Pattern Recognition最新文献_第3页

SAR target augmentation and recognition via cross-domain reconstruction 通过跨域重建进行合成孔径雷达目标增强和识别

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-04 DOI: 10.1016/j.patcog.2024.111117

Ganggang Dong, Yafei Song

{"title":"SAR target augmentation and recognition via cross-domain reconstruction","authors":"Ganggang Dong, Yafei Song","doi":"10.1016/j.patcog.2024.111117","DOIUrl":"10.1016/j.patcog.2024.111117","url":null,"abstract":"<div><div>The deep learning-based target recognition methods have achieved great performance in the preceding works. Large amounts of training data with label were collected to train a deep architecture, by which the inference can be obtained. For radar sensors, the data could be collected easily, yet the prior knowledge on label was difficult to be accessed. To solve the problem, a cross-domain re-imaging target augmentation method was proposed in this paper. The original image was first recast into the frequency domain. The frequency were then randomly filtered by a randomly generated mask. The size and the shape of mask was randomly determined. The filtering results were finally used for re-imaging. The original target can be then reconstructed accordingly. A series of new samples can be generated freely. The amounts and the diversity of dataset can be therefore improved. The proposed augmentation method can be implemented on-line or off-line, making it adaptable to various downstream tasks. Multiple comparative studies throw the light on the superiority of proposed method over the standard and recent techniques. It served to generate the images that would aid the downstream tasks.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"159 ","pages":"Article 111117"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652233","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

Joint Intra-view and Inter-view Enhanced Tensor Low-rank Induced Affinity Graph Learning 联合视图内和视图间增强型张量低秩诱导亲和图学习

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-04 DOI: 10.1016/j.patcog.2024.111140

Weijun Sun, Chaoye Li, Qiaoyun Li, Xiaozhao Fang, Jiakai He, Lei Liu

引用次数: 0

PIM-Net: Progressive Inconsistency Mining Network for image manipulation localization PIM-Net：用于图像处理定位的渐进式不一致挖掘网络

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-03 DOI: 10.1016/j.patcog.2024.111136

Ningning Bai , Xiaofeng Wang , Ruidong Han , Jianpeng Hou , Yihang Wang , Shanmin Pang

{"title":"PIM-Net: Progressive Inconsistency Mining Network for image manipulation localization","authors":"Ningning Bai , Xiaofeng Wang , Ruidong Han , Jianpeng Hou , Yihang Wang , Shanmin Pang","doi":"10.1016/j.patcog.2024.111136","DOIUrl":"10.1016/j.patcog.2024.111136","url":null,"abstract":"<div><div>The content authenticity and reliability of digital images have promoted the research on image manipulation localization (IML). Most current deep learning-based methods focus on extracting global or local tampering features for identifying forged regions. These features usually contain semantic information and lead to inaccurate detection results for non-object or incomplete semantic tampered regions. In this study, we propose a novel Progressive Inconsistency Mining Network (PIM-Net) for effective IML. Specifically, PIM-Net consists of two core modules, the Inconsistency Mining Module (ICMM) and the Progressive Fusion Refinement module (PFR). ICMM models the inconsistency between authentic and forged regions at two levels, i.e., pixel correlation inconsistency and region attribute incongruity, while avoiding the interference of semantic information. Then PFR progressively aggregates and refines extracted inconsistent features, which in turn yields finer and pure localization responses. Extensive qualitative and quantitative experiments on five benchmarks demonstrate PIM-Net’s superiority to current state-of-the-art IML methods. Code: <span><span>https://github.com/ningnbai/PIM-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"159 ","pages":"Article 111136"},"PeriodicalIF":7.5,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652135","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

Cross-modal adapter for vision–language retrieval 视觉语言检索的跨模态适配器

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-03 DOI: 10.1016/j.patcog.2024.111144

Haojun Jiang , Jianke Zhang , Rui Huang , Chunjiang Ge , Zanlin Ni , Shiji Song , Gao Huang

