Pattern Recognition最新文献_第2页

Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced Training 采用自标签增强训练的可扩展自适应图神经网络

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-23 DOI: 10.1016/j.patcog.2024.111210

Chuxiong Sun , Jie Hu , Hongming Gu , Jinpeng Chen , Wei Liang , Mingchuan Yang

{"title":"Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced Training","authors":"Chuxiong Sun , Jie Hu , Hongming Gu , Jinpeng Chen , Wei Liang , Mingchuan Yang","doi":"10.1016/j.patcog.2024.111210","DOIUrl":"10.1016/j.patcog.2024.111210","url":null,"abstract":"<div><div>Although GNNs have achieved success in semi-supervised graph learning tasks, common GNNs suffer from expensive message passing during each epoch and the exponentially growing receptive field occupying too much memory, especially on large graphs. Neighbor sampling techniques can reduce GNNs’ memory footprints, but they encounter either redundant computation or incomplete edges. Some simplified GNNs decouple graph convolutions and feature transformations to reduce computation in training. However, only a part of them can scale to large graphs without neighbor sampling techniques, which can be concluded as decoupled GNNs. Nevertheless, they either only utilize the last convolution output or simply add multi-hop features with uniform weights, which limits their expressiveness. In this paper, we refine the pipeline of decoupled GNNs and propose Scalable and Adaptive Graph Neural Networks (SAGN), which effectively leverages multi-hop information with a scalable attention mechanism. Moreover, we generalize the input of decoupled GNNs to view another classical technique, label propagation, as a special case of decoupled GNNs and propose decoupled label trick (DecLT) to incorporate label information into decoupled GNNs. Furthermore, by incorporating self-training technique, we further propose the Self-Label-Enhanced (SLE) training framework, leveraging pseudo labels to simultaneously augment the training set and improve label propagation. Extensive experiments show that SAGN outperforms other baselines, and that DecLT and SLE can consistently and significantly improve all types of models on semi-supervised node classification tasks. Many top-ranked models on Open Graph Benchmark (OGB) leaderboard adopt our methods as the main backbone.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"160 ","pages":"Article 111210"},"PeriodicalIF":7.5,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722839","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

Consistency and label constrained transfer low-rank representation for cross-light finger vein recognition

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-23 DOI: 10.1016/j.patcog.2024.111208

Zhen Zhang , Lu Yang , Kuikui Wang , Xiaoming Xi , Xiushan Nie , Gongping Yang , Yilong Yin

{"title":"Consistency and label constrained transfer low-rank representation for cross-light finger vein recognition","authors":"Zhen Zhang , Lu Yang , Kuikui Wang , Xiaoming Xi , Xiushan Nie , Gongping Yang , Yilong Yin","doi":"10.1016/j.patcog.2024.111208","DOIUrl":"10.1016/j.patcog.2024.111208","url":null,"abstract":"<div><div>Finger vein sensors are embedded into all kinds of electronic devices for personal identification, and the upgrading of sensors is unavoidable. Therefore, the concern about cross-sensor finger vein recognition is raised recently. However, little attention is paid to cross-sensor finger vein recognition. The imaging light variation is one main difference between different sensors, and it brings large image differences, seriously degrading finger vein recognition performance. This paper focuses on cross-light finger vein recognition problem, in which we assume that the training and testing finger vein images are captured by different near-infrared lights, and proposes a consistency and label constrained transfer low-rank representation (CLTLRR) method for dealing with cross-light finger vein recognition. In the proposed method, we first transfer cross-light finger vein images into a common feature space to narrow the gap between training images and testing images, and achieve the low-rank linear representations of images. Then, we develop a consistency constraint between the low-rank coefficients in the common feature space and the sparse coefficients in the original feature space to enhance the discrimination of linear representation. In addition, we design a class label constraint for the projection matrix to guide image transfer. Finally, the low-rank coefficients and the projected features in the common feature space are integrated for recognition. Experiments are performed on single-light and cross-light finger palmar vein databases and finger dorsal vein databases, and the experimental results prove the effectiveness of our CLTLRR.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"161 ","pages":"Article 111208"},"PeriodicalIF":7.5,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759371","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 game-inspired algorithm for marginal and global clustering 边际和全局聚类的博弈启发算法

