International Journal of Machine Learning and Cybernetics最新文献_第7页

Visible-infrared person re-identification with complementary feature fusion and identity consistency learning 利用互补特征融合和身份一致性学习进行可见红外人员再识别

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-24 DOI: 10.1007/s13042-024-02282-5

Yiming Wang, Xiaolong Chen, Yi Chai, Kaixiong Xu, Yutao Jiang, Bowen Liu

{"title":"Visible-infrared person re-identification with complementary feature fusion and identity consistency learning","authors":"Yiming Wang, Xiaolong Chen, Yi Chai, Kaixiong Xu, Yutao Jiang, Bowen Liu","doi":"10.1007/s13042-024-02282-5","DOIUrl":"https://doi.org/10.1007/s13042-024-02282-5","url":null,"abstract":"The dual-mode 24/7 monitoring systems continuously obtain visible and infrared images in a real scene. However, differences such as color and texture between these cross-modality images pose challenges for visible-infrared person re-identification (ReID). Currently, the general method is modality-shared feature learning or modal-specific information compensation based on style transfer, but the modality differences often result in the inevitable loss of valuable feature information in the training process. To address this issue, A complementary feature fusion and identity consistency learning (CFF-ICL) method is proposed. On the one hand, the multiple feature fusion mechanism based on cross attention is used to promote the features extracted by the two groups of networks in the same modality image to show a more obvious complementary relationship to improve the comprehensiveness of feature information. On the other hand, the designed collaborative adversarial mechanism between dual discriminators and feature extraction network is designed to remove the modality differences, and then construct the identity consistency between visible and infrared images. Experimental results by testing on SYSU-MM01 and RegDB datasets verify the method’s effectiveness and superiority.","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"25 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Text semantic matching algorithm based on the introduction of external knowledge under contrastive learning 对比学习下基于外部知识引入的文本语义匹配算法

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-24 DOI: 10.1007/s13042-024-02285-2

Jie Hu, Yinglian Zhu, Lishan Wu, Qilei Luo, Fei Teng, Tianrui Li

{"title":"Text semantic matching algorithm based on the introduction of external knowledge under contrastive learning","authors":"Jie Hu, Yinglian Zhu, Lishan Wu, Qilei Luo, Fei Teng, Tianrui Li","doi":"10.1007/s13042-024-02285-2","DOIUrl":"https://doi.org/10.1007/s13042-024-02285-2","url":null,"abstract":"Measuring the semantic similarity between two texts is a fundamental aspect of text semantic matching. Each word in the texts holds a weighted meaning, and it is essential for the model to effectively capture the most crucial knowledge. However, current text matching methods based on BERT have limitations in acquiring professional domain knowledge. BERT requires extensive domain-specific training data to perform well in specialized fields such as medicine, where obtaining labeled data is challenging. In addition, current text matching models that inject domain knowledge often rely on creating new training tasks to fine-tune the model, which is time-consuming. Although existing works have directly injected domain knowledge into BERT through similarity matrices, they struggle to handle the challenge of small sample sizes in professional fields. Contrastive learning trains a representation learning model by generating instances that exhibit either similarity or dissimilarity, so that a more general representation can be learned with a small number of samples. In this paper, we propose to directly integrate the word similarity matrix into BERT’s multi-head attention mechanism under a contrastive learning framework to align similar words during training. Furthermore, in the context of Chinese medical applications, we propose an entity MASK approach to enhance the understanding of medical terms by pre-trained models. The proposed method helps BERT acquire domain knowledge to better learn text representations in professional fields. Extensive experimental results have shown that the algorithm significantly improves the performance of the text matching model, especially when training data is limited.","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"13 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Condensed-gradient boosting 浓缩梯度增强

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-23 DOI: 10.1007/s13042-024-02279-0

Seyedsaman Emami, Gonzalo Martínez-Muñoz

引用次数: 0

A dual stream attention network for facial expression recognition in the wild 用于野生面部表情识别的双流注意力网络

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-23 DOI: 10.1007/s13042-024-02287-0

Hui Tang, Yichang Li, Zhong Jin

{"title":"A dual stream attention network for facial expression recognition in the wild","authors":"Hui Tang, Yichang Li, Zhong Jin","doi":"10.1007/s13042-024-02287-0","DOIUrl":"https://doi.org/10.1007/s13042-024-02287-0","url":null,"abstract":"Facial Expression Recognition (FER) is crucial for human-computer interaction and has achieved satisfactory results on lab-collected datasets. However, occlusion and head pose variation in the real world make FER extremely challenging due to facial information deficiency. This paper proposes a novel Dual Stream Attention Network (DSAN) for occlusion and head pose robust FER. Specifically, DSAN consists of a Global Feature Element-based Attention Network (GFE-AN) and a Multi-Feature Fusion-based Attention Network (MFF-AN). A sparse attention block and a feature recalibration loss designed in GFE-AN selectively emphasize feature elements meaningful for facial expression and suppress those unrelated to facial expression. And a lightweight local feature attention block is customized in MFF-AN to extract rich semantic information from different representation sub-spaces. In addition, DSAN takes into account computation overhead minimization when designing model architecture. Extensive experiments on public benchmarks demonstrate that the proposed DSAN outperforms the state-of-the-art methods with 89.70% on RAF-DB, 89.93% on FERPlus, 65.77% on AffectNet-7, 62.13% on AffectNet-8. Moreover, the parameter size of DSAN is only 11.33M, which is lightweight compared to most of the recent in-the-wild FER algorithms.","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"37 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial attack method based on enhanced spatial momentum 基于增强空间动量的对抗性攻击方法

