Pattern Recognition Letters最新文献

FAM: Adaptive federated meta-learning for MRI data FAM：针对核磁共振成像数据的自适应联合元学习

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.09.018

{"title":"FAM: Adaptive federated meta-learning for MRI data","authors":"","doi":"10.1016/j.patrec.2024.09.018","DOIUrl":"10.1016/j.patrec.2024.09.018","url":null,"abstract":"<div><div>Federated learning enables multiple clients to collaborate to train a model without sharing data. Clients with insufficient data or data diversity participate in federated learning to learn a model with superior performance. MRI data suffers from inadequate data and different data distribution due to differences in MRI scanners and client characteristics. Also, privacy concerns preclude data sharing. In this work, we propose a novel adaptive federated meta-learning (FAM) mechanism for collaboratively learning a single global model, which is personalized locally on individual clients. The learnt sparse global model captures the common features in the MRI data across clients. This model is grown on each client to learn a personalized model by capturing additional client-specific parameters from local data. Experimental results on multiple data sets show that the personalization process at each client quickly converges using a limited number of epochs. The personalized client models outperformed the locally trained models, demonstrating the efficacy of the FAM mechanism. Additionally, the FAM-based sparse global model has fewer parameters that require less communication overhead during federated learning. This makes the model viable for networks with limited resources.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EDS: Exploring deeper into semantics for video captioning EDS：深入探索视频字幕语义

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.09.017

{"title":"EDS: Exploring deeper into semantics for video captioning","authors":"","doi":"10.1016/j.patrec.2024.09.017","DOIUrl":"10.1016/j.patrec.2024.09.017","url":null,"abstract":"<div><div>Efficiently leveraging semantic information is crucial for advancing video captioning in recent years. But, prevailing approaches that involve designing various Part-of-Speech (POS) tags as prior information lack essential linguistic knowledge guidance throughout the training procedure, particularly in the context of POS and initial description generation. Furthermore, the restriction to a single source of semantic information ignores the potential for varied interpretations inherent in each video. To solve these problems, we propose the Exploring Deeper into Semantics (EDS) method for video captioning. EDS comprises three feasible modules that focus on semantic information. Specifically, we propose the Semantic Supervised Generation (SSG) module. It integrates semantic information as a prior, and facilitates enriched interrelations among words for POS supervision. A novel Similarity Semantic Extension (SSE) module is proposed to employ a query-based semantic expansion for collaboratively generating fine-grained content. Additionally, the proposed Input Semantic Enhancement (ISE) module provides a strategy for mitigating the information constraints faced during the initial phase of word generation. The experiments conducted show that, by exploiting semantic information through supervision, extension, and enhancement, EDS not only yields promising results but also underlines the effectiveness. Code will be available at <span><span>https://github.com/BradenJoson/EDS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepMarkerNet: Leveraging supervision from the Duchenne Marker for spontaneous smile recognition DeepMarkerNet：利用 Duchenne 标记的监督进行自发微笑识别

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.09.015

{"title":"DeepMarkerNet: Leveraging supervision from the Duchenne Marker for spontaneous smile recognition","authors":"","doi":"10.1016/j.patrec.2024.09.015","DOIUrl":"10.1016/j.patrec.2024.09.015","url":null,"abstract":"<div><div>Distinguishing between spontaneous and posed smiles from videos poses a significant challenge in pattern classification literature. Researchers have developed feature-based and deep learning-based solutions for this problem. To this end, deep learning outperforms feature-based methods. However, certain aspects of feature-based methods could improve deep learning methods. For example, previous research has shown that Duchenne Marker (or D-Marker) features from the face play a vital role in spontaneous smiles, which can be useful to improve deep learning performances. In this study, we propose a deep learning solution that leverages D-Marker features to improve performance further. Our multi-task learning framework, named DeepMarkerNet, integrates a transformer network with the utilization of facial D-Markers for accurate smile classification. Unlike past methods, our approach simultaneously predicts the class of the smile and associated facial D-Markers using two different feed-forward neural networks, thus creating a symbiotic relationship that enriches the learning process. The novelty of our approach lies in incorporating supervisory signals from the pre-calculated D-Markers (instead of as input in previous works), harmonizing the loss functions through a weighted average. In this way, our training utilizes the benefits of D-Markers, but the inference does not require computing the D-Marker. We validate our model’s effectiveness on four well-known smile datasets: UvA-NEMO, BBC, MMI facial expression, and SPOS datasets, and achieve state-of-the-art results.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of visual SLAM algorithms in unstructured planetary-like and agricultural environments 评估非结构化类地行星和农业环境中的视觉 SLAM 算法

