Pattern Recognition最新文献_第2页

Frequency-Aware Self-Supervised Group Activity Recognition with skeleton sequences 基于骨架序列的频率感知自监督群体活动识别

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-21 DOI: 10.1016/j.patcog.2025.111710

Guoquan Wang, Mengyuan Liu, Hong Liu, Jinyan Zhang, Peini Guo, Ruijia Fan, Siyu Chen

{"title":"Frequency-Aware Self-Supervised Group Activity Recognition with skeleton sequences","authors":"Guoquan Wang, Mengyuan Liu, Hong Liu, Jinyan Zhang, Peini Guo, Ruijia Fan, Siyu Chen","doi":"10.1016/j.patcog.2025.111710","DOIUrl":"10.1016/j.patcog.2025.111710","url":null,"abstract":"<div><div>Self-supervised, skeleton-based techniques have recently demonstrated great potential for group activity recognition via contrastive learning. However, these methods have difficulty accommodating the dynamic and complex nature of spatio-temporal data, weakening the ability to conduct effective modeling and extract crucial features. To this end, we propose a novel <strong>F</strong>requency-<strong>A</strong>ware <strong>G</strong>roup <strong>A</strong>ctivity <strong>R</strong>ecognition (FAGAR) network, which offers a comprehensive solution by addressing three key subproblems. First, the challenge of extracting discriminative features is further exacerbated by pose estimation algorithms’ limitations under random spatio-temporal data augmentation. To mitigate this, a frequency domain passing augmentation method that emphasizes individual collaborative changes is introduced, effectively filtering out noise interference. Second, the fixed connections in traditional relation modeling networks fail to adapt to dynamic scene changes. To address this, we design an adaptive frequency domain compression network, which dynamically adjusts to scene variations. Third, the temporal modeling process often leads to a loss of focus on key features, reducing the model’s ability to assess individual contributions within a group. To resolve this, we propose an amplitude-aware loss function that guides the network in learning the relative importance of individuals, ensuring it maintains the correct learning direction. Our FAGAR achieves state-of-the-art performance on several datasets for self-supervised skeleton-based group activity recognition. Code is available at <span><span>https://github.com/WGQ109/FAGAR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"167 ","pages":"Article 111710"},"PeriodicalIF":7.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 federated learning framework for semantic segmentation of terminal block in smart substation 一种新的智能变电站终端块语义分割联邦学习框架

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-21 DOI: 10.1016/j.patcog.2025.111665

Rong Xie , Zhong Chen , Weiguo Cao , Congying Wu , Tiecheng Li

{"title":"A novel federated learning framework for semantic segmentation of terminal block in smart substation","authors":"Rong Xie , Zhong Chen , Weiguo Cao , Congying Wu , Tiecheng Li","doi":"10.1016/j.patcog.2025.111665","DOIUrl":"10.1016/j.patcog.2025.111665","url":null,"abstract":"<div><div>Recent advancements in computer vision have significantly enhanced the intelligence operation and maintenance of substation equipment. In this paper, we advance this progress and focus on semantic segmentation of secondary screen cabinet terminal blocks in substations. We note that existing schemes are centralized, which may be unscalable, and more importantly, may be very difficult to protect data privacy. In response, we develop a novel semantic segmentation framework based on federated learning. This framework includes a federated learning system composed of a trusted third party, a cloud server, multiple power stations, and substations across various regions. To ensure substation security, our design incorporates anonymous identity verification managed by the trusted third party and other participants. Local substations then employ the designed semantic segmentation model to extract data and model elements through cameras and store them in distributed power stations. To address data heterogeneity in distributed semantic segmentation, we design a diffusion model for data augmentation and improve the feature similarity loss, which helps mitigate the local optima and enhance the global generalization capability of the final model. Experiments conducted using real data from multiple substations have demonstrated that our framework achieves an intelligent terminal block recognition system with an accuracy of 93.41% and mIoU of 81.37%.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"167 ","pages":"Article 111665"},"PeriodicalIF":7.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Multi-channel set polynomial based label regularized graph neural networks against extreme data scarcity 基于多通道集多项式的极端数据稀缺标签正则化图神经网络

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-21 DOI: 10.1016/j.patcog.2025.111754