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-22 DOI: 10.1007/s13042-024-02290-5

Jun Hu, Guanghao Wei, Shuyin Xia, Guoyin Wang

{"title":"Adversarial attack method based on enhanced spatial momentum","authors":"Jun Hu, Guanghao Wei, Shuyin Xia, Guoyin Wang","doi":"10.1007/s13042-024-02290-5","DOIUrl":"https://doi.org/10.1007/s13042-024-02290-5","url":null,"abstract":"Deep neural networks have been widely applied in many fields, but it is found that they are vulnerable to adversarial examples, which can mislead the DNN-based models with imperceptible perturbations. Many adversarial attack methods can achieve great success rates when attacking white-box models, but they usually exhibit poor transferability when attacking black-box models. Momentum iterative gradient-based methods can effectively improve the transferability of adversarial examples. Still, the momentum update mechanism of existing methods may lead to a problem of unstable gradient update direction and result in poor local optima. In this paper, we propose an enhanced spatial momentum iterative gradient-based adversarial attack method. Specifically, we introduce the spatial domain momentum accumulation mechanism. Instead of only accumulating the gradients of data points on the optimization path in the gradient update process, we additionally accumulate the average gradients of multiple sampling points within the neighborhood of data points. This mechanism fully utilizes the contextual gradient information of different regions within the image to smooth the accumulated gradients and find a more stable gradient update direction, thus escaping from poor local optima. Empirical results on the standard ImageNet dataset demonstrate that our method can significantly improve the attack success rate of momentum iterative gradient-based methods and shows excellent attack performance not only against normally trained models but also against adversarial training and defense models, outperforming the state-of-the-art methods.","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"81 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One-step graph-based multi-view clustering via specific and unified nonnegative embeddings 通过特定和统一的非负嵌入，实现基于图形的一步式多视图聚类

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-17 DOI: 10.1007/s13042-024-02280-7

Sally El Hajjar, Fahed Abdallah, Hichem Omrani, Alain Khaled Chaaban, Muhammad Arif, Ryan Alturki, Mohammed J. AlGhamdi

{"title":"One-step graph-based multi-view clustering via specific and unified nonnegative embeddings","authors":"Sally El Hajjar, Fahed Abdallah, Hichem Omrani, Alain Khaled Chaaban, Muhammad Arif, Ryan Alturki, Mohammed J. AlGhamdi","doi":"10.1007/s13042-024-02280-7","DOIUrl":"https://doi.org/10.1007/s13042-024-02280-7","url":null,"abstract":"Multi-view clustering techniques, especially spectral clustering methods, are quite popular today in the fields of machine learning and data science owing to the ever-growing diversity in data types and information sources. As the landscape of data continues to evolve, the need for advanced clustering approaches becomes increasingly crucial. In this context, the research in this study addresses the challenges posed by traditional multi-view spectral clustering techniques, offering a novel approach that simultaneously learns nonnegative embedding matrices and spectral embeddings. Moreover, the cluster label matrix, also known as the nonnegative embedding matrix, is split into two different types of matrices: (1) the shared nonnegative embedding matrix, which reflects the common cluster structure, (2) the individual nonnegative embedding matrices, which represent the unique cluster structure of each view. The proposed strategy allows us to effectively deal with noise and outliers in multiple views. The simultaneous optimization of the proposed model is solved efficiently with an alternating minimization scheme. The proposed method exhibits significant improvements, with an average accuracy enhancement of 4% over existing models, as demonstrated through extensive experiments on various real datasets. This highlights the efficacy of the approach in achieving superior clustering results.","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"21 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industrial product surface defect detection via the fast denoising diffusion implicit model 通过快速去噪扩散隐含模型检测工业产品表面缺陷

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-11 DOI: 10.1007/s13042-024-02213-4

Yue Wang, Yong Yang, Mingsheng Liu, Xianghong Tang, Haibin Wang, Zhifeng Hao, Ze Shi, Gang Wang, Botao Jiang, Chunyang Liu

