性差与易引入噪声等问题.针对上述问题,本文提出了一种知识增强与自注意力引导的图神经网络KESA-GNN(Knowledge-En-hanced &Self-Attention Guided Graph Neural Network).首先,KESA-GNN通过外部知识嵌入增强槽的语义表征提升多头自注意力机制对槽位间相关性的辨别能力.其次,为了精确建模槽位间的诸如共指、共现等相关性,提出了 一种自注意力引导的图神经网络建模槽位相关性.该网络采用多头注意力机制获得槽位间的注意力矩阵以及槽位表征,通过Max-N Relation算法获得注意力矩阵中强相关关系集,将稠密的注意力矩阵稀疏化,从而引导图神经网络中强相关槽位间的信息传播,降低无关槽位的噪声影响.最后,KESA-GNN采用门控融合机制过滤槽位多头注意力和图神经网络输出的槽位表征,从而获取更准确的槽位表征向量,进一步提升了 KESA-GNN的鲁棒性.在多域对话数据集上的实验结果表明,KESA-GNN模型的性能优于最新的基线模型.

Keyword :

图神经网络 图神经网络 对话状态追踪 对话状态追踪 知识图谱 知识图谱 自注意力引导 自注意力引导 门控融合 门控融合

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GB/T 7714 刘漳辉 , 林宇航 , 陈羽中 . 一种用于多域对话状态追踪的知识增强与自注意力引导的图神经网络 [J]. | 小型微型计算机系统 , 2024 , 45 (1) : 108-114 .
MLA 刘漳辉 et al. "一种用于多域对话状态追踪的知识增强与自注意力引导的图神经网络" . | 小型微型计算机系统 45 . 1 (2024) : 108-114 .
APA 刘漳辉 , 林宇航 , 陈羽中 . 一种用于多域对话状态追踪的知识增强与自注意力引导的图神经网络 . | 小型微型计算机系统 , 2024 , 45 (1) , 108-114 .
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一种用于多域对话状态追踪的知识增强与自注意力引导的图神经网络 CSCD PKU
期刊论文 | 2024 , 45 (01) , 108-114 | 小型微型计算机系统
Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection EI
会议论文 | 2024 , 2012 , 137-151 | 18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023
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Abstract :

Community detection is widely used in network analysis, which seeks to divide network nodes into distinct communities based on the topology structure and attribute information of the network. Due to its interpretability, nonnegative matrix factorization becomes an essential method for community detection. However, it decomposes the adjacency matrix and attribute matrix separately, which do not tightly incorporate topology and attributes. And in the problem of division inconsistency based on topology and attributes caused by the mismatch between the topology similarity and attribute similarity of paired nodes, it ignores the difference in the matching degree of each attribute and each node. In this paper, we propose a nonnegative matrix factorization algorithm for community detection (MTACD) based on the matching degree between topology and attribute. First, we employ an attribute embedding mechanism to enhance the node-attribute relationship. Second, we design an attribute matching degree and a node topology-and-attribute matching degree in order to resolve the mismatch between topology and attribute similarity. Experiments on both real-world and synthetic networks demonstrate the effectiveness of our algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keyword :

Matrix algebra Matrix algebra Matrix factorization Matrix factorization Population dynamics Population dynamics Topology Topology

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GB/T 7714 Zeng, Ruolan , Liu, Zhanghui , Guo, Kun . Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection [C] . 2024 : 137-151 .
MLA Zeng, Ruolan et al. "Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection" . (2024) : 137-151 .
APA Zeng, Ruolan , Liu, Zhanghui , Guo, Kun . Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection . (2024) : 137-151 .
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Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection Scopus
其他 | 2024 , 2012 , 137-151 | Communications in Computer and Information Science
A knowledge-enhanced interest segment division attention network for click-through rate prediction EI
期刊论文 | 2024 , 36 (34) , 21817-21837 | Neural Computing and Applications
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Abstract :

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

Keyword :

Contrastive Learning Contrastive Learning Knowledge graph Knowledge graph Prediction models Prediction models Semantic Segmentation Semantic Segmentation

