IEEE Signal Processing Letters最新文献

Hierarchical Signal Calibration and Refinement for Multimodal Sentiment Analysis 多模态情感分析的层次信号校准与细化

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-28 DOI: 10.1109/LSP.2025.3603884

Baojian Ren;Tao Cao;Zhengyang Zhang;Shuchen Bai;Na Liu

{"title":"Hierarchical Signal Calibration and Refinement for Multimodal Sentiment Analysis","authors":"Baojian Ren;Tao Cao;Zhengyang Zhang;Shuchen Bai;Na Liu","doi":"10.1109/LSP.2025.3603884","DOIUrl":"https://doi.org/10.1109/LSP.2025.3603884","url":null,"abstract":"To address the issues of noise amplification and feature incompatibility arising from modal heterogeneity in multimodal sentiment analysis, this paper proposes a hierarchical optimization framework. In the first stage, we introduce the Semantic-Guided Calibration Network (SGC-Net), which, through a Dynamic Balancing Regulator (DBR), leverages textual semantics to intelligently weight and calibrate the cross-modal interactions of audio and video, thereby suppressing noise while preserving key dynamics. In the second stage, the Synergistic Refinement Fusion Module (SRF-Module) performs a deep refinement of the fused multi-source features. This module employs a Saliency-Gated Complementor (SGC) to rigorously filter and exchange effective information across streams, ultimately achieving feature de-redundancy and strong complementarity. Extensive experiments on the CMU-MOSI and CMU-MOSEI datasets validate the effectiveness of our method, with the model achieving state-of-the-art performance on key metrics such as binary accuracy (Acc-2: 86.73% on MOSI, 86.52% on MOSEI) and seven-class accuracy (Acc-7: 48.35% on MOSI, 53.81% on MOSEI).","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"3450-3454"},"PeriodicalIF":3.9,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057460","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

BAN: A Boundary-Aware Network for Accurate Colorectal Polyp Segmentation 基于边界感知网络的结直肠息肉精确分割

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-28 DOI: 10.1109/LSP.2025.3603828

Nengxiang Zhang;Baojiang Zhong;Minghao Piao;Kai-Kuang Ma

引用次数: 0

Unsupervised Domain Adaptation With Anatomical-Aware Self-Training for Optic Disc Segmentation in Abnormal Fundus Images 基于解剖意识自训练的无监督域自适应眼底异常图像视盘分割

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-25 DOI: 10.1109/LSP.2025.3602653

Xiao Wei;Bo Jiang;Yuye Ling;Peiyao Jin;Xinbing Wang

{"title":"Unsupervised Domain Adaptation With Anatomical-Aware Self-Training for Optic Disc Segmentation in Abnormal Fundus Images","authors":"Xiao Wei;Bo Jiang;Yuye Ling;Peiyao Jin;Xinbing Wang","doi":"10.1109/LSP.2025.3602653","DOIUrl":"https://doi.org/10.1109/LSP.2025.3602653","url":null,"abstract":"Optic disc (OD) segmentation in abnormal fundus images is crucial for glaucoma screening, and different screening populations may alter the types and proportions of abnormalities. Since annotating all abnormal types or re-annotating for each screening scenario is costly, an alternative is to utilize existing annotated data. However, these datasets only contain limited abnormal types, leading to a domain shift issue. Unsupervised domain adaptation alleviates this issue through adversarial learning or self-training. Yet, adversarial learning methods tend to overemphasize brightness as a discriminative feature, which fails under pathological changes, while self-training approaches remain vulnerable to noisy pseudo-labels. Existing denoising methods assume noise lies near decision boundaries, but abnormalities can produce noise far from them. In this letter, we propose an unsupervised domain adaptation method integrating anatomical-aware self-training with adversarial learning for OD segmentation. By exploiting the OD’s convex shape and boundary consistency, we develop two pseudo-labeling strategies to suppress noise. Experiments on four fundus image datasets demonstrate the effectiveness of our method in diverse screening scenarios.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"3475-3479"},"PeriodicalIF":3.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057466","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

SRTE-Net: Spectral-Spatial Similarity Reduction and Reorganized Texture Encoding for Hyperspectral Video Tracking SRTE-Net：用于高光谱视频跟踪的光谱空间相似性降低和重组纹理编码

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-25 DOI: 10.1109/LSP.2025.3602380