{"title":"Cross-modal adapter for vision–language retrieval","authors":"Haojun Jiang , Jianke Zhang , Rui Huang , Chunjiang Ge , Zanlin Ni , Shiji Song , Gao Huang","doi":"10.1016/j.patcog.2024.111144","DOIUrl":"10.1016/j.patcog.2024.111144","url":null,"abstract":"<div><div>Vision–language retrieval is an important multi-modal learning topic, where the goal is to retrieve the most relevant visual candidate for a given text query. Recently, pre-trained models, <em>e.g.</em>, CLIP, show great potential on retrieval tasks. However, as pre-trained models are scaling up, fully fine-tuning them on donwstream retrieval datasets has a high risk of overfitting. Moreover, in practice, it would be costly to train and store a large model for each task. To overcome the above issues, we present a novel <strong>Cross-Modal Adapter</strong> for parameter-efficient transfer learning. Inspired by adapter-based methods, we adjust the pre-trained model with a few parameterization layers. However, there are two notable differences. First, our method is designed for the multi-modal domain. Secondly, it allows encoder-level implicit cross-modal interactions between vision and language encoders. Although surprisingly simple, our approach has three notable benefits: (1) reduces the vast majority of fine-tuned parameters, (2) saves training time, and (3) allows all the pre-trained parameters to be fixed, enabling the pre-trained model to be shared across datasets. Extensive experiments demonstrate that, without bells and whistles, our approach outperforms adapter-based methods on image–text retrieval datasets (MSCOCO, Flickr30K) and video–text retrieval datasets (MSR-VTT, DiDeMo, and ActivityNet).</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"159 ","pages":"Article 111144"},"PeriodicalIF":7.5,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652238","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

Image shadow removal via multi-scale deep Retinex decomposition 通过多尺度深度 Retinex 分解去除图像阴影

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-02 DOI: 10.1016/j.patcog.2024.111126

Yan Huang , Xinchang Lu , Yuhui Quan , Yong Xu , Hui Ji

{"title":"Image shadow removal via multi-scale deep Retinex decomposition","authors":"Yan Huang , Xinchang Lu , Yuhui Quan , Yong Xu , Hui Ji","doi":"10.1016/j.patcog.2024.111126","DOIUrl":"10.1016/j.patcog.2024.111126","url":null,"abstract":"<div><div>In recent years, deep learning has emerged as an important tool for image shadow removal. However, existing methods often prioritize shadow detection and, in doing so, they oversimplify the lighting conditions of shadow regions. Furthermore, these methods neglect cues from the overall image lighting when re-lighting shadow areas, thereby failing to ensure global lighting consistency. To address these challenges in images captured under complex lighting conditions, this paper introduces a multi-scale network built on a Retinex decomposition model. The proposed approach effectively senses shadows with uneven lighting and re-light them, achieving greater consistency along shadow boundaries. Furthermore, for the design of network, we introduce several techniques for boosting shadow removal performance, including a shadow-aware channel attention module, local discriminative and Retinex decomposition loss functions, and a multi-scale mechanism for guiding Retinex decomposition by concurrently capturing both fine-grained details and large-scale contextual information. Experimental results demonstrate the superiority of our proposed method over existing solutions, particularly for images taken under complex lighting conditions.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"159 ","pages":"Article 111126"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593960","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

S2Match: Self-paced sampling for data-limited semi-supervised learning S2Match：用于数据有限的半监督学习的自定步调采样

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-02 DOI: 10.1016/j.patcog.2024.111121

Dayan Guan , Yun Xing , Jiaxing Huang , Aoran Xiao , Abdulmotaleb El Saddik , Shijian Lu

引用次数: 0

Consistency-driven feature scoring and regularization network for visible–infrared person re-identification 用于可见红外人员再识别的一致性驱动特征评分和正则化网络

