Pattern Recognition最新文献

{"title":"Infrared and visible image fusion via dual encoder based on dense connection","authors":"Quan Lu,&nbsp;Hongbin Zhang,&nbsp;Linfei Yin","doi":"10.1016/j.patcog.2025.111476","DOIUrl":"10.1016/j.patcog.2025.111476","url":null,"abstract":"<div><div>Aiming at the problems of information loss and edge blurring due to the loss of gradient features that tend to occur during the fusion of infrared and visible images, this study proposes a dual encoder image fusion method (DEFusion) based on dense connectivity. The proposed method processes infrared and visible images by different means, therefore guaranteeing the best possible preservation of the features of the original image. A new progressive fusion strategy is constructed to ensure that the network is better able to capture the detailed information present in visible images while minimizing the gradient loss of the infrared image. Furthermore, a novel loss function that includes gradient loss and content loss, which ensures that the fusion results consider both the detailed information and gradient of the source image, is proposed in this study to facilitate the fusion process. The experimental results with the state-of-art methods on TNO and RoadScene datasets verify that the proposed method exhibits superior performance in most indices. The fused image exhibits excellent subjective contrast and clarity, providing a strong visual perception. The results of the comparison experiment demonstrate that this method exhibits favorable characteristics in terms of generalization and robustness.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111476"},"PeriodicalIF":7.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454213","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":"MDFCL: Multimodal data fusion-based graph contrastive learning framework for molecular property prediction","authors":"Xu Gong ,&nbsp;Maotao Liu ,&nbsp;Qun Liu ,&nbsp;Yike Guo ,&nbsp;Guoyin Wang","doi":"10.1016/j.patcog.2025.111463","DOIUrl":"10.1016/j.patcog.2025.111463","url":null,"abstract":"<div><div>Molecular property prediction is a critical task with substantial applications for drug design and repositioning. The multiplicity of molecular data modalities and paucity of labeled data present significant challenges that affect algorithmic performance in this domain. Nevertheless, conventional approaches typically focus on singular data modalities and ignore either hierarchical structural features or other data pattern information, leading to problems when expressing complex phenomena and relationships. Additionally, the scarcity of labeled data obstructs the accurate mapping of instances to labels in property prediction tasks. To address these issues, we propose the <strong>M</strong>ultimodal <strong>D</strong>ata <strong>F</strong>usion-based graph <strong>C</strong>ontrastive <strong>L</strong>earning framework (MDFCL) for molecular property prediction. Specifically, we incorporate exhaustive information from dual molecular data modalities, namely graph and sequence structures. Subsequently, adaptive data augmentation strategies are designed based on the molecular backbones and side chains for multimodal data. Built upon these augmentation strategies, we develop a graph contrastive learning framework and pre-train it with unlabeled data (<span><math><mo>∼</mo></math></span> 10M molecules). MDFCL is tested using 13 molecular property prediction benchmark datasets, demonstrating its effectiveness through empirical findings. In addition, a visualization study demonstrates that MDFCL can embed molecules into representative features and steer the distribution of molecular representations.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111463"},"PeriodicalIF":7.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436846","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":"AttriDiffuser: Adversarially enhanced diffusion model for text-to-facial attribute image synthesis","authors":"Wenfeng Song ,&nbsp;Zhongyong Ye ,&nbsp;Meng Sun ,&nbsp;Xia Hou ,&nbsp;Shuai Li ,&nbsp;Aimin Hao","doi":"10.1016/j.patcog.2025.111447","DOIUrl":"10.1016/j.patcog.2025.111447","url":null,"abstract":"<div><div>In the progressive domain of computer vision, generating high-fidelity facial images from textual descriptions with precision remains a complex challenge. While existing diffusion models have demonstrated capabilities in text-to-image synthesis, they often struggle with capturing intricate details from complex, multi-attribute textual descriptions, leading to entity or attribute loss and inaccurate combinations. We propose AttriDiffuser, a novel model designed to ensure that each entity and attribute in textual descriptions is distinctly and accurately represented in the synthesized images. AttriDiffuser utilizes a text-driven attribute diffusion adversarial model, enhancing the correspondence between textual attributes and image features. It incorporates an attribute-gating cross-attention mechanism seamlessly into the adversarial learning enhanced diffusion model. AttriDiffuser advances traditional diffusion models by integrating a face diversity discriminator, which augments adversarial training and promotes the generation of