Jingxiao Zhang , Shifei Ding , Jian Zhang , Lili Guo , Ling Ding

{"title":"Multi-channel set polynomial based label regularized graph neural networks against extreme data scarcity","authors":"Jingxiao Zhang , Shifei Ding , Jian Zhang , Lili Guo , Ling Ding","doi":"10.1016/j.patcog.2025.111754","DOIUrl":"10.1016/j.patcog.2025.111754","url":null,"abstract":"<div><div>Graph Neural Networks (GNNs) are one of the commonly used methods for semi-supervised node classification. Their advantage lies in modeling the relational information in the data and propagating the feature information of labeled nodes to unlabeled nodes in the graph, thereby predicting their labels. However, current research results indicate that existing models perform poorly when labeled data are extremely limited. To address this problem, we introduce a label regularization method and propose a <strong>m</strong>ulti-channel <strong>s</strong>et <strong>p</strong>olynomial based <strong>l</strong>abel <strong>r</strong>egularized graph neural network against extreme data scarcity <strong>(MSP-LR)</strong>. It consists of two components: a basic learning module based on multi-channel set polynomials and a label regularization module. Specifically, we use the basic module to expand the model's receptive field and obtain pseudo-labels for all nodes. For labeled nodes, we replace the obtained pseudo-label information with their initial label information. In the label regularization module, we impose regularization constraints on unlabeled nodes based on the clustering assumption to improve the reliability of labels. Experimental results on two homogeneous graphs and four heterogeneous graphs with different labeling rates demonstrate the effectiveness of this model.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"166 ","pages":"Article 111754"},"PeriodicalIF":7.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Growing-before-pruning: A progressive neural architecture search strategy via group sparsity and deterministic annealing 基于群稀疏性和确定性退火的渐进式神经结构搜索策略

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-21 DOI: 10.1016/j.patcog.2025.111697

Xiaotong Lu , Weisheng Dong , Zhenxuan Fang , Jie Lin , Xin Li , Guangming Shi

{"title":"Growing-before-pruning: A progressive neural architecture search strategy via group sparsity and deterministic annealing","authors":"Xiaotong Lu , Weisheng Dong , Zhenxuan Fang , Jie Lin , Xin Li , Guangming Shi","doi":"10.1016/j.patcog.2025.111697","DOIUrl":"10.1016/j.patcog.2025.111697","url":null,"abstract":"<div><div>Network pruning is a widely studied technique of obtaining compact representations from over-parameterized deep convolutional neural networks. Existing pruning methods are based on finding an optimal combination of pruned filters in the fixed search space. However, the optimality of those methods is often questionable due to limited search space and pruning choices - e.g., the difficulty with removing the entire layer and the risk of unexpected performance degradation. Inspired by the exploration vs. exploitation trade-off in reinforcement learning, we propose to reconstruct the filter space without increasing the model capacity and prune them by exploiting group sparsity. Our approach challenges the conventional wisdom by advocating the strategy of Growing-before-Pruning (GbP), which allows us to explore more space before exploiting the power of architecture search. Meanwhile, to achieve more efficient pruning, we propose to measure the importance of filters by global group sparsity, which extends the existing Gaussian scale mixture model. Such global characterization of sparsity in the filter space leads to a novel deterministic annealing strategy for progressively pruning the filters. We have evaluated our method on several popular datasets and network architectures. Our extensive experiment results have shown that the proposed method advances the current state-of-the-art.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"166 ","pages":"Article 111697"},"PeriodicalIF":7.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 framework for global role-based author name disambiguation 基于角色的全局作者姓名消歧框架

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-21 DOI: 10.1016/j.patcog.2025.111703

