Pattern Recognition最新文献

{"title":"AMGSN: Adaptive mask-guide supervised network for debiased facial expression recognition","authors":"Tianlong Gu,&nbsp;Hao Li,&nbsp;Xuan Feng,&nbsp;Yiqin Luo","doi":"10.1016/j.patcog.2025.112023","DOIUrl":"10.1016/j.patcog.2025.112023","url":null,"abstract":"<div><div>Facial expression recognition plays a crucial role in understanding human emotions and behavior. However, existing models often exhibit biases and imbalance towards diverse expression classes. To address this problem, we propose an Adaptive Mask-Guide Supervised Network (AMGSN) to enhance the uniform performance of the facial expression recognition models. We propose an adaptive mask guidance mechanism to mitigate bias and ensure uniform performance across different expression classes. AMGSN focuses on learning the ability to distinguish facial features with under-expressed expressions by dynamically generating masks during pre-training. Specifically, we employ an asymmetric encoder–decoder architecture, where the encoder encodes only the unmasked visible regions, while the lightweight decoder reconstructs the original image using latent representations and mask markers. By utilizing dynamically generated masks and focusing on informative regions, these models effectively reduce the interference of confounding factors, thus enhancing the discriminative power of the learned representation. In the pre-training stage, we introduce the Attention-Based Mask Generator (ABMG) to identify salient regions of expressions. Additionally, we advance the Mask Ratio Update Strategy (MRUS), which utilizes image reconstruction loss, to adjust the mask ratio for each image during pre-training. In the finetune stage, debiased center loss and contrastive loss are introduced to optimize the network to ensure the overall performance of expression recognition. Extensive experimental results on several standard datasets demonstrate that the proposed AMGSN significantly improves both balance and accuracy compared to state-of-the-art methods. For example, AMGSN reached 89.34% on RAF-DB, and 62.83% on AffectNet, respectively, with a standard deviation of only 0.0746 and 0.0484. This demonstrates the effectiveness of our improvements<span><span>¹</span></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 112023"},"PeriodicalIF":7.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548745","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}

{"title":"MDGP-forest: A novel deep forest for multi-class imbalanced learning based on multi-class disassembly and feature construction enhanced by genetic programming","authors":"Zhikai Lin ,&nbsp;Yong Xu ,&nbsp;Kunhong Liu ,&nbsp;Liyan Chen","doi":"10.1016/j.patcog.2025.112070","DOIUrl":"10.1016/j.patcog.2025.112070","url":null,"abstract":"<div><div>Class imbalance is a significant challenge in the field of machine learning. Due to factors such as quantity differences and feature overlap among classes, the imbalance problem for multiclass classification is more difficult than that for binary one, which leads to the existing research primarily focusing on the binary classification scenario. This study proposes a novel deep forest algorithm with the aid of Genetic Programming (GP), MDGP-Forest, for the multiclass imbalance problem. MDGP-Forest utilizes Multi-class Disassembly and undersampling based on instance hardness between layers to obtain multiple binary classification datasets, each corresponding to a GP population for feature construction. The improved fitness function of GP assesses the incremental importance of the constructed features for enhanced vectors, introducing higher-order information into subsequent layers to improve predicted performance. Each GP population generates a set of new features that improve the separability of classes, empowering MDGP-Forest with the capability to address the challenge of overlapping features among multiple classes. We thoroughly evaluate the classification performance of MDGP-Forest on 35 datasets. The experimental results demonstrate that MDGP-Forest significantly outperforms existing methods in addressing multiclass imbalance problems, exhibiting superior predictive performance.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 112070"},"PeriodicalIF":7.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562955","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}