diverse yet precise facial images in alignment with complex textual descriptions. Our empirical evaluation, conducted on the renowned Multimodal VoxCeleb and CelebA-HQ datasets, and benchmarked against other state-of-the-art models, demonstrates AttriDiffuser’s superior efficacy. The results indicate its unparalleled capability to synthesize high-quality facial images with rigorous adherence to complex, multi-faceted textual descriptions, marking a significant advancement in text-to-facial attribute synthesis. Our code and model will be made publicly available at <span><span>https://github.com/sunmeng7/AttriDiffuser</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111447"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464532","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":"FedPnP: Personalized graph-structured federated learning","authors":"Arash Rasti-Meymandi,&nbsp;Ahmad Sajedi,&nbsp;Konstantinos N. Plataniotis","doi":"10.1016/j.patcog.2025.111455","DOIUrl":"10.1016/j.patcog.2025.111455","url":null,"abstract":"<div><div>In Personalized Federated Learning (PFL), current methods often fail to consider the fine-grained relationships between clients and their local datasets, hindering effective information exchange. Here, we propose “FedPnP”, a novel method that harnesses the inherent graph-based connections among clients. Clients linked by a graph tend to yield similar model responses to comparable input data. In the proposed FedPnP we present the graph-based optimization as an inverse problem. We then solve this optimization by employing a Half-Quadratic-Splitting technique (HQS) to divide it into two subproblems. The first ensures local model performance on respective datasets, acting as a data fidelity term, while the second promotes the smoothness of model weights on the graph. Notably, we present a structural proximal term in the first subproblem and demonstrate the integration of any graph denoiser in the second subproblem as a plug &amp; play solution. Experiments on CIFAR10, CIFAR100, FashionMNIST, and SVHN demonstrate FedPnP’s superiority over 10 state-of-the-art algorithms, with accuracy improvements ranging from 0.2% to 3%. Notably, FedPnP excels in handling highly heterogeneous data, a critical challenge in real-world PFL scenarios. Additional evaluations show that FedPnP performs consistently well across various denoisers, with the Heat filter delivering the best results. This bridge between PFL algorithms and inverse problems opens up the potential for cross-pollination of solutions, yielding superior algorithms for PFL tasks. The GitHub code is available at <span><span>https://github.com/arashrasti96/FedPnP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111455"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436847","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":"Knowledge-Driven Compositional Action Recognition","authors":"Yang Liu,&nbsp;Fang Liu,&nbsp;Licheng Jiao,&nbsp;Qianyue Bao,&nbsp;Shuo Li,&nbsp;Lingling Li,&nbsp;Xu Liu","doi":"10.1016/j.patcog.2025.111452","DOIUrl":"10.1016/j.patcog.2025.111452","url":null,"abstract":"<div><div>Human action often involves interaction with objects, so in action recognition, action labels can be defined by compositions of verbs and nouns. It is almost infeasible to collect and annotate enough training data for every possible composition in the real world. Therefore, the main challenge in compositional action recognition is to enable the model to understand “action-objects” compositions that have not been seen during training. We propose a Knowledge-Driven Composition Modulation Model (KCMM), which constructs unseen “action-objects” compositions to improve action recognition generalization. We first design a Grammar Knowledge-Driven Composition (GKC) module, which extracts the labels of verbs and nouns and their corresponding feature representations from compositional actions, and then modulates them under the guidance of grammatical rules to construct new “action-objects” actions. Subsequently, to verify the rationality of the new “action-objects” actions, we design a Common Knowledge-Driven Verification (CKV) module. This module extracts motion commonsense from ConceptNet and infuses it into the compositional labels to improve the comprehensiveness of the verification. It should be noted that GKC does not construct new videos, but directly composes verbs and nouns at the label and feature space to obtain new compositional action label-feature pairs. We conduct extensive experiments on Something-Else and NEU-I datasets, and our method significantly outperforms current state-of-the-art methods in both compositional settings and few-shot settings. The source code is available at <span><span>https://github.com/XDLiuyyy/KCMM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111452"},"PeriodicalIF":7.