Pattern Recognition Letters最新文献

{"title":"Summarized knowledge guidance for single-frame temporal action localization","authors":"Jinrong Sheng ,&nbsp;Ao Li ,&nbsp;Yongxin Ge","doi":"10.1016/j.patrec.2025.02.027","DOIUrl":"10.1016/j.patrec.2025.02.027","url":null,"abstract":"<div><div>Single-frame temporal action localization has garnered attention in the computer vision community. Existing methods address annotation sparsity by generating dense pseudo labels within individual videos, but disregard the variable representation from intra-class action instances, resulting in inferior completeness localization. In this paper, we propose to model intra-class relationships by using Summarized Knowledge Guidance (SKG). Specifically, we initially design a learnable memory bank to summarize annotated single-frame knowledge for each class. Then, we introduce two corresponding components, i.e., the knowledge propagation module (KPM) and the knowledge refinement module (KRM), for intra-class guidance. In KPM, we propagate summarized knowledge for feature-level enhancement through bipartite matching. In KRM, summarized knowledge is presented as confident pseudo positive samples for label-level refinement in a contrastive learning manner. Extensive experiments and ablation studies on the THUMOS14, GTEA and BEOID reveal that our method significantly outperforms state-of-the-art methods.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"191 ","pages":"Pages 31-36"},"PeriodicalIF":3.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562378","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}

{"title":"Beyond the known: Enhancing Open Set Domain Adaptation with unknown exploration","authors":"Lucas Fernando Alvarenga e Silva ,&nbsp;Samuel Felipe dos Santos ,&nbsp;Nicu Sebe ,&nbsp;Jurandy Almeida","doi":"10.1016/j.patrec.2024.12.010","DOIUrl":"10.1016/j.patrec.2024.12.010","url":null,"abstract":"<div><div>Convolutional neural networks (CNNs) can learn directly from raw data, resulting in exceptional performance across various research areas. However, factors present in non-controllable environments such as unlabeled datasets with varying levels of domain and category shift can reduce model accuracy. The Open Set Domain Adaptation (OSDA) is a challenging problem that arises when both of these issues occur together. Existing OSDA approaches in literature only align known classes or use supervised training to learn unknown classes as a single new category. In this work, we introduce a new approach to improve OSDA techniques by extracting a set of high-confidence unknown instances and using it as a hard constraint to tighten the classification boundaries. Specifically, we use a new loss constraint that is evaluated in three different ways: (1) using <em>pristine</em> negative instances directly; (2) using data augmentation techniques to create randomly <em>transformed</em> negatives; and (3) with <em>generated</em> synthetic negatives containing adversarial features. We analyze different strategies to improve the discriminator and the training of the Generative Adversarial Network (GAN) used to generate synthetic negatives. We conducted extensive experiments and analysis on OVANet using three widely-used public benchmarks, the Office-31, Office-Home, and VisDA datasets. We were able to achieve similar H-score to other state-of-the-art methods, while increasing the accuracy on unknown categories.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"189 ","pages":"Pages 265-272"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520023","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}

{"title":"Convolutional Spiking Neural Networks targeting learning and inference in highly imbalanced datasets","authors":"Bernardete Ribeiro,&nbsp;Francisco Antunes,&nbsp;Dylan Perdigão,&nbsp;Catarina Silva","doi":"10.1016/j.patrec.2024.08.002","DOIUrl":"10.1016/j.patrec.2024.08.002","url":null,"abstract":"<div><div>Spiking Neural Networks (SNNs) are regarded as the next frontier in AI, as they can be implemented on neuromorphic hardware, paving the way for advancements in real-world applications in the field. SNNs provide a biologically inspired solution that is event-driven, energy-efficient and sparse. While showing promising results, there are challenges that need to be addressed. For example, the design-build-evaluate process for integrating the architecture, learning, hyperparameter optimization and inference need to be tailored to a specific problem. This is particularly important in critical high-stakes industries such as finance services. In this paper, we present SpikeConv, a novel deep Convolutional Spiking Neural Network (CSNN), and investigate this process in the context of a highly imbalanced online bank account opening fraud problem. Our approach is compared with Deep Spiking Neural Networks (DSNNs) and Gradient Boosting Decision Trees (GBDT) showing competitive results.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"189 ","pages":"Pages 241-247"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946011","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}