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-02 DOI: 10.1016/j.patcog.2024.111131

Xueting Chen , Yan Yan , Jing-Hao Xue , Nannan Wang , Hanzi Wang

{"title":"Consistency-driven feature scoring and regularization network for visible–infrared person re-identification","authors":"Xueting Chen , Yan Yan , Jing-Hao Xue , Nannan Wang , Hanzi Wang","doi":"10.1016/j.patcog.2024.111131","DOIUrl":"10.1016/j.patcog.2024.111131","url":null,"abstract":"<div><div>Recently, visible–infrared person re-identification (VI-ReID) has received considerable attention due to its practical importance. A number of methods extract multiple local features to enrich the diversity of feature representations. However, some local features often involve modality-relevant information, leading to deteriorated performance. Moreover, existing methods optimize the models by only considering the samples at each batch while ignoring the learned features at previous iterations. As a result, the features of the same person images drastically change at different training epochs, hindering the training stability. To alleviate the above issues, we propose a novel consistency-driven feature scoring and regularization network (CFSR-Net), which consists of a backbone network, a local feature learning block, a feature scoring block, and a global–local feature fusion block, for VI-ReID. On the one hand, we design a cross-modality consistency loss to highlight modality-irrelevant local features and suppress modality-relevant local features for each modality, facilitating the generation of a reliable compact local feature. On the other hand, we develop a feature consistency regularization strategy (including a momentum class contrastive loss and a momentum distillation loss) to impose consistency regularization on the learning of different levels of features by considering the learned features at historical epochs. This effectively enables smooth feature changes and thus improves the training stability. Extensive experiments on public VI-ReID datasets clearly show the effectiveness of our method against several state-of-the-art VI-ReID methods. Code will be released at <span><span>https://github.com/cxtjl/CFSR-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"159 ","pages":"Article 111131"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594023","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

ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample selection for robust learning with noisy labels ANNE：基于自适应近邻和特征向量的样本选择，实现带噪声标签的稳健学习

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-02 DOI: 10.1016/j.patcog.2024.111132

Filipe R. Cordeiro , Gustavo Carneiro

{"title":"ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample selection for robust learning with noisy labels","authors":"Filipe R. Cordeiro , Gustavo Carneiro","doi":"10.1016/j.patcog.2024.111132","DOIUrl":"10.1016/j.patcog.2024.111132","url":null,"abstract":"<div><div>An important stage of most state-of-the-art (SOTA) noisy-label learning methods consists of a sample selection procedure that classifies samples from the noisy-label training set into noisy-label or clean-label subsets. The process of sample selection typically consists of one of the two approaches: loss-based sampling, where high-loss samples are considered to have noisy labels, or feature-based sampling, where samples from the same class tend to cluster together in the feature space and noisy-label samples are identified as anomalies within those clusters. Empirically, loss-based sampling is robust to a wide range of noise rates, while feature-based sampling tends to work effectively in particular scenarios, e.g., the filtering of noisy instances via their eigenvectors (FINE) sampling exhibits greater robustness in scenarios with low noise rates, and the K nearest neighbour (KNN) sampling mitigates better high noise-rate problems. This paper introduces the Adaptive Nearest Neighbours and Eigenvector-based (ANNE) sample selection methodology, a novel approach that integrates loss-based sampling with the feature-based sampling methods FINE and Adaptive KNN to optimize performance across a wide range of noise rate scenarios. ANNE achieves this integration by first partitioning the training set into high-loss and low-loss sub-groups using loss-based sampling. Subsequently, within the low-loss subset, sample selection is performed using FINE, while the high-loss subset employs Adaptive KNN for effective sample selection. We integrate ANNE into the noisy-label learning state of the art (SOTA) method SSR+, and test it on CIFAR-10/-100 (with symmetric, asymmetric and instance-dependent noise), Webvision and ANIMAL-10, where our method shows better accuracy than the SOTA in most experiments, with a competitive training time. The code is available at <span><span>https://github.com/filipe-research/anne</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"159 ","pages":"Article 111132"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593964","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