{"title":"AniFaceDiff: Animating stylized avatars via parametric conditioned diffusion models","authors":"Ken Chen ,&nbsp;Sachith Seneviratne ,&nbsp;Wei Wang ,&nbsp;Dongting Hu ,&nbsp;Sanjay Saha ,&nbsp;Md. Tarek Hasan ,&nbsp;Sanka Rasnayaka ,&nbsp;Tamasha Malepathirana ,&nbsp;Mingming Gong ,&nbsp;Saman Halgamuge","doi":"10.1016/j.patcog.2025.112017","DOIUrl":"10.1016/j.patcog.2025.112017","url":null,"abstract":"<div><div>Animating stylized head avatars with dynamic poses and expressions has become an important focus in recent research due to its broad range of applications (e.g. VR/AR, film and animation, privacy protection). Previous research has made significant progress by training controllable generative models to animate the reference avatar using the target pose and expression. However, existing portrait animation methods are mostly trained using human faces, making them struggle to generalize to stylized avatar references such as cartoon, painting, and 3D-rendered avatars. Moreover, the mechanisms used to animate avatars – namely, to control the pose and expression of the reference – often inadvertently introduce unintended features – such as facial shape – from the target, while also causing a loss of intended features, like expression-related details. This paper proposes AniFaceDiff, a Stable Diffusion based method with a new conditioning module for animating stylized avatars. First, we propose a refined spatial conditioning approach by Facial Alignment to minimize identity mismatches, particularly between stylized avatars and human faces. Then, we introduce an Expression Adapter that incorporates additional cross-attention layers to address the potential loss of expression-related information. Extensive experiments demonstrate that our method achieves state-of-the-art performance, particularly in the most challenging out-of-domain stylized avatar animation, i.e., domains unseen during training. It delivers superior image quality, identity preservation, and expression accuracy. This work enhances the quality of virtual stylized avatar animation for constructive and responsible applications. To promote ethical use in virtual environments, we contribute to the advancement of face manipulation detection by evaluating state-of-the-art detectors, highlighting potential areas for improvement, and suggesting solutions.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 112017"},"PeriodicalIF":7.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548744","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}

{"title":"Deep matrix factorization with adaptive weights for multi-view clustering","authors":"Yasser Khalafaoui ,&nbsp;Basarab Matei ,&nbsp;Martino Lovisetto ,&nbsp;Nistor Grozavu","doi":"10.1016/j.patcog.2025.112027","DOIUrl":"10.1016/j.patcog.2025.112027","url":null,"abstract":"<div><div>Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we introduce a novel Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering (DMFAW). Our method simultaneously incorporates feature selection and generates local partitions, enhancing clustering results. The feature weights are driven by a single, control-theory-inspired parameter that is updated dynamically, which improves stability and speeds convergence. A late fusion approach is then proposed to align the weighted local partitions with the consensus partition. Finally, the optimization problem is solved via an alternating optimization algorithm with theoretically guaranteed convergence. Extensive experiments on benchmark datasets highlight that DMFAW outperforms state-of-the-art methods in terms of clustering performance.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 112027"},"PeriodicalIF":7.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519059","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}

{"title":"Robust facial expression recognition by simultaneously addressing hard and mislabeled samples","authors":"Yuandong Min ,&nbsp;Ruyi Xu ,&nbsp;Jingying Chen ,&nbsp;Yanfeng Ji ,&nbsp;Xiaodi Liu","doi":"10.1016/j.patcog.2025.112026","DOIUrl":"10.1016/j.patcog.2025.112026","url":null,"abstract":"<div><div>Facial Expression Recognition (FER) in the wild is a challenging task, especially when training data contains numerous mislabeled samples and hard samples. Typically, FER models either overfit to the mislabeled samples or underfit to the hard samples, resulting in degraded performance. However, most existing methods fail to address both issues simultaneously. To overcome this limitation, this paper introduces a novel FER method called Noise-Hard robust Graph (NHG), which dynamically supervises the updating of the adjacency matrix in the Graph Convolutional Networks, striking a balance between suppressing the impacts of noisy labels and encouraging learning from hard samples. First, we map high-dimensional facial expression features onto low-dimensional manifolds to initialize the topological relationships between the samples, thus measuring the hard sample relationships more accurately. Second, we design a Label Consistency Mask (LCM) strategy to retain potential connections for hard sample learning. LCM could also potentially preserve correct connections while noisy labels exist, supporting noise-robust learning. Third, based on differences in trends of <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>-norm variation between mislabeled samples and hard samples, the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>-norm regularization (L2R) suppresses the learning of mislabeled samples while preserving the learning potential of hard samples and suppressing the propagation of their features within the graph. Experimental results demonstrate that our method achieves competitive performance compared to state-of-the-art methods in scenarios with noisy labels and hard samples.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 112026"},"PeriodicalIF":7.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579264","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}