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-22 DOI: 10.1016/j.patcog.2024.111158

Miguel de Carvalho , Gabriel Martos , Andrej Svetlošák

引用次数: 0

Reviving undersampling for long-tailed learning

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-22 DOI: 10.1016/j.patcog.2024.111200

Hao Yu, Yingxiao Du , Jianxin Wu

{"title":"Reviving undersampling for long-tailed learning","authors":"Hao Yu, Yingxiao Du , Jianxin Wu","doi":"10.1016/j.patcog.2024.111200","DOIUrl":"10.1016/j.patcog.2024.111200","url":null,"abstract":"<div><div>The training datasets used in long-tailed recognition are extremely unbalanced, resulting in significant variation in per-class accuracy across categories. Prior works mostly used average accuracy to evaluate their algorithms, which easily ignores those worst-performing categories. In this paper, we aim to enhance the accuracy of the worst-performing categories and utilize the harmonic mean and geometric mean to assess the model’s performance. We revive the balanced undersampling idea to achieve this goal. In few-shot learning, balanced subsets are few-shot and will surely under-fit, hence it is not used in modern long-tailed learning. But, we find that it produces a more equitable distribution of accuracy across categories with much higher harmonic and geometric mean accuracy, but with lower average accuracy. Moreover, we devise a straightforward model ensemble strategy, which does not result in any additional overhead and achieves improved harmonic and geometric mean while keeping the average accuracy almost intact when compared to state-of-the-art long-tailed learning methods. We validate the effectiveness of our approach on widely utilized benchmark datasets for long-tailed learning. Our code is at <span><span>https://github.com/yuhao318/BTM/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"161 ","pages":"Article 111200"},"PeriodicalIF":7.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759373","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

Frequency domain-based latent diffusion model for underwater image enhancement 基于频域的潜在扩散模型用于水下图像增强

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-22 DOI: 10.1016/j.patcog.2024.111198

Jingyu Song , Haiyong Xu , Gangyi Jiang , Mei Yu , Yeyao Chen , Ting Luo , Yang Song

引用次数: 0

Dynamic VAEs via semantic-aligned matching for continual zero-shot learning 通过语义对齐匹配实现动态 VAE，实现持续的零镜头学习

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-21 DOI: 10.1016/j.patcog.2024.111199

Junbo Yang , Borui Hu , Hanyu Li , Yang Liu , Xinbo Gao , Jungong Han , Fanglin Chen , Xuangou Wu

引用次数: 0

Multi-branch feature transformation cross-domain few-shot learning for hyperspectral image classification 用于高光谱图像分类的多分支特征变换跨域少镜头学习

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-20 DOI: 10.1016/j.patcog.2024.111197

Meilin Shi , Jiansi Ren

{"title":"Multi-branch feature transformation cross-domain few-shot learning for hyperspectral image classification","authors":"Meilin Shi , Jiansi Ren","doi":"10.1016/j.patcog.2024.111197","DOIUrl":"10.1016/j.patcog.2024.111197","url":null,"abstract":"<div><div>In the field of hyperspectral image (HSI) classification, a source dataset with ample labeled samples is commonly utilized to enhance the classification performance of a target dataset with few labeled samples. Existing few-shot learning (FSL) methods typically assume identical feature distribution in the source and target domains. However, since the classes of samples collected from different regions may vary considerably, it leads to a disparity in the feature distribution. To address the domain distribution shift between the source and target domains, a cross-domain FSL method based on multi-branch feature transformation (MBFT-CFSL) is proposed for HSI classification. First, the spectral–spatial features of the image are extracted by the multi-branch feature fusion module, and the feature diversity is increased using the featurewise transformation layers to boost the generalization performance of the model. Then, the conditional adversarial domain adaptation technique is employed for model training to lessen the impact of domain shift. Finally, the model is optimized by minimizing the maximum mean difference loss function to further diminish the distribution difference between the source and target domains. Experimental results on three distinct hyperspectral datasets validate the effectiveness of MBFT-CFSL, with the overall classification accuracy improved by 1.73%–5.45% compared to the suboptimal method. The source code is available at <span><span>https://github.com/Ziyin2/MBFT-CFSL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"160 ","pages":"Article 111197"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723042","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