Self-supervised random mask attention GAN in tackling pose-invariant face recognition 解决姿态不变人脸识别问题的自监督随机掩码注意力 GAN

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-02 DOI: 10.1016/j.patcog.2024.111112

Jiashu Liao , Tanaya Guha , Victor Sanchez

{"title":"Self-supervised random mask attention GAN in tackling pose-invariant face recognition","authors":"Jiashu Liao , Tanaya Guha , Victor Sanchez","doi":"10.1016/j.patcog.2024.111112","DOIUrl":"10.1016/j.patcog.2024.111112","url":null,"abstract":"<div><div>Pose Invariant Face Recognition (PIFR) has significantly advanced with Generative Adversarial Networks (GANs), which rotate face images acquired at any angle to a frontal view for enhanced recognition. However, such frontalization methods typically need ground-truth frontal-view images, often collected under strict laboratory conditions, making it challenging and costly to acquire the necessary training data. Additionally, traditional self-supervised PIFR methods rely on external rendering models for training, further complicating the overall training process. To tackle these two issues, we propose a new framework called <em>Mask Rotate</em>. Our framework introduces a novel training approach that requires no paired ground truth data for the face image frontalization task. Moreover, it eliminates the need for an external rendering model during training. Specifically, our framework simplifies the face image frontalization task by transforming it into a face image completion task. During the inference or testing stage, it employs a reliable pre-trained rendering model to obtain a frontal-view face image, which may have several regions with missing texture due to pose variations and occlusion. Our framework then uses a novel self-supervised <em>Random Mask</em> Attention Generative Adversarial Network (RMAGAN) to fill in these missing regions by considering them as randomly masked regions. Furthermore, our proposed <em>Mask Rotate</em> framework uses a reliable post-processing model designed to improve the visual quality of the face images after frontalization. In comprehensive experiments, the <em>Mask Rotate</em> framework eliminates the requirement for complex computations during training and achieves strong results, both qualitative and quantitative, compared to the state-of-the-art.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"159 ","pages":"Article 111112"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

引用次数: 0

Spcolor: Semantic prior guided exemplar-based image colorization Spcolor：基于先验语义引导的示例图像着色

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2024-11-02 DOI: 10.1016/j.patcog.2024.111109

Siqi Chen , Xianlin Zhang , Mingdao Wang , Xueming Li , Yu Zhang , Yue Zhang

{"title":"Spcolor: Semantic prior guided exemplar-based image colorization","authors":"Siqi Chen , Xianlin Zhang , Mingdao Wang , Xueming Li , Yu Zhang , Yue Zhang","doi":"10.1016/j.patcog.2024.111109","DOIUrl":"10.1016/j.patcog.2024.111109","url":null,"abstract":"<div><div>Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods directly search for correspondence over the entire reference image, and this type of global matching is prone to mismatch. Intuitively, a reasonable correspondence should be established between objects which are semantically similar. Motivated by this, we introduce the idea of semantic prior and propose SPColor, a semantic prior guided exemplar-based image colorization framework. Several novel components are systematically designed in SPColor, including a semantic prior guided correspondence network (SPC), a category reduction algorithm (CRA), and a similarity masked perceptual loss (SMP loss). Different from previous methods, SPColor establishes the correspondence between the pixels in the same semantic class locally. In this way, improper correspondence between different semantic classes is explicitly excluded, and the mismatch is obviously alleviated. In addition, SPColor supports region-level class assignments before SPC in the pipeline. With this feature, a category manipulation process (CMP) is proposed as an interactive interface to control colorization, which can also produce more varied colorization results and improve the flexibility of reference selection. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively on public dataset. Our code is available at <span><span>https://github.com/viector/spcolor</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"159 ","pages":"Article 111109"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652140","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