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480632","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":"An efficient and effective pore matching method using ResCNN descriptor and local outliers","authors":"Feng Liu ,&nbsp;Qiuheng Wang ,&nbsp;Yanfeng Xiao ,&nbsp;Linlin Shen","doi":"10.1016/j.patcog.2025.111446","DOIUrl":"10.1016/j.patcog.2025.111446","url":null,"abstract":"<div><div>With the advancement of high-resolution fingerprint scanners, sweat pores have emerged as a robust biometric feature for fingerprint representation and recognition. Numerous pore-matching algorithms have been developed to enhance the accuracy of automatic fingerprint recognition systems (AFRSs). However, existing models often suffer from inefficiencies and poor generalization performance. This article introduces a novel method that balances efficiency and effectiveness. After fingerprints are aligned and pores are annotated, a ResCNN-based pore descriptor is designed to capture both static and dynamic features of sweat pores, with an emphasis on inter-class differences and intra-class similarities. This leads to the generation of robust descriptors that can handle variations such as deformation and pressure changes. Additionally, the AdaLAM algorithm is refined to efficiently remove local outliers, which improves matching accuracy and reduces computational time. To adapt to different scenarios, different strategies are employed for partial and full fingerprint recognition. For partial fingerprints, the method addresses the challenge of small overlapping areas by incorporating distinctive pore matching results using AdaLAM. For full fingerprints, the method trains image descriptors and integrates fingerprint similarity with pore matching to further enhance accuracy. Experiments on the benchmark PolyU-HRF dataset demonstrate that the algorithm achieves an equal error rate (EER) of 1.71% for DBI (partial fingerprints) and 0.02% for DBII (full fingerprints). Compared to current state-of-the-art approaches, the method reduces the False Match Rate 1000 (FMR1000) by 38.88% for partial fingerprints and 100% for full fingerprints, with a speed improvement of approximately 90 times.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111446"},"PeriodicalIF":7.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428779","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":"Transformer-based material recognition via short-time contact sensing","authors":"Zhenyang Liu ,&nbsp;Yitian Shao ,&nbsp;Qiliang Li ,&nbsp;Jingyong Su","doi":"10.1016/j.patcog.2025.111448","DOIUrl":"10.1016/j.patcog.2025.111448","url":null,"abstract":"<div><div>Embodied intelligence needs haptic sensing for spontaneous and accurate material recognition. The haptic sensing module of an intelligent system can acquire material data through either sliding or tapping motions. Sliding movements are commonly adopted for collecting the spatial frequency features of the material but are less time-efficient than tapping. Here, we introduce a haptic sensing framework that can extract material features from short-time tapping signals. To improve the performance of material recognition, transfer learning is used by transferring the knowledge of pretrained model training on large-scale images into haptic sensing. The waveforms of the tapping signals are encoded as images to be input into a transformer model tailored for image recognition tasks. The encoding employs line graph image-point scaling, effectively accommodating signals that exhibit large variations in magnitude and temporal structures. Using the LMT haptic material database containing sliding and tapping data, our study showcases the efficacy of the proposed framework in material recognition tasks, especially for short-time (<span><math><mo>≤</mo></math></span> <!--> <!-->60 ms) sensing via tapping interactions. The findings provide fresh insights into haptic sensing technologies and may help improve the physical interaction capabilities of embodied intelligence, such as medical and rescue robots.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111448"},"PeriodicalIF":7.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454211","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":"Real-time dual-eye collaborative eyeblink detection with contrastive learning","authors":"Hanli Zhao ,&nbsp;Yu Wang ,&nbsp;Wanglong Lu ,&nbsp;Zili Yi ,&nbsp;Jun Liu ,&nbsp;Minglun Gong","doi":"10.1016/j.patcog.2025.111440","DOIUrl":"10.1016/j.patcog.2025.111440","url":null,"abstract":"<div><div>Real-time detection of eyeblinks in uncontrolled settings is crucial for applications such as driver fatigue monitoring, face spoofing prevention, and emotion analysis. This task, however, is significantly challenged by variations in facial poses, motion blur, and inconsistent lighting conditions, which frequently lead traditional facial landmark analysis tools to perform poorly, especially in low-light and dynamic environments. often lead to imprecise localization of key regions of interest, undermining the effectiveness of subsequent blink detection. To address these issues, we have developed a novel real-time dual-eye collaborative eyeblink detection method that incorporates contrastive learning. Our approach includes a consistent eye feature embedding technique that minimizes the impact of adverse lighting and extraneous noise during feature extraction. Through contrastive learning, we align feature embeddings of coarsely captured, low-light eye patches with those from finely detailed, well-lit patches. Furthermore, to enhance eyeblink detection and reduce false identifications of eye regions, we exploit the natural synchrony in blink patterns between the left and right eyes. We introduce a dual-eye collaborative spatio-temporal attention mechanism that captures both the inter-eye correlations and the temporal dynamics across sequences. Our collaborative learning approach maximizes the inherent synchrony and cooperation between the two eyes, significantly improving detection accuracy. Extensive experiments on three datasets and their low-light variants demonstrate that our method operates in real-time, adjusts effectively to varying lighting conditions, and performs robustly in untrimmed video scenarios.