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.09.025

引用次数: 0

Innovative multi-stage matching for counting anything 创新的多级匹配，可用于任何计数

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.09.014

{"title":"Innovative multi-stage matching for counting anything","authors":"","doi":"10.1016/j.patrec.2024.09.014","DOIUrl":"10.1016/j.patrec.2024.09.014","url":null,"abstract":"<div><div>Few-shot counting (FSC) is the task of counting the number of objects in an image that belong to the same category, by using a provided exemplar pattern. By replacing the exemplar, we can effectively count anything, even in cases where we have no prior knowledge of that category’s exemplar. However, due to the variations within the same category and the impact of inter-class similarity, it is challenging to achieve accurate intra-class similarity matching using conventional similarity comparison methods. To tackle these issues, we propose a novel few-shot counting method called Multi-stage Exemplar Attention Match Network (MEAMNet), which increases the accuracy of matching, reduces the impact of noise, and enhances similarity feature matching. Specifically, we propose a multi-stage matching strategy to obtain more stable and effective matching results by acquiring similar feature in different feature spaces. In addition, we propose a novel feature matching module called Exemplar Attention Match (EAM). With this module, the intra-class similarity representation in each stage will be enhanced to achieve a better matching of the key feature. Experimental results indicate that our method not only significantly surpasses the state-of-the-art (SOTA) methods in most evaluation metrics on the FSC-147 dataset but also achieves comprehensive superiority on the CARPK dataset. This highlights the outstanding accuracy and stability of our matching performance, as well as its exceptional transferability. We will release the code at <span><span>https://github.com/hzg0505/MEAMNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DDOWOD: DiffusionDet for open-world object detection DDOWOD：用于开放世界物体检测的 DiffusionDet

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.10.002

{"title":"DDOWOD: DiffusionDet for open-world object detection","authors":"","doi":"10.1016/j.patrec.2024.10.002","DOIUrl":"10.1016/j.patrec.2024.10.002","url":null,"abstract":"<div><div>Open-world object detection (OWOD) poses a significant challenge in computer vision, requiring models to detect unknown objects and incrementally learn new categories. To explore this field, we propose the DDOWOD based on the DiffusionDet. It is more likely to cover unknown objects hidden in the background and can reduce the model’s bias towards known class objects during training due to its ability to randomly generate boxes and reconstruct the characteristics of the GT from them. Also, to improve the insufficient quality of pseudo-labels which leads to reduced accuracy in recognizing unknown classes, we use the Segment Anything Model (SAM) as the teacher model in distillation learning to endow DDOWOD with rich visual knowledge. Surprisingly, compared to other existing models, our DDOWOD is more suitable for using SAM as the teacher. Furthermore, we proposed the Stepwise distillation (SD) which is a new incremental learning method specialized for our DDOWOD to avoid catastrophic forgetting during the training. Our approach utilizes all previously trained models from past tasks rather than solely relying on the last one. DDOWOD has achieved excellent performance. U-Recall is 53.2, 51.5, 50.7 in OWOD split and U-AP is 21.9 in IntensiveSet.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Label-noise learning via uncertainty-aware neighborhood sample selection 通过不确定性感知邻域样本选择进行标签噪声学习