{"title":"Exploring percolation features with polynomial algorithms for classifying Covid-19 in chest X-ray images","authors":"Guilherme F. Roberto ,&nbsp;Danilo C. Pereira ,&nbsp;Alessandro S. Martins ,&nbsp;Thaína A.A. Tosta ,&nbsp;Carlos Soares ,&nbsp;Alessandra Lumini ,&nbsp;Guilherme B. Rozendo ,&nbsp;Leandro A. Neves ,&nbsp;Marcelo Z. Nascimento","doi":"10.1016/j.patrec.2024.07.022","DOIUrl":"10.1016/j.patrec.2024.07.022","url":null,"abstract":"<div><div>Covid-19 is a severe illness caused by the Sars-CoV-2 virus, initially identified in China in late 2019 and swiftly spreading globally. Since the virus primarily impacts the lungs, analyzing chest X-rays stands as a reliable and widely accessible means of diagnosing the infection. In computer vision, deep learning models such as CNNs have been the main adopted approach for detection of Covid-19 in chest X-ray images. However, we believe that handcrafted features can also provide relevant results, as shown previously in similar image classification challenges. In this study, we propose a method for identifying Covid-19 in chest X-ray images by extracting and classifying local and global percolation-based features. This technique was tested on three datasets: one comprising 2,002 segmented samples categorized into two groups (Covid-19 and Healthy); another with 1,125 non-segmented samples categorized into three groups (Covid-19, Healthy, and Pneumonia); and a third one composed of 4,809 non-segmented images representing three classes (Covid-19, Healthy, and Pneumonia). Then, 48 percolation features were extracted and give as input into six distinct classifiers. Subsequently, the AUC and accuracy metrics were assessed. We used the 10-fold cross-validation approach and evaluated lesion sub-types via binary and multiclass classification using the Hermite polynomial classifier, a novel approach in this domain. The Hermite polynomial classifier exhibited the most promising outcomes compared to five other machine learning algorithms, wherein the best obtained values for accuracy and AUC were 98.72% and 0.9917, respectively. We also evaluated the influence of noise in the features and in the classification accuracy. These results, based in the integration of percolation features with the Hermite polynomial, hold the potential for enhancing lesion detection and supporting clinicians in their diagnostic endeavors.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"189 ","pages":"Pages 248-255"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Section on SIBGRAPI 2023","authors":"Thales Sehn Körting ,&nbsp;Esteban Walter Gonzalez Clua ,&nbsp;Rogerio Feris ,&nbsp;Fernando Vieira Paulovich","doi":"10.1016/j.patrec.2025.02.023","DOIUrl":"10.1016/j.patrec.2025.02.023","url":null,"abstract":"","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"189 ","pages":"Page 264"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519647","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}

{"title":"Incremental watershed cuts: Interactive segmentation algorithm with parallel strategy","authors":"Quentin Lebon ,&nbsp;Josselin Lefèvre ,&nbsp;Jean Cousty ,&nbsp;Benjamin Perret","doi":"10.1016/j.patrec.2024.12.005","DOIUrl":"10.1016/j.patrec.2024.12.005","url":null,"abstract":"<div><div>In this article, we design an incremental method for computing seeded watershed cuts for interactive image segmentation. We propose an algorithm based on the hierarchical image representation called the binary partition tree to compute a seeded watershed cut. Additionally, we leverage properties of minimum spanning forests to introduce a parallel method for labeling a connected component. We show that those algorithms fits perfectly in an interactive segmentation process by handling user interactions, seed addition or removal, in linear time with respect to the number of affected pixels. Run time comparisons with several state-of-the-art interactive and non-interactive watershed methods show that the proposed method can handle user interactions much faster than previous methods with a significant speedup ranging from 10 to 60 on both 2D and 3D images, thus improving the user experience on large images.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"189 ","pages":"Pages 256-263"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter-separability and intra-concentration to enhance stochastic neural network adversarial robustness","authors":"Omar Dardour ,&nbsp;Eduardo Aguilar ,&nbsp;Petia Radeva ,&nbsp;Mourad Zaied","doi":"10.1016/j.patrec.2025.02.028","DOIUrl":"10.1016/j.patrec.2025.02.028","url":null,"abstract":"<div><div>It has been shown that Deep Neural Networks can be easily fooled by adding an imperceptible noise termed as adversarial examples. To address this issue, in this paper, we propose a defense method called Inter-Separability and Intra-Concentration Stochastic Neural Networks (ISIC-SNN). The suggested ISIC-SNN method learns to enlarge between different label representations using label embedding and a designed inter-separability loss. It introduces uncertainty in the features latent space using the variational information bottleneck method and enhances compactness in stochastic features using intra-concentration loss. Finally, it uses dot-product similarity between stochastic feature representations and label embedding to classify features. ISIC-SNN learns in standard training which is much more efficient than adversarial training. Experiments on datasets SVHN, CIFAR-10 and CIFAR-100 demonstrate the superior defensive capability of the proposed method compared to various SNNs defensive methods.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"191 ","pages":"Pages 1-7"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534496","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}