Lan Huang, Jiayuan Zhang, Bo Wang, Zixu Li, Shu Wang, Rui Zhang

{"title":"A framework for global role-based author name disambiguation","authors":"Lan Huang, Jiayuan Zhang, Bo Wang, Zixu Li, Shu Wang, Rui Zhang","doi":"10.1016/j.patcog.2025.111703","DOIUrl":"10.1016/j.patcog.2025.111703","url":null,"abstract":"<div><div>The academic community has long been confronted with the issue of Author Name Disambiguation (AND), where different authors share the same name. Most existing methods formalize AND as a task of clustering papers, based on the assumption that the more similar the papers are, the more likely they are to be the work of the same researcher. This paper introduces a framework for global role-based author name disambiguation, GRAND. It redefines the problem of AND by distinguishing between a real-world researcher and the author roles he/she plays, formalizing it as a role player matching problem. Furthermore, it proposes an embedding and clustering strategy based on meta-path, combined with a global coauthor sampling algorithm to address ambiguity in coauthor pairs. Finally, a set of rule-based metrics are employed to match real-world researchers with their author roles. The innovation of GRAND lies in its combination of global meta-path embedding method and rule-based author mapping. It effectively handles fuzzy coauthor relationships. In addition, it combines local and global information, and it improves disambiguation by distinguishing between researchers and the author roles they plays. The experimental results show GRAND out-performs several state-of-the-art approaches, with the F1-score improving by 0.49% to 5.45% across the three datasets.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"166 ","pages":"Article 111703"},"PeriodicalIF":7.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Unsupervised anomaly detection with a temporal continuation, confidence-aware VAE-GAN 无监督异常检测与时间延续，置信度感知VAE-GAN

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-21 DOI: 10.1016/j.patcog.2025.111699

Zeyu Xing , Owais Mehmood , William A.P. Smith

{"title":"Unsupervised anomaly detection with a temporal continuation, confidence-aware VAE-GAN","authors":"Zeyu Xing , Owais Mehmood , William A.P. Smith","doi":"10.1016/j.patcog.2025.111699","DOIUrl":"10.1016/j.patcog.2025.111699","url":null,"abstract":"<div><div>We propose an unsupervised approach to anomaly detection in data with a temporal dimension. We adapt the VAE-GAN architecture to learn the proxy task of temporal sequence continuation. Rather than reconstructing the input, our variational decoder decodes to a forecast of the future sequence. In order to separate structural uncertainty (which our model can reconstruct by fitting to observed data) from stochastic uncertainty (which it cannot) we introduce an additional decoder that outputs the pointwise confidence of the prediction, after the optimal latent-variable has been found. We can use this for zero-shot anomaly detection, separating anomalies from stochastic variation that cannot be modelled, without any examples. This is important for domains in which anomalies are so rare that it is not possible or meaningful to train a supervised model. As an example of such a domain, we introduce a new dataset comprising linescan imagery of railway lines which we use to illustrate our methods. We also achieve state-of-the-art performance on the ECG5000 and MIT-BIH time series anomaly detection datasets. We make an implementation of our method available at <span><span>https://github.com/YorkXingZeyu/ECG-VAEGAN-Project</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"166 ","pages":"Article 111699"},"PeriodicalIF":7.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Knowledge-enhanced and structure-enhanced representation learning for protein–ligand binding affinity prediction 蛋白质-配体结合亲和力预测的知识增强和结构增强表征学习

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-21 DOI: 10.1016/j.patcog.2025.111701

Mei Li , Ye Cao , Xiaoguang Liu , Hua Ji

{"title":"Knowledge-enhanced and structure-enhanced representation learning for protein–ligand binding affinity prediction","authors":"Mei Li , Ye Cao , Xiaoguang Liu , Hua Ji","doi":"10.1016/j.patcog.2025.111701","DOIUrl":"10.1016/j.patcog.2025.111701","url":null,"abstract":"<div><div>Protein–ligand binding affinity (PLA) prediction is a fundamental preliminary stage in drug discovery and development. Existing methods mainly focus on structure-free prediction of binding affinities and the investigation of structural PLA prediction is not fully explored yet. Spatial structures of protein–ligand complexes are critical in determining binding affinities. A few graph neural network (GNN) based methods model spatial structures of complexes with pairwise atomic distances within a cutoff, which provides insufficient spatial descriptions and limits their capabilities in distinguishing between certain molecules. In this paper, we propose a knowledge-enhanced and structure-enhanced representation learning method (KSM) for structural PLA prediction. The proposed KSM has a specially designed structure-based GNN (KSGNN) to learn complete representations for PLA prediction by combining sequence and structure information of complexes. Notably, KSGNN is capable of learning structure-aware representations via incorporating relative spatial information of distances and angles among atoms into the message passing. Additionally, we adopt an attentive pooling layer (APL) to further refine structural patterns in complexes. We compare KSM against 18 state-of-the-art baselines on two benchmarks. KSM outperforms its competitors with improvements of 0.0536 and 0.19 on the PDBbind core set and the CSAR-HiQ dataset, respectively, in terms of the metric of RMSE, demonstrating its superiority in binding affinity prediction.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"166 ","pages":"Article 111701"},"PeriodicalIF":7.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