{"title":"Structure anchor graph learning for multi-view clustering","authors":"Wei Guo ,&nbsp;Zhe Wang ,&nbsp;Wei Shao","doi":"10.1016/j.patcog.2025.111880","DOIUrl":"10.1016/j.patcog.2025.111880","url":null,"abstract":"<div><div>With the growth of data and diverse data sources, clustering large-scale multi-view data has emerged as a prominent topic in the field of machine learning. Anchor graph is an efficient strategy to improve the scalability of graph based multi-view clustering methods because it can capture the essence of the entire dataset by utilizing only a small set of representative anchor points. However, most existing anchor graph based methods encounter at least one of the following two challenges: the first one is the separation of anchor selection from the anchor graph construction process, while the second one is the requirement of an additional clustering step to generate the indicator matrix. Both of the separated steps can potentially lead to suboptimal solutions. In this paper, we propose structure anchor graph learning for multi-view clustering (SAGL), which jointly addresses the two challenges within a unified learning framework. Specifically, instead of utilizing the fixed anchors selected during the pre-processing step, SAGL jointly learns the consensus anchors in the latent space, and constructs anchor graph by assigning larger similarity values to sample-anchor pairs with shorter distances. Meanwhile, by manipulating the connected components of the anchor graph with rank constraint, SAGL obtains the anchor graph with clear cluster structure that can directly reveal the indicator of samples without any post-processing step. As a result, it becomes a truly one-stage end-to-end learning problem. In addition, a simple yet effective transformation is introduced to convert vector-sum-from to matrix-multiplication-form with trace operation, which leads an efficient optimization algorithm. Extensive experiments on several real-world multi-view datasets demonstrate the effectiveness and efficiency of the proposed methods over other state-of-the-art MvC methods.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 111880"},"PeriodicalIF":7.5,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548749","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}

{"title":"A survey on image compressive sensing: From classical theory to the latest explicable deep learning","authors":"Lijun Zhao ,&nbsp;Yufeng Zhang ,&nbsp;Xinlu Wang ,&nbsp;Jinjing Zhang ,&nbsp;Huihui Bai ,&nbsp;Anhong Wang","doi":"10.1016/j.patcog.2025.112022","DOIUrl":"10.1016/j.patcog.2025.112022","url":null,"abstract":"<div><div>Deep learning has achieved significant advancements in both low-level and high-level computer vision tasks, which can also drive the development of an essential research field of Image Compressive Sensing (ICS) today and in the future. Nowadays model-inspired ICS reconstruction methods have gained considerable attention from researchers, resulting in numerous new developments. However, existing literature lacks a comprehensive summary of these advancements. To revitalize the field of ICS, it is crucial to summarize them to provide valuable insights for various other fields and practical applications. Thus, this article first looks back on foundational theories of ICS, including signal sparse representation, sampling and reconstruction. Next, we summarize different types of measurement matrices for sampling, which include learnable/non-learnable measurement matrix, uniform/non-uniform measurement matrix. Then, we provide a detailed review of ICS reconstruction, covering traditional optimization reconstruction methods, inexplicable reconstruction methods and explainable reconstruction methods as well as Transformer-based reconstruction methods, which will help readers quickly grasp the history of ICS development. We also evaluate several representative ICS reconstruction methods on publicly available datasets, comparing their performance and computational complexities to highlight their strengths and weaknesses. Finally, we conclude this paper and their future opportunities and challenges are prospected. All related materials can be found at <span><span>https://github.com/mdcnn/CS-Survey</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 112022"},"PeriodicalIF":7.5,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548746","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}