Recursive reservoir concatenation for salt-and-pepper denoising 用于椒盐去噪的递归水库串联

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-20 DOI: 10.1016/j.patcog.2024.111196

In-mo Lee , Yoojeung Kim , Taehoon Kim , Hayoung Choi , Seung Yeop Yang , Yunho Kim

引用次数: 0

Distilling heterogeneous knowledge with aligned biological entities for histological image classification 利用对齐的生物实体提炼异质知识，实现组织学图像分类

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-20 DOI: 10.1016/j.patcog.2024.111173

Kang Wang , Feiyang Zheng , Dayan Guan , Jia Liu , Jing Qin

{"title":"Distilling heterogeneous knowledge with aligned biological entities for histological image classification","authors":"Kang Wang , Feiyang Zheng , Dayan Guan , Jia Liu , Jing Qin","doi":"10.1016/j.patcog.2024.111173","DOIUrl":"10.1016/j.patcog.2024.111173","url":null,"abstract":"<div><div>In the task of classifying histological images, prior works widely leverage Graph neural network (GNN) to aggregate histological knowledge from multi-level biological entities (<em>e.g.,</em> cell and tissue). However, current GNN-based methods suffer from either inadequate entity representation or intolerable computation burden. To the end, we propose a heterogeneous knowledge distillation (HKD) model to capture and amalgamate the spatial-hierarchical feature of multi-level biological entities. We first design multiple message-passing GNNs with different hidden layers as the teachers for extracting adjacent regions of cells, and leverage a transformer-based GNN as the student to model the global interaction of tissues. Such multi-teacher student architecture enables our HKD to simultaneously obtain topological knowledge at different scales from heterogeneous biological entities. We further propose a biological affiliation recognition module to adaptively align the cell knowledge learned from multi-teacher models with cell-corresponding tissue in the student model, encouraging the student model to attentively amalgamate the semantics of multi-level biological entities for highly accurate classification. Extensive experiments show that our method outperforms the state-of-the-art on three public datasets of histological image classification.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"160 ","pages":"Article 111173"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722011","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

Exploring Latent Transferability of feature components

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-20 DOI: 10.1016/j.patcog.2024.111184

Zhengshan Wang , Long Chen , Juan He , Linyao Yang , Fei-Yue Wang

{"title":"Exploring Latent Transferability of feature components","authors":"Zhengshan Wang , Long Chen , Juan He , Linyao Yang , Fei-Yue Wang","doi":"10.1016/j.patcog.2024.111184","DOIUrl":"10.1016/j.patcog.2024.111184","url":null,"abstract":"<div><div>Feature disentanglement techniques have been widely employed to extract transferable (domain-invariant) features from non-transferable (domain-specific) features in Unsupervised Domain Adaptation (UDA). However, due to the complex interplay among high-dimensional features, the separated “non-transferable” features may still be partially informative. Suppressing or disregarding them, as commonly employed in previous methods, can overlook the inherent transferability. In this work, we introduce two concepts: Partially Transferable Class Features and Partially Transferable Domain Features (PTCF and PTDF), and propose a succinct feature disentanglement technique. Different with prior works, we do not seek to thoroughly peel off the non-transferable features, as it is challenging practically. Instead, we take the two-stage strategy consisting of rough feature disentanglement and dynamic adjustment. We name our model as ELT because it can systematically Explore Latent Transferability of feature components. ELT can automatically evaluate the transferability of internal feature components, dynamically giving more attention to features with high transferability and less to features with low transferability, effectively solving the problem of negative transfer. Extensive experimental results have proved its efficiency. The code and supplementary file will be available at <span><span>https://github.com/njtjmc/ELT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"160 ","pages":"Article 111184"},"PeriodicalIF":7.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743130","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