OIL-AD: An anomaly detection framework for decision-making sequences OIL-AD：决策序列异常检测框架

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-19 DOI: 10.1016/j.patcog.2025.111656

Chen Wang , Sarah Erfani , Tansu Alpcan , Christopher Leckie

{"title":"OIL-AD: An anomaly detection framework for decision-making sequences","authors":"Chen Wang , Sarah Erfani , Tansu Alpcan , Christopher Leckie","doi":"10.1016/j.patcog.2025.111656","DOIUrl":"10.1016/j.patcog.2025.111656","url":null,"abstract":"<div><div>Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning and the sequential nature of the task. Most existing methods based on Reinforcement Learning (RL) are difficult to implement in the real world due to unrealistic assumptions, such as having access to environment dynamics, reward signals, and online interactions with the environment. To address these limitations, we propose an unsupervised method named Offline Imitation Learning based Anomaly Detection (OIL-AD), which detects anomalies in decision-making sequences using two extracted behaviour features: <em>action optimality</em> and <em>sequential association</em>. Our offline learning model is an adaptation of behavioural cloning with a transformer policy network, where we modify the training process to learn a Q function and a state value function from normal trajectories. We propose that the Q function and the state value function can provide sufficient information about agents’ behavioural data, from which we derive two features for anomaly detection. The intuition behind our method is that the <em>action optimality</em> feature derived from the Q function can differentiate the optimal action from others at each local state, and the <em>sequential association</em> feature derived from the state value function has the potential to maintain the temporal correlations between decisions (state–action pairs). Our experiments show that OIL-AD can achieve outstanding online anomaly detection performance with up to 34.8% improvement in <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> score over comparable baselines. The source code is available on <span><span>https://github.com/chenwang4/OILAD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"166 ","pages":"Article 111656"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Sample selection for noisy partial label learning with interactive contrastive learning 基于交互式对比学习的带噪声部分标签学习的样本选择

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-19 DOI: 10.1016/j.patcog.2025.111681

Xiaotong Yu , Shiding Sun , Yingjie Tian

引用次数: 0

Cross-domain person re-identification via learning Heterogeneous Pseudo Labels 通过学习异构伪标签进行跨域人员再识别

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-04-19 DOI: 10.1016/j.patcog.2025.111702

Zhong Zhang, Di He, Shuang Liu

{"title":"Cross-domain person re-identification via learning Heterogeneous Pseudo Labels","authors":"Zhong Zhang, Di He, Shuang Liu","doi":"10.1016/j.patcog.2025.111702","DOIUrl":"10.1016/j.patcog.2025.111702","url":null,"abstract":"<div><div>Assigning pseudo labels is vital for cross-domain person re-identification (ReID), and most existing methods only assign one kind of pseudo labels to unlabeled target domain samples, which cannot describe these unlabeled samples accurately due to large intra-class and small inter-class variances caused by diverse environmental factors, such as occlusions, illuminations, viewpoints, and poses, etc. In this paper, we propose a novel label learning method named Heterogeneous Pseudo Labels (HPL) for cross-domain person ReID, which could overcome large intra-class and small inter-class variances between pedestrian images in the target domain. For each unlabeled target domain sample, HPL simultaneously learns three different kinds of pseudo labels, i.e., fine-grained labels, coarse-grained labels, and instance labels. With the three kinds of labels, we could make full use of their own advantages to describe target domain samples from different perspectives. Meanwhile, we propose the Pseudo Labels Constraint (PLC) to improve the quality of the heterogeneous labels by using their consistency. Furthermore, in order to relieve the influence of noisy labels from the aspect of contrastive learning, we propose the Confidence Contrastive Loss (CCL) to consider the sample confidence in the learning process. Extensive experiments on four cross-domain tasks demonstrate that the proposed method achieves a new state-of-the-art performance, for example, the proposed method achieves 87.2% mAP and 95.0% Rank-1 accuracy on MSMT17<span><math><mo>→</mo></math></span>Market.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"166 ","pages":"Article 111702"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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