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GB/T 7714 Liu, Zhanghui , Chen, Shijie , Chen, Yuzhong et al. A knowledge-enhanced interest segment division attention network for click-through rate prediction [J]. | Neural Computing and Applications , 2024 , 36 (34) : 21817-21837 .
MLA Liu, Zhanghui et al. "A knowledge-enhanced interest segment division attention network for click-through rate prediction" . | Neural Computing and Applications 36 . 34 (2024) : 21817-21837 .
APA Liu, Zhanghui , Chen, Shijie , Chen, Yuzhong , Su, Jieyang , Zhong, Jiayuan , Dong, Chen . A knowledge-enhanced interest segment division attention network for click-through rate prediction . | Neural Computing and Applications , 2024 , 36 (34) , 21817-21837 .
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A knowledge-enhanced interest segment division attention network for click-through rate prediction Scopus
期刊论文 | 2024 , 36 (34) , 21817-21837 | Neural Computing and Applications
Marine litter detection based on YOLOv7 algorithm and data encryption protection EI
会议论文 | 2023 , 82-87 | 13th International Conference on Communication and Network Security, ICCNS 2023
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Abstract :

Marine litter can cause significant damage to marine biodiversity, threatening the marine food chain and spreading harmful substances, posing a significant impact on the ocean ecosystem. Autonomous Underwater Vehicles (AUVs) can automatically remove marine litter using sensors and trained models. this paper evaluates the YOLOv7 series of models that utilize deep learning to detect targets in a real underwater environment. In order to improve the performance of the model, we introduced two attention mechanisms, and the experimental results showed a 2.5% increase in Mean Average Precision(mAP) values. We used a large publicly available dataset of re-annotated open-water debris to train convolutional neural networks for target detection, and we evaluated the trained models on a subset of the dataset, to provide insights into the ability of deep learning to detect marine litter. In addition, to prevent attacks by malicious actors during AUVs cloud platform access, we introduced data encryption protection to ensure that the model's predicted results can be correctly received by AUVs. © 2023 ACM.

Keyword :

Autonomous underwater vehicles Autonomous underwater vehicles Biodiversity Biodiversity Convolutional neural networks Convolutional neural networks Cryptography Cryptography Deep learning Deep learning Large datasets Large datasets Learning systems Learning systems

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GB/T 7714 Wu, Qiaowen , Ke, Yaojie , Liu, Zhanghui et al. Marine litter detection based on YOLOv7 algorithm and data encryption protection [C] . 2023 : 82-87 .
MLA Wu, Qiaowen et al. "Marine litter detection based on YOLOv7 algorithm and data encryption protection" . (2023) : 82-87 .
APA Wu, Qiaowen , Ke, Yaojie , Liu, Zhanghui , Zhang, Yuanyuan , Wu, Qiyan . Marine litter detection based on YOLOv7 algorithm and data encryption protection . (2023) : 82-87 .
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一种用于方面级情感分析的关系注意力图卷积网络 CSCD PKU
期刊论文 | 2023 , 44 (04) , 752-758 | 小型微型计算机系统
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Abstract :

方面级情感分析是情感分析的子任务,旨在判断评论目标的具体方面所对应的情感极性.近年来,深度神经网络模型在方面级情感分析问题上取得了较大进展.然而,现有的方面级情感分析模型仍存在方面信息丢失、没有充分利用句法依存关系等问题.本文提出了一种基于关系注意力机制的图卷积网络RAGCN(Relational Attention based Graph Convolutional Network).首先,RAGCN通过两个双向长短期记忆网络分别对句子和增强后的方面建模,以引导图卷积网络对向量表示进行更新.其次,为了区分上下文单词对给定方面情感的贡献,提出了一种关系注意力机制.该机制能充分利用评论节点间的边类型,结合双向长短期记忆网络的输出以捕获方面和上下文单词之间的关系.此外,为进一步提高模型的鲁棒性,RAGCN采用门控融合机制来过滤关系注意力层和图卷积网络层的输出,从而获取多更准确的句子表征向量.多个方面级情感分析数据集上的实验结果表明,RAGCN模型在准确度,Macro-F1方面均优于对比模型.

Keyword :

关系注意力 关系注意力 图卷积网络 图卷积网络 方面级情感分析 方面级情感分析 门控融合机制 门控融合机制

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GB/T 7714 刘漳辉 , 杨耀东 , 陈羽中 . 一种用于方面级情感分析的关系注意力图卷积网络 [J]. | 小型微型计算机系统 , 2023 , 44 (04) : 752-758 .
MLA 刘漳辉 et al. "一种用于方面级情感分析的关系注意力图卷积网络" . | 小型微型计算机系统 44 . 04 (2023) : 752-758 .
APA 刘漳辉 , 杨耀东 , 陈羽中 . 一种用于方面级情感分析的关系注意力图卷积网络 . | 小型微型计算机系统 , 2023 , 44 (04) , 752-758 .
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一种用于方面级情感分析的关系注意力图卷积网络 CSCD PKU
期刊论文 | 2023 , 44 (4) , 752-758 | 小型微型计算机系统
一种用于方面级情感分析的关系注意力图卷积网络 CSCD PKU
期刊论文 | 2023 , 44 (04) , 752-758 | 小型微型计算机系统
Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks CPCI-S
期刊论文 | 2023 , 809-814 | ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS
WoS CC Cited Count: 1
Abstract&Keyword Cite Version(1)

