IEEE Signal Processing Letters最新文献_第3页

Spectral Scaling-Based Augmentation for Corruption-Robust Image Classification

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-07 DOI: 10.1109/LSP.2025.3549014

Zhuang Zhang;Lijun Zhang;Dejian Meng;Wei Tian;Jun Yan

{"title":"Spectral Scaling-Based Augmentation for Corruption-Robust Image Classification","authors":"Zhuang Zhang;Lijun Zhang;Dejian Meng;Wei Tian;Jun Yan","doi":"10.1109/LSP.2025.3549014","DOIUrl":"https://doi.org/10.1109/LSP.2025.3549014","url":null,"abstract":"Image classifiers often degrade in performance when test images differ significantly from the training distribution due to real-world image corruptions. Frequency-based augmentations can be used to address this issue, but existing methods excel against corruptions caused by noise and blur while struggling with those caused by contrast and fog. To tackle these challenges, we propose a novel image augmentation method grounded in a new perspective of relative spectral differences. This perspective characterizes spectral variations introduced by common corruptions as changes in non-zero frequencies, providing a unified understanding of their effects on image spectra. Building on this insight, the proposed method incorporates two key modules: a random spectral scaling module that captures statistical properties of image spectra and a deep spectral scaling module that adaptively learns spectral adjustments through a neural network. Experiments demonstrate that the proposed method improves overall robustness across various corruptions, with notable gains of 6.3% and 6.4% on contrast and fog, respectively, where existing methods often fall short.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1206-1210"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ProSegDiff: Prostate Segmentation Diffusion Network Based on Adaptive Adjustment of Injection Features

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-06 DOI: 10.1109/LSP.2025.3548422

Jialong Zhong;Tingwei Liu;Yongri Piao;Weibing Sun;Huchuan Lu

引用次数: 0

Enhanced Batch Adaptive Filter Based on Fractional-Order Generalized Cauchy Kernel Loss

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-05 DOI: 10.1109/LSP.2025.3548432

Mingjing Cui;Yunxiang Jiang;Dongyuan Lin;Shiyuan Wang;Fuliang He

引用次数: 0

Multi-Domain Time-Frequency Fusion Feature Contrastive Learning for Machinery Fault Diagnosis

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-05 DOI: 10.1109/LSP.2025.3548466

Yang Wei;Kai Wang

{"title":"Multi-Domain Time-Frequency Fusion Feature Contrastive Learning for Machinery Fault Diagnosis","authors":"Yang Wei;Kai Wang","doi":"10.1109/LSP.2025.3548466","DOIUrl":"https://doi.org/10.1109/LSP.2025.3548466","url":null,"abstract":"The scarcity of a large amount of labeled data for adequately training of deep learning models, along with their restricted generalization capabilities, persistently hinders the real-world practical application of data-driven deep learning in few-shot fault diagnosis and transfer task fault diagnosis. This paper proposes a self-supervised Wide Kernel Time-Frequency Fusion (WTFF) contrastive learning method that leverages extensive unlabeled signals to extract discriminative time-frequency fusion features, thereby enhancing fault diagnosis performance even with a limited number of labeled samples. Moreover, the WTFF integrates a multi-layer time-frequency wide convolutional neural network (TFCNN) encoder with a novel local and global time-frequency contrastive loss (LGTFCL) to capture time frequency consistency by facilitating the alignment of time-domain and frequency-domain feature embeddings across the shallow and deep network layers. In the fine-tuning phase, time frequency features across various levels learned from transferred pretrained model are fused to extract signal characteristics that exhibit both time and frequency discrimination. The proposed method demonstrates superior diagnostic accuracy and robustness in experiments involving few-shot and transfer learning-based fault diagnosis.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1116-1120"},"PeriodicalIF":3.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Body Parts to Holistic Action: A Fine-Grained Teacher-Student CLIP for Action Recognition

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-05 DOI: 10.1109/LSP.2025.3548448