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111440"},"PeriodicalIF":7.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422185","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":"Learning from not-all-negative pairwise data and unlabeled data","authors":"Shuying Huang,&nbsp;Junpeng Li,&nbsp;Changchun Hua,&nbsp;Yana Yang","doi":"10.1016/j.patcog.2025.111442","DOIUrl":"10.1016/j.patcog.2025.111442","url":null,"abstract":"<div><div>A weakly-supervised approach utilizing data pairs with comparative or similarity/dissimilarity information has gained popularity in various fields due to its cost-effectiveness. However, the challenge of dealing with not all negative (<em>i.e</em>., pairwise data that includes at least one positive) or not all positive (<em>i.e</em>., pairwise data that includes at least one negative) data pairs has not been specifically addressed by any algorithm. To overcome this bottleneck, this paper explores a novelty weakly-supervision framework of learning from pairwise data that includes at least one positive and unlabeled data points (<span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>p</mi><mi>o</mi><mi>s</mi></mrow></msub><mi>U</mi></mrow></math></span>) as a representative. The provided pairwise data ensures that each data pair contains at least one positive data point. Unlabeled data refers to data without labeled information. Firstly, this paper shows an unbiased risk estimator for <span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>p</mi><mi>o</mi><mi>s</mi></mrow></msub><mi>U</mi></mrow></math></span> data and use risk correction functions to mitigate the overfitting caused by negative terms. In addition, the estimation error bound is established for the empirical risk minimizer and the optimal convergence rate is obtained. Finally, the detailed experimental process and results are presented to demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111442"},"PeriodicalIF":7.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428780","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":"Adaptive division and priori reinforcement part learning network for vehicle re-identification","authors":"Xiaoying Zhou ,&nbsp;Xi Li ,&nbsp;Houren Zhou ,&nbsp;Xiyu Pang ,&nbsp;Jiachen Tian ,&nbsp;Xiushan Nie ,&nbsp;Cheng Wang ,&nbsp;Yilong Yin","doi":"10.1016/j.patcog.2025.111453","DOIUrl":"10.1016/j.patcog.2025.111453","url":null,"abstract":"<div><div>Vehicle Re-identification (Re-ID) recognizes images belonging to the same vehicle from a large number of vehicle images captured by different cameras. Learning subtle discriminative information in parts is key to meeting the challenge of small interclass difference in vehicle Re-ID. Methods that use additional models and annotations can accurately locate parts to learn part-level features, however, they require more computational and labor costs. The rigid division strategy can fully utilize the priori information to learn interpretable part features, but it breaks semantic continuity of parts and makes the interference of noise larger. In this paper, we propose an adaptive division part learning module (ADP). It adaptively generates spatially nonoverlapping diversity part masks based on multi-head self-attention semantic aggregation process to decouple part learning. It lets each head focus on the semantic aggregation of different parts and does not need to resort to additional annotations or models. In addition, we propose a priori reinforcement parts learning module (PRP). PRP establishes links between one part and all parts obtained by rigid division through a self-attention mechanism. This process emphasizes important detail information within the part from a global viewpoint and suppresses noise interference. Finally, based on the above two modules, we construct an adaptive division and priori reinforcement part learning network (ADPRP-Net) to learn granular features in an adaptive and priori way to deal with the challenge of small interclass difference. Experimental results on the VeRi-776 and VehicleID datasets show that ADPRP-Net achieves excellent vehicle Re-ID performance. And on the small test subset of the VehicleID dataset, ADPRP-Net has 3.3% higher Rank-1 accuracy and 1.7% higher Rank-5 accuracy compared to the state-of-the-art (SOTA) transformer-based Re-ID method (DSN). Code is available at <span><span>https://github.com/zxy1116/ADPRP-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"163 ","pages":"Article 111453"},"PeriodicalIF":7.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428781","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}