Abstract :

Load prediction is an essential technique to improve edge system performance by proactively configuring and allocating system resources. Traditional load prediction methods obtain high prediction when handling loads exhibiting cyclical trend behavior, but they are unable to capturing highly-variable loads in edge computing environments. Existing studies fit prediction models via independent time series and output single-point real-value predictions. However, in practical edge scenarios, it is more valuable to obtain application value by utilizing the probability distribution of future loads rather than directly predicting specific values. To solve these problems, we propose an Edge Load Prediction method empowered by Deep Auto-regressive Recurrent networks (ELP-DAR). The ELP-DAR uses the time-series data of edge loads to train deep auto-regressive recurrent networks, which integrate Long Short-Term Memory (LSTM) into the S2S framework to calculate the parameters of the probability distribution at the next time-point. Therefore, the ELP-DAR can efficiently extract the essential representations of edge loads and learn their complex patterns, and the probability distribution for highly-variable edge loads can be accurately predicted. Extensive simulation experiments are conducted to validate the effectiveness of the proposed ELP-DAR method based on real-world edge load datasets. The results show that the ELP-DAR achieves higher prediction accuracy than other benchmark methods with different prediction lengths.

Keyword :

deep auto-regression deep auto-regression Edge computing Edge computing load prediction load prediction probability distribution probability distribution recurrent neural networks recurrent neural networks

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GB/T 7714 Liu, Zhanghui , Chen, Lixian , Chen, Zheyi et al. Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks [J]. | ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS , 2023 : 809-814 .
MLA Liu, Zhanghui et al. "Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks" . | ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (2023) : 809-814 .
APA Liu, Zhanghui , Chen, Lixian , Chen, Zheyi , Huang, Yifan , Liang, Jie , Yu, Zhengxin et al. Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks . | ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS , 2023 , 809-814 .
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Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks EI
会议论文 | 2023 , 2023-May , 809-814
Image Semantic Segmentation Network Based on Semantic Propagation and Fore-Background Aware EI CSCD PKU
期刊论文 | 2022 , 35 (1) , 71-81 | Pattern Recognition and Artificial Intelligence
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Abstract :

Although image segmentation is widely applied in many fields owing to the assistance of better analysis and understanding of images, the models based on fully convolutional neural networks still engender the problems of resolution reconstruction and contextual information usage in semantic segmentation. Aiming at the problems, a semantic propagation and fore-background aware network for image semantic segmentation is proposed. A joint semantic propagation up-sampling module(JSPU) is proposed to obtain semantic weights by extracting the global and local semantic information from high-level features. Then the semantic information is propagated from high-level features to low-level features for alleviating the semantic gap between them. The resolution reconstruction is achieved through a hierarchical up-sampling structure. In addition, a pyramid fore-background aware module is proposed to extract foreground and background features of different scales through two parallel branches. Multi-scale fore-background aware features are captured by establishing the dependency relationships between the foreground and background features, thereby the contextual representation of foreground features is enhanced. Experiments on semantic segmentation benchmark datasets show that SPAFBA is superior in performance. © 2022, Science Press. All right reserved.

Keyword :

Backpropagation Backpropagation Benchmarking Benchmarking Convolution Convolution Convolutional neural networks Convolutional neural networks Semantics Semantics Semantic Segmentation Semantic Segmentation Semantic Web Semantic Web Signal sampling Signal sampling

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GB/T 7714 Liu, Zhanghui , Zhan, Xiaolu , Chen, Yuzhong . Image Semantic Segmentation Network Based on Semantic Propagation and Fore-Background Aware [J]. | Pattern Recognition and Artificial Intelligence , 2022 , 35 (1) : 71-81 .
MLA Liu, Zhanghui et al. "Image Semantic Segmentation Network Based on Semantic Propagation and Fore-Background Aware" . | Pattern Recognition and Artificial Intelligence 35 . 1 (2022) : 71-81 .
APA Liu, Zhanghui , Zhan, Xiaolu , Chen, Yuzhong . Image Semantic Segmentation Network Based on Semantic Propagation and Fore-Background Aware . | Pattern Recognition and Artificial Intelligence , 2022 , 35 (1) , 71-81 .
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基于语义传播与前/背景感知的图像语义分割网络 CSCD PKU
期刊论文 | 2022 , 35 (1) , 71-81 | 模式识别与人工智能
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