Yangjun Ou;Xiao Shi;Jia Chen;Ruhan He;Chi Liu

{"title":"From Body Parts to Holistic Action: A Fine-Grained Teacher-Student CLIP for Action Recognition","authors":"Yangjun Ou;Xiao Shi;Jia Chen;Ruhan He;Chi Liu","doi":"10.1109/LSP.2025.3548448","DOIUrl":"https://doi.org/10.1109/LSP.2025.3548448","url":null,"abstract":"Action recognition in dynamic video remains challenging, particularly when distinguishing between visually similar actions. While existing methods often rely on holistic representations, they overlook the fine-grained details that are significant for accurate classification. We propose a novel Fine-grained Teacher-student CLIP (FT-CLIP) that integrates body part analysis with holistic action recognition through a teacher-student architecture, bridging the gap between fine-grained action parsing and overall action understanding. The teacher model processes individual body parts alongside specialized description to generate part-specific features, which are then aggregated and distilled into the student model. Through knowledge distillation with learnable prompts, the student model effectively learns to capture subtle action distinctions while maintaining efficient inference. FT-CLIP achieves a more nuanced understanding of complex actions by progressing from detailed body part analysis to comprehensive action recognition. Experiments on Kinetics-TPS under a fully-supervised setting and on HMDB51 and UCF101 under a zero-shot setting demonstrate the effectiveness of our method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1336-1340"},"PeriodicalIF":3.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Centroid-Free K-Means With Balanced Clustering

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-04 DOI: 10.1109/LSP.2025.3547665

Bin Meng;Fangfang Li;Fan Yang;Quanxue Gao

{"title":"Centroid-Free K-Means With Balanced Clustering","authors":"Bin Meng;Fangfang Li;Fan Yang;Quanxue Gao","doi":"10.1109/LSP.2025.3547665","DOIUrl":"https://doi.org/10.1109/LSP.2025.3547665","url":null,"abstract":"Currently, a wide array of clustering algorithms have emerged, yet many approaches rely on K-means to detect clusters. However, K-means is highly sensitive to the selection of the initial cluster centers, which poses a significant obstacle to achieving optimal clustering results. Moreover, its capability to handle nonlinearly separable data is less than satisfactory. To overcome the limitations of traditional K-means, we draw inspiration from manifold learning to reformulate the K-means algorithm into a new clustering method based on manifold structures. This method not only eliminates the need to calculate centroids in traditional approaches, but also preserves the consistency between manifold structures and clustering labels. Furthermore, we introduce the <inline-formula> <tex-math>$ell _{2,1}$</tex-math></inline-formula>-norm to naturally maintain class balance during the clustering process. Additionally, we develop a versatile K-means variant framework that can accommodate various types of distance functions, thereby facilitating the efficient processing of nonlinearly separable data. The experimental results of several databases confirm the superiority of our proposed model.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1191-1195"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Millimeter-Wave MIMO Transmission for FBMC Systems With Lens Antenna Arrays

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-04 DOI: 10.1109/LSP.2025.3547268

Ying Wang;Qiang Guo;Jianhong Xiang;Yu Zhong

引用次数: 0

Outlier-Robust Multistatic Target Localization

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-04 DOI: 10.1109/LSP.2025.3547859

Piyush Varshney;Prabhu Babu;Petre Stoica

引用次数: 0

XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-04 DOI: 10.1109/LSP.2025.3547861

Yang Xiao;Rohan Kumar Das

引用次数: 0

Adaptive Feature Selection Modulation Network for Efficient Image Super-Resolution

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-04 DOI: 10.1109/LSP.2025.3547669

Chen Wu;Ling Wang;Xin Su;Zhuoran Zheng

{"title":"Adaptive Feature Selection Modulation Network for Efficient Image Super-Resolution","authors":"Chen Wu;Ling Wang;Xin Su;Zhuoran Zheng","doi":"10.1109/LSP.2025.3547669","DOIUrl":"https://doi.org/10.1109/LSP.2025.3547669","url":null,"abstract":"In the realm of image super-resolution, learning-based methods have made significant progress. However, limited computational resources still restrict their application. This prompts us to develop an efficient method for achieving effective image super-resolution. In this letter, we propose a novel adaptive feature selection modulation network (AFSMNet) tailored for efficient image super-resolution. Specifically, we design feature modulation blocks, which include the adaptive feature selection modulation (AFSM) module and the self-gating feed-forward network (SFN). The AFSM module dynamically computes the importance of each feature channel. For channels with differing levels of importance, we employ distinct processing strategies, thereby concentrating the computational resources of the network on the more critical features as much as possible. This approach facilitates the maintenance of a low computational cost without compromising performance. The SFN restricts the flow of irrelevant feature information within the network through a simple gating mechanism. In this way, our method achieves efficient and effective image super-resolution. Extensive experiment results show that the proposed method achieves a better trade-off between reconstruction performance and computational efficiency compared to the current state-of-the-art lightweight super-resolution methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1231-1235"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0