{"title":"AFPN: Alignment feature pyramid network for real-time semantic segmentation","authors":"Yongsheng Dong ,&nbsp;Chongchong Mao ,&nbsp;Lintao Zheng ,&nbsp;Qingtao Wu ,&nbsp;Mingchuan Zhang ,&nbsp;Xuelong Li","doi":"10.1016/j.patcog.2025.112019","DOIUrl":"10.1016/j.patcog.2025.112019","url":null,"abstract":"<div><div>The structures of two pathways and the feature pyramid network (FPN) have achieved advanced performance in semantic segmentation. These two types of structures adopt different approaches to fuse low-level (shallow layer) spatial information and high-level (deep layer) semantic information. However, the segmentation results still lack local details due to the loss of information caused by simply fusing low-level feature details directly with multi-level deep features. To alleviate this problem, we propose an alignment feature pyramid network (AFPN) for real-time semantic segmentation. It can efficiently utilize both the low-level spatial information and high-level semantic information. Specifically, our AFPN consists of two components: the pooling enhancement attention block (PEAB) and the dual pooling alignment block (DPAB). The PEAB can effectively extract global information by using an aggregation pooling operation. The DPAB performs two types of pooling operations along the channel and spatial dimensions, reducing the differences between multi-scale feature maps. Extensive experiments show that AFPN achieves a better balance between accuracy and speed. On the Cityscapes, CamVid, and ADE20K datasets, AFPN achieves 78.75%, 79.24%, and 39.56% mIoU and the speed meets the real-time requirement. Our code can be available at the <span><span>https://github.com/chongchongmao/AFPN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 112019"},"PeriodicalIF":7.5,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519061","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}

{"title":"Multi-task dynamic graph learning for brain disorder identification with functional MRI","authors":"Yunling Ma ,&nbsp;Chaojun Zhang ,&nbsp;Di Xiong ,&nbsp;Han Zhang ,&nbsp;Shihui Ying","doi":"10.1016/j.patcog.2025.111922","DOIUrl":"10.1016/j.patcog.2025.111922","url":null,"abstract":"<div><div>Dynamic functional connectivity (FC) analysis based on resting-state functional magnetic resonance imaging (rs-fMRI) is widely used for automated diagnosis of brain disorders. A large number of dynamic FC analysis methods rely on sliding window techniques to extract time-varying features of brain activity from localized time periods. However, these methods are sensitive to window parameters and individual differences, leading to significant variability in the extracted features and impacting the stability and accuracy of disease classification. Additionally, while dynamic graph learning holds promise in modeling time-varying brain networks, existing methods still encounter difficulties in effectively capturing spatio-temporal dynamic information. Therefore, in this paper we propose a multi-task dynamic graph learning framework (MT-DGL) to align FC trajectories and learn the spatio-temporal dynamic information for brain disease recognition. The MT-DGL mainly includes three parts: (1) SPD-valued FC trajectory alignment module for overcoming the model’s dependence on sliding window parameters and mitigating the impact of asynchrony in execution rates across individuals, (2) Mamba-based multi-scale dynamic graph learning module for extracting spatio-temporal dynamic features from fMRI time series, and (3) multi-scale fusion and multi-task learning strategy to enhance the model’s understanding of age-related brain FC changes and improve the effectiveness of brain disorder identification. Experimental results indicate that the proposed method exhibits excellent performance in several publicly available fMRI datasets. Specifically, on the largest site in the ABIDE dataset, the accuracy and area under the curve reached 73.9% and 74.9%, respectively.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 111922"},"PeriodicalIF":7.5,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523906","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}

{"title":"Improve ranking algorithms based on matrix factorization in rating systems","authors":"Shuyan Chen ,&nbsp;Shengli Zhang ,&nbsp;Gengzhong Zheng","doi":"10.1016/j.patcog.2025.112011","DOIUrl":"10.1016/j.patcog.2025.112011","url":null,"abstract":"<div><div>The proliferation of the Internet has led to an increase in the usage of rating systems. Inspired by matrix factorization, we present two improved iterative ranking algorithms called L1-AVG-RMF and AA-RMF for rating systems. In the new algorithms, the missing ratings are estimated by matrix factorization before applying traditional ranking algorithms. Theoretical analysis illustrates that the proposed algorithms have a better accuracy and robustness. And it is also demonstrated by Experiments with synthetic and real data. Additionally, experimental results also show that L1-AVG-RMF has superior effectiveness and robustness compared to some other ranking algorithms. Our findings emphasize the potential benefits of applying matrix factorization to ranking algorithms.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"170 ","pages":"Article 112011"},"PeriodicalIF":7.5,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548747","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}