{"title":"Editorial of the special section: CIARP 2023","authors":"Inês Domingues,&nbsp;Verónica Vasconcelos,&nbsp;Simão Paredes","doi":"10.1016/j.patrec.2024.12.013","DOIUrl":"10.1016/j.patrec.2024.12.013","url":null,"abstract":"","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"189 ","pages":"Pages 239-240"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518960","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}

{"title":"Interactive shape estimation for densely cluttered objects","authors":"Jiangfan Ran,&nbsp;Haibin Yan","doi":"10.1016/j.patrec.2025.02.026","DOIUrl":"10.1016/j.patrec.2025.02.026","url":null,"abstract":"<div><div>Accurately recognizing the shape of objects in dense and cluttered scenes is important for robots to perform a variety of manipulation tasks, such as grasping and packing. However, the performance of previous shape estimation methods is not satisfactory due to the heavy occlusion between objects in dense clutter. In this paper, we propose an interactive exploration framework to estimate the shape of densely cluttered objects. Our framework utilizes pixel-wise uncertainty to generate efficient interactions, allowing to achieve a better trade-off between the shape estimation accuracy and the interaction cost. Specifically, the extracted features are utilized as network weights to predict the confidence of each proposal located on the surface of the objects. Proposals with higher confidence are considered reliable results for shape estimation. Meanwhile, we obtain the uncertainty of shape and scale estimation based on the confidence of each proposal, and further propose the adaptive fusion strategy to construct the pixel-wise estimation uncertainty height map. In addition, our proposed interaction strategy leverages the uncertainty height map to generate effective interaction actions to significantly improve the shape estimation accuracy for severely occluded objects. Therefore, the optimal accuracy-efficiency trade-off for shape estimation in dense clutter is achieved by iterating the shape estimation and interaction actions. Extensive experimental results verify the effectiveness of the proposed approach. Under challenging cases, the proposed approach has 66.7% and 52.0% less average Chamfer distance than direct estimation and random interaction, respectively.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"191 ","pages":"Pages 8-14"},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534494","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}

{"title":"Multi-corpus emotion recognition method based on cross-modal gated attention fusion","authors":"Elena Ryumina ,&nbsp;Dmitry Ryumin ,&nbsp;Alexandr Axyonov ,&nbsp;Denis Ivanko ,&nbsp;Alexey Karpov","doi":"10.1016/j.patrec.2025.02.024","DOIUrl":"10.1016/j.patrec.2025.02.024","url":null,"abstract":"<div><div>Automatic emotion recognition techniques are critical to natural human–computer interaction. However, current methods suffer from limited applicability due to their tendency to overfit on single-corpus datasets. It reduces real-world effectiveness of the methods when faced with new unseen corpora. We propose the first multi-corpus multimodal emotion recognition method with high generalizability evaluated through a leave-one-corpus-out protocol. The method uses three fine-tuned encoders per modality (audio, video, and text) and a decoder employing a context-independent gated attention to combine features from all three modalities. The research is conducted on four benchmark corpora: MOSEI, MELD, IEMOCAP, and AFEW. The proposed method achieves the state-of-the-art results on these corpora and establishes the first baseline for multi-corpus studies. We demonstrate that due to the MELD rich emotional expressiveness across three modalities, the models trained on it exhibit the best generalization ability when applied to other corpora used. We also reveal that the AFEW annotation better correlates with the annotations of MOSEI, MELD, and IEMOCAP, as well as shows the best cross-corpus performance as it is consistent with the widely-accepted theories of basic emotions.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"190 ","pages":"Pages 192-200"},"PeriodicalIF":3.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521215","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}