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.09.012

引用次数: 0

Explainable hypergraphs for gait based Parkinson classification 基于帕金森病步态分类的可解释超图

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.09.026

{"title":"Explainable hypergraphs for gait based Parkinson classification","authors":"","doi":"10.1016/j.patrec.2024.09.026","DOIUrl":"10.1016/j.patrec.2024.09.026","url":null,"abstract":"<div><div>Parkinson Disease (PD) classification using Vertical Ground Reaction Force (VGRF) sensors can help in unobtrusive detection and monitoring of PD patients. State-of-the-art (SOTA) research in PD classification reveals that Deep Learning (DL), at the expense of explainability, performs better than Shallow Learning (SL). In this paper, we introduce a novel explainable weighted hypergraph, where the interconnections of the SOTA features are exploited, leading to more discriminative derived features, and thereby, forming an SL arm. In parallel, we create a DL arm consisting of ResNet architecture to learn the spatio-temporal patterns of the VGRF signals. Probabilities of PD classification scores from the SL and the DL arms are adaptively fused to create a hybrid pipeline. The pipeline achieves an AUC value of 0.979 on the Physionet Parkinson Dataset. This AUC value is found to be superior to the SL as well as the DL arm used in isolation, yielding respective AUCs of 0.878 and 0.852. The proposed pipeline demonstrates explainability through improved permutation feature importance and contrasting examples of use cases, where incorrect misclassification of the DL arm gets rectified by the SL arm and vice versa. We further demonstrate that our solution achieves comparable performance with SOTA methods. To the best of our knowledge, this is the first approach to analyze PD classification with a hypergraph based xAI (Explainable Artificial Intelligence).</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of a differentiable L-1 norm for pattern recognition and machine learning 设计用于模式识别和机器学习的可微分 L-1 准则

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.09.020

{"title":"Design of a differentiable L-1 norm for pattern recognition and machine learning","authors":"","doi":"10.1016/j.patrec.2024.09.020","DOIUrl":"10.1016/j.patrec.2024.09.020","url":null,"abstract":"<div><div>In various applications of pattern recognition, feature selection, and machine learning, L-1 norm is used as either an objective function or a regularizer. Mathematically, L-1 norm has unique characteristics that make it attractive in machine learning, feature selection, optimization, and regression. Computationally, however, L-1 norm presents a hurdle as it is non-differentiable, making the process of finding a solution difficult. Existing approach therefore relies on numerical approaches. In this work we designed an L-1 norm that is differentiable and, thus, has an analytical solution. The differentiable L-1 norm removes the absolute sign in the conventional definition and is everywhere differentiable. The new L-1 norm is almost everywhere linear, a desirable feature that is also present in the conventional L-1 norm. The only limitation of the new L-1 norm is that near zero, its behavior is not linear, hence we consider the new L-1 norm quasi-linear. Being differentiable, the new L-1 norm and its quasi-linear variation make them amenable to analytic solutions. Hence, it can facilitate the development and implementation of many algorithms involving L-1 norm. Our tests validate the capability of the new L-1 norm in various applications.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive feature alignment for adversarial training 对抗训练的自适应特征对齐

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2024-10-01 DOI: 10.1016/j.patrec.2024.10.004

{"title":"Adaptive feature alignment for adversarial training","authors":"","doi":"10.1016/j.patrec.2024.10.004","DOIUrl":"10.1016/j.patrec.2024.10.004","url":null,"abstract":"<div><div>Recent studies reveal that Convolutional Neural Networks (CNNs) are typically vulnerable to adversarial attacks. Many adversarial defense methods have been proposed to improve the robustness against adversarial samples. Moreover, these methods can only defend adversarial samples of a specific strength, reducing their flexibility against attacks of varying strengths. Moreover, these methods often enhance adversarial robustness at the expense of accuracy on clean samples. In this paper, we first observed that features of adversarial images change monotonically and smoothly w.r.t the rising of attacking strength. This intriguing observation suggests that features of adversarial images with various attacking strengths can be approximated by interpolating between the features of adversarial images with the strongest and weakest attacking strengths. Due to the monotonicity property, the interpolation weight can be easily learned by a neural network. Based on the observation, we proposed the adaptive feature alignment (AFA) that automatically align features to defense adversarial attacks of various attacking strengths. During training, our method learns the statistical information of adversarial samples with various attacking strengths using a dual batchnorm architecture. In this architecture, each batchnorm process handles samples of a specific attacking strength. During inference, our method automatically adjusts to varying attacking strengths by linearly interpolating the dual-BN features. Unlike previous methods that need to either retrain the model or manually tune hyper-parameters for a new attacking strength, our method can deal with arbitrary attacking strengths with a single model without introducing any hyper-parameter. Additionally, our method improves the model robustness against adversarial samples without incurring much loss of accuracy on clean images. Experiments on CIFAR-10, SVHN and tiny-ImageNet datasets demonstrate that our method outperforms the state-of-the-art under various attacking strengths and even improve accuracy on clean samples. Code will be made open available upon acceptance.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0