Wenhao Jiang;Weixiang Zhong;Pattathal V. Arun;Pei Xiang;Dong Zhao

{"title":"SRTE-Net: Spectral-Spatial Similarity Reduction and Reorganized Texture Encoding for Hyperspectral Video Tracking","authors":"Wenhao Jiang;Weixiang Zhong;Pattathal V. Arun;Pei Xiang;Dong Zhao","doi":"10.1109/LSP.2025.3602380","DOIUrl":"https://doi.org/10.1109/LSP.2025.3602380","url":null,"abstract":"Hyperspectral video tracking poses unique challenges due to the high dimensionality of spectral data and the limited capacity to capture discriminative texture information. To address this, we propose a novel tracking framework that integrates spectral-spatial similarity reduction with reorganized texture encoding for robust hyperspectral target tracking. Specifically, we introduce a dimensionality compression strategy that converts the multi-band hyperspectral input into a representative grayscale image, preserving key spectral-spatial cues. To enhance discriminative texture modeling, a 3D Gabor filter is applied to the search region, and the extracted responses are adaptively fused based on their local variance. The resulting texture representations are selectively masked to suppress background noise and are then passed into a correlation filter module for precise target localization. Furthermore, we design a template update mechanism that mitigates model drift and cumulative errors during tracking. Extensive experiments on public hyperspectral video benchmarks demonstrate that our method achieves competitive performance against state-of-the-art hyperspectral trackers, especially in scenarios with background clutter.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"3390-3394"},"PeriodicalIF":3.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914269","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

Reinforcing Localization Credibility Through Convex Optimization 通过凸优化增强定位可信度

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-22 DOI: 10.1109/LSP.2025.3601835

Slavisa Tomic;Marko Beko;Yakubu Tsado;Bamidele Adebisi;Abiola Oladipo

引用次数: 0

Matrix-Based Signal Reconstruction and Narrowband Interference Suppression in DSSS Systems DSSS系统中基于矩阵的信号重构与窄带干扰抑制

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-22 DOI: 10.1109/LSP.2025.3601850

Yuan Liu;Zhenguo Liu;Xuanang Mao;Chuxin Tang

引用次数: 0

Spectrally Sharpened Two Channel Graph Filter Bank and Its Polyphase Structure 谱锐化双通道图滤波器组及其多相结构

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601560

David B. Tay

引用次数: 0

Correntropy-Induced Hypergraph Spectral Clustering With Discrete Optimization 具有离散优化的相关熵诱导超图谱聚类

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601523

Jiaqi Nie;Ben Yang;Zhiyuan Xue;Xuetao Zhang;Fei Wang

{"title":"Correntropy-Induced Hypergraph Spectral Clustering With Discrete Optimization","authors":"Jiaqi Nie;Ben Yang;Zhiyuan Xue;Xuetao Zhang;Fei Wang","doi":"10.1109/LSP.2025.3601523","DOIUrl":"https://doi.org/10.1109/LSP.2025.3601523","url":null,"abstract":"Hypergraph clustering has garnered considerable attention in complex learning tasks due to its powerful capacity for modeling high-order relationships among samples. Nevertheless, existing methods encounter two fundamental challenges: 1) The need for an additional discretization step following low-dimensional spectral embedding, which introduces a suboptimal mismatch between continuous embeddings and discrete cluster assignments, thereby impairing clustering performance; and 2) the susceptibility to diverse and complex noise are commonly present in real-world scenarios, which significantly compromises clustering robustness. To address these issues, we propose a novel correntropy-induced hypergraph spectral clustering (CIHSC) model. Different from current spectral clustering methods, CIHSC integrates a correntropy-based framework to enable direct discrete spectral decomposition on hypergraphs, eliminating the need for post discretization and thereby enhancing clustering fidelity and robustness. To effectively address the non-convex optimization arising from the correntropy-induced objective, we develop a half-quadratic optimization strategy tailored to the CIHSC model. Extensive experiments conducted on both real-world and noise-contaminated datasets demonstrate that CIHSC consistently outperforms state-of-the-art clustering methods in terms of performance and robustness.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"3280-3284"},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909103","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

Prompt-Based Cross-Modal Feature Alignment for Weakly Supervised IFER 基于提示的弱监督IFER跨模态特征对齐

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601513

Hanqin Shi;Xiaofeng Kang;Jiaxiang Wang;Aihua Zheng;Wenjuan Cheng

引用次数: 0

Hybrid Cooperative Relative Localization for Urban Vehicles Based on Vehicle-to-Vehicle Communication 基于车对车通信的城市车辆混合协同相对定位

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601515

Qijie Li;Zhi Xiong;Chenfa Shi;Tianxv Wu;Jun Xiong

引用次数: 0