IEEE Journal of Selected Topics in Signal Processing最新文献

A CNN-Transformer Hybrid Framework for Mapping Annual Wheat Fractional Cover From 2001-2023 Using MODIS Satellite Data Over Asia 利用MODIS卫星数据绘制2001-2023年亚洲小麦年复盖度的CNN-Transformer混合框架

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1109/JSTSP.2026.3660045

Wenyuan Li;Shunlin Liang;Yongzhe Chen;Han Ma;Jianglei Xu;Yichuan Ma;Zhongxin Chen;Husheng Fang;Fengjiao Zhang

{"title":"A CNN-Transformer Hybrid Framework for Mapping Annual Wheat Fractional Cover From 2001-2023 Using MODIS Satellite Data Over Asia","authors":"Wenyuan Li;Shunlin Liang;Yongzhe Chen;Han Ma;Jianglei Xu;Yichuan Ma;Zhongxin Chen;Husheng Fang;Fengjiao Zhang","doi":"10.1109/JSTSP.2026.3660045","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3660045","url":null,"abstract":"Wheat is a staple crop in over 40 countries, with Asia accounting for more than 40% of global cultivation. Long-term mapping of wheat cover is critical for agricultural management and food security. However, existing wheat mapping products face a key limitation in spatiotemporal coverage: they either offer broad spatial coverage for a single or few years, or provide long time series that are confined to specific regions. To address this gap, we propose DeepMapping, a hybrid deep learning framework designed to generate a consistent annual fractional wheat cover product at 250 m resolution from 2001 to 2023 over Asia. Our framework integrates Convolutional Neural Networks (CNNs) and Transformer models to extract complementary spatial and temporal features. It processes multi-resolution data, including 250 m and 500 m MODIS reflectance, alongside GLASS Leaf Area Index and Fractional Vegetation Cover products. The model is trained with fractions derived from the 10 m resolution WorldCereal 2021 map and refined with ancillary information such as coarse-resolution crop products, land cover data, and agricultural statistics to enhance reliability. By learning the relationship between short-term, high-resolution labels and long-term, coarse-resolution MODIS observations, DeepMapping is efficiently used to generates annual wheat fractional cover product over Asia at 250 m from 2001–2023. Validation with 2,556 samples demonstrates overall accuracy of 84.1%, producer's accuracy of 87.8%, and user's accuracy of 70.07%. Comparison with national statistical data confirms high consistency (<inline-formula><tex-math>$R^{2}$</tex-math></inline-formula>: 0.880–0.943). DeepMapping offers a scalable solution for long-term, reliable agricultural mapping.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"153-167"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Labels Generated by Large Language Models Help Measure People’s Empathy in Vitro 由大型语言模型生成的标签有助于在体外测量人们的同理心

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-03-06 DOI: 10.1109/JSTSP.2026.3671186

Md Rakibul Hasan;Yue Yao;Md Zakir Hossain;Aneesh Krishna;Imre Rudas;Shafin Rahman;Tom Gedeon

{"title":"Labels Generated by Large Language Models Help Measure People’s Empathy in Vitro","authors":"Md Rakibul Hasan;Yue Yao;Md Zakir Hossain;Aneesh Krishna;Imre Rudas;Shafin Rahman;Tom Gedeon","doi":"10.1109/JSTSP.2026.3671186","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3671186","url":null,"abstract":"Large language models (LLMs) have revolutionised many fields, with LLM-as-a-service (LLMSaaS) offering accessible, general-purpose solutions without costly task-specific training. In contrast to the widely studied prompt engineering for directly solving tasks (in vivo), this paper explores LLMs’ potential for in-vitro applications: using LLM-generated labels to improve supervised training of mainstream models. We examine two strategies – (1) noisy label correction and (2) training data augmentation – in empathy computing, an emerging task to predict psychology-based questionnaire outcomes from inputs like textual narratives. Crowdsourced datasets in this domain often suffer from noisy labels that misrepresent underlying empathy. We show that replacing or supplementing these crowdsourced labels with LLM-generated labels, developed using psychology-based scale-aware prompts, achieves statistically significant accuracy improvements. Notably, the RoBERTa pre-trained language model (PLM) trained with noise-reduced labels yields a state-of-the-art Pearson correlation coefficient of 0.648 on the public NewsEmp benchmarks. This paper further analyses evaluation metric selection and demographic biases to help guide the future development of more equitable empathy computing models.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"213-226"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Cross-Model Adjudication for Bias Mitigation in Large Language Models 大型语言模型中偏见缓解的跨模型裁决