{"title":"Industrial product surface defect detection via the fast denoising diffusion implicit model","authors":"Yue Wang, Yong Yang, Mingsheng Liu, Xianghong Tang, Haibin Wang, Zhifeng Hao, Ze Shi, Gang Wang, Botao Jiang, Chunyang Liu","doi":"10.1007/s13042-024-02213-4","DOIUrl":"https://doi.org/10.1007/s13042-024-02213-4","url":null,"abstract":"In the age of intelligent manufacturing, surface defect detection plays a pivotal role in the automated quality control of industrial products, constituting a fundamental aspect of smart factory evolution. Considering the diverse sizes and feature scales of surface defects on industrial products and the difficulty in procuring high-quality training samples, the achievement of real-time and high-quality surface defect detection through artificial intelligence technologies remains a formidable challenge. To address this, we introduce a defect detection approach grounded in the Fast Denoising Probabilistic Implicit Models. Firstly, we propose a noise predictor influenced by the spectral radius feature tensor of images. This enhancement augments the ability of generative model to capture nuanced details in non-defective areas, thus overcoming limitations in model versatility and detail portrayal. Furthermore, we present a loss function constraint based on the Perron-root. This is designed to incorporate the constraint within the representational space, ensuring the denoising model consistently produces high-quality samples. Lastly, comprehensive experiments on both the Magnetic Tile and Market-PCB datasets, benchmarked against nine most representative models, underscore the exemplary detection efficacy of our proposed approach.","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"25 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-representation with adaptive loss minimization via doubly stochastic graph regularization for robust unsupervised feature selection 通过双随机图正则化实现自适应损失最小化的自我呈现，从而实现稳健的无监督特征选择

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-06 DOI: 10.1007/s13042-024-02275-4

Xiangfa Song

{"title":"Self-representation with adaptive loss minimization via doubly stochastic graph regularization for robust unsupervised feature selection","authors":"Xiangfa Song","doi":"10.1007/s13042-024-02275-4","DOIUrl":"https://doi.org/10.1007/s13042-024-02275-4","url":null,"abstract":"Unsupervised feature selection (UFS), which involves selecting representative features from unlabeled high-dimensional data, has attracted much attention. Numerous self-representation-based models have been recently developed successfully for UFS. However, these models have two main problems. First, existing self-representation-based UFS models cannot effectively handle noise and outliers. Second, many graph-regularized self-representation-based UFS models typically construct a fixed graph to maintain the local structure of data. To overcome the above shortcomings, we propose a novel robust UFS model called self-representation with adaptive loss minimization via doubly stochastic graph regularization (SRALDS). Specifically, SRALDS uses an adaptive loss function to minimize the representation residual term, which may enhance the robustness of the model and diminish the effect of noise and outliers. Besides, rather than utilizing a fixed graph, SRALDS learns a high-quality doubly stochastic graph that more accurately captures the local structure of data. Finally, an efficient optimization algorithm is designed to obtain the optimal solution for SRALDS. Extensive experiments demonstrate the superior performance of SRALDS over several well-known UFS methods.","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"6 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-strategy hybrid cuckoo search algorithm with specular reflection based on a population linear decreasing strategy 基于群体线性递减策略的带有镜面反射的多策略混合布谷鸟搜索算法

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-05 DOI: 10.1007/s13042-024-02273-6

Chengtian Ouyang, Xin Liu, Donglin Zhu, Yangyang Zheng, Changjun Zhou, Chengye Zou

{"title":"A multi-strategy hybrid cuckoo search algorithm with specular reflection based on a population linear decreasing strategy","authors":"Chengtian Ouyang, Xin Liu, Donglin Zhu, Yangyang Zheng, Changjun Zhou, Chengye Zou","doi":"10.1007/s13042-024-02273-6","DOIUrl":"https://doi.org/10.1007/s13042-024-02273-6","url":null,"abstract":"The cuckoo search algorithm (CS), an algorithm inspired by the nest-parasitic breeding behavior of cuckoos, has proved its own effectiveness as a problem-solving approach in many fields since it was proposed. Nevertheless, the cuckoo search algorithm still suffers from an imbalance between exploration and exploitation as well as a tendency to fall into local optimization. In this paper, we propose a new hybrid cuckoo search algorithm (LHCS) based on linear decreasing of populations, and in order to optimize the local search of the algorithm and make the algorithm converge quickly, we mix the solution updating strategy of the Grey Yours sincerely, wolf optimizer (GWO) and use the linear decreasing rule to adjust the calling ratio of the strategy in order to balance the global exploration and the local exploitation; Second, the addition of a specular reflection learning strategy enhances the algorithm's ability to jump out of local optima; Finally, the convergence ability of the algorithm on different intervals and the adaptive ability of population diversity are improved using a population linear decreasing strategy. The experimental results on 29 benchmark functions from the CEC2017 test set show that the LHCS algorithm has significant superiority and stability over other algorithms when the quality of all solutions is considered together. In order to further verify the performance of the proposed algorithm in this paper, we applied the algorithm to engineering problems, functional tests, and Wilcoxon test results show that the comprehensive performance of the LHCS algorithm outperforms the other 14 state-of-the-art algorithms. In several engineering optimization problems, the practicality and effectiveness of the LHCS algorithm are verified, and the design cost can be greatly reduced by applying it to real engineering problems.","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel abstractive summarization model based on topic-aware and contrastive learning 基于主题感知和对比学习的新型抽象摘要模型

IF 5.6 3区计算机科学

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-04 DOI: 10.1007/s13042-024-02263-8

Huanling Tang, Ruiquan Li, Wenhao Duan, Quansheng Dou, Mingyu Lu

引用次数: 0