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1109/JSTSP.2026.3662478

Xiangmei Li;Changwei Li;Wenjie Liu;Yan Long;Yifan Huang;Yi Liu

{"title":"Cross-Model Adjudication for Bias Mitigation in Large Language Models","authors":"Xiangmei Li;Changwei Li;Wenjie Liu;Yan Long;Yifan Huang;Yi Liu","doi":"10.1109/JSTSP.2026.3662478","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3662478","url":null,"abstract":"With the increasing adoption and prevalence of large language models (LLMs), concerns regarding their inherent biases have become paramount. Existing approaches often rely on fixed datasets and metrics for bias detection and subsequent fine-tuning for mitigation. However, relying solely on such fixed datasets and metrics, akin to standardized exams, may be insufficient due to their inherent inflexibility (e.g., fixed testing content) and susceptibility to gaming. Furthermore, these benchmarks risk contamination if inadvertently included in training data. Drawing an analogy to human peer review and collaborative learning, this paper introduces a novel Cross-Model Adjudication Framework (CMAF) for detecting and mitigating biases in LLMs. We implement a distributed peer-review mechanism where four state-of-the-art models (Qwen2.5-7B, DeepSeek-7B-chat, Gemma2-9B, LLaMA3.1-8B) critically evaluate each other’s responses to prompts from the HolisticBias dataset. The consensus-derived, low-bias outputs are then utilized for parameter-efficient fine-tuning. Our method achieves a bias reduction of up to 12.3%, measured by the statistical significance of token likelihood differences across demographic groups, and yields another comparable B-score metric against commercial LLMs, while preserving core task performance and maintaining minimal inference latency overhead post-fine-tuning.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"177-191"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SPGrasp: Spatiotemporal Prompt-Driven Grasp Synthesis in Dynamic Scenes SPGrasp：动态场景中时空驱动的抓取合成

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-03-06 DOI: 10.1109/JSTSP.2026.3671182

Yunpeng Mei;Hongjie Cao;Wei Xiao;Yinqiu Xia;Zhaohan Feng;Gang Wang;Jie Chen

{"title":"SPGrasp: Spatiotemporal Prompt-Driven Grasp Synthesis in Dynamic Scenes","authors":"Yunpeng Mei;Hongjie Cao;Wei Xiao;Yinqiu Xia;Zhaohan Feng;Gang Wang;Jie Chen","doi":"10.1109/JSTSP.2026.3671182","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3671182","url":null,"abstract":"Real-time interactive grasp synthesis for dynamic objects remains challenging, as existing instance-level methods struggle to achieve low-latency inference while maintaining robust temporal consistency. To bridge this gap, we propose SPGrasp (Spatiotemporal Prompt-driven dynamic Grasp synthesis), a novel framework that extends the Segment Anything Model 2 (SAM 2) for video-stream grasp estimation. Our core innovation integrates user prompts with a spatiotemporal context module, enabling real-time interaction with end-to-end latency as low as 59 ms while preserving consistent instance identity and grasp predictions in dynamic, cluttered scenes, including object overlap and occlusion. In benchmark evaluations, SPGrasp achieves instance-level grasp accuracies of 90.6% on OCID and 93.8% on Jacquard. On the GraspNet-1Billion dataset under continuous tracking, SPGrasp reaches 92.0% accuracy with 73.1 ms per-frame latency, corresponding to a 58.5% latency reduction over the prior state-of-the-art promptable method RoG-SAM while maintaining competitive accuracy. Real-world experiments further demonstrate reliable interactive grasping under frequent occlusions, achieving a 94.8% success rate. These results suggest that SPGrasp effectively mitigates the latency–interactivity trade-off in dynamic grasp synthesis.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"202-212"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

OmniMamba: Omnidirectional Scanning Meets State Space Models for Efficient Hyperspectral Image Classification OmniMamba：全向扫描满足状态空间模型的高效高光谱图像分类

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1109/JSTSP.2026.3657243

Qiyun Zheng;Taosheng Xu;Chenglong Zhang;Peng Li;Wenwen Min;Changmiao Wang

{"title":"OmniMamba: Omnidirectional Scanning Meets State Space Models for Efficient Hyperspectral Image Classification","authors":"Qiyun Zheng;Taosheng Xu;Chenglong Zhang;Peng Li;Wenwen Min;Changmiao Wang","doi":"10.1109/JSTSP.2026.3657243","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3657243","url":null,"abstract":"Accurate classification of hyperspectral images (HSI) is crucial for earth observation and agricultural production analysis, yet remains challenging due to high dimensionality, spectral variability, and limited training samples. Traditional approaches often struggle to effectively balance computational complexity with the ability to capture spatial-spectral relationships of observation targets. To address this challenge, we propose OmniMamba, a novel omnidirectional state space model that adopts a collaborative alternation strategy integrating single-scale and multi-scale feature processing with omnidirectional scanning mechanisms. Four complementary scanning patterns (row, column, zigzag, and snake) are employed in the omnidirectional scanning mechanism to transfer 2D spatial data into 1D spatially structured feature sequence, which preserves directional sensitivity while achieving global dependency modeling with only linear complexity. This avoids the quadratic complexity bottleneck inherent in self-attention mechanisms. Our collaborative alternation strategy coordinates fine-grained spectral signatures with hierarchical spatial contexts through cascaded processing stages, addressing the spectral-spatial feature fusion challenge in HSI classification. Extensive experiments conducted on four benchmark datasets validate the superiority of OmniMamba, achieving a mean overall accuracy of 99.28%, significantly outperforming existing methods. Remarkably, our model accomplishes the performance with only 246 K parameters and 0.04 GFLOPs, demonstrating dramatically low computational complexity than the state-of-the art conventional CNN and transformer-based architectures.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"142-152"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation StyleAvatar3D：利用图像-文本扩散模型高保真3D头像生成

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1109/JSTSP.2026.3662496

Chi Zhang;Yiwen Chen;Yijun Fu;Wei Cheng;Zhenglin Zhou;Wenjia Jiang;Zhibin Wang;Bin Fu;Tao Chen;Gang Yu;Guosheng Lin;Chenxi Song

{"title":"StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation","authors":"Chi Zhang;Yiwen Chen;Yijun Fu;Wei Cheng;Zhenglin Zhou;Wenjia Jiang;Zhibin Wang;Bin Fu;Tao Chen;Gang Yu;Guosheng Lin;Chenxi Song","doi":"10.1109/JSTSP.2026.3662496","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3662496","url":null,"abstract":"The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D resources presents significant challenges to learning. In this paper, we present a novel method for generating high-quality, stylized 3D avatars that utilizes pre-trained image-text diffusion models for data generation and a Generative Adversarial Network (GAN)-based 3D generation network for training. Our method leverages the comprehensive priors of appearance and geometry offered by image-text diffusion models to generate multi-view images of avatars in various styles. During data generation, we employ poses extracted from existing 3D models to guide the generation of multi-view images. To handle inaccurate pose annotations of stylized images, we investigate view-specific prompts and develop a coarse-to-fine discriminator for GAN training. Additionally, we develop a latent diffusion model within the style space of StyleGAN to enable the generation of avatars based on image or text inputs. Our approach demonstrates superior performance over current state-of-the-art methods in terms of visual quality and diversity of the produced avatars.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"192-201"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

DepthCropSeg++: Scaling a Crop Segmentation Foundation Model With Depth-Labeled Data 使用深度标记数据缩放作物分割基础模型

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1109/JSTSP.2026.3654362

Jiafei Zhang;Songliang Cao;Binghui Xu;Yanan Li;Weiwei Jia;Tingting Wu;Hao Lu;Weijuan Hu;Zhiguo Han

{"title":"DepthCropSeg++: Scaling a Crop Segmentation Foundation Model With Depth-Labeled Data","authors":"Jiafei Zhang;Songliang Cao;Binghui Xu;Yanan Li;Weiwei Jia;Tingting Wu;Hao Lu;Weijuan Hu;Zhiguo Han","doi":"10.1109/JSTSP.2026.3654362","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3654362","url":null,"abstract":"We introduce DepthCropSeg++, a foundation model for crop segmentation, capable of segmenting different crop species under open in-field environment. Crop segmentation is a fundamental task for modern agriculture, underpinning many downstream tasks such as plant phenotyping, density estimation, and weed control. In foundation-model era, a number of generic large language and vision models have been developed. These models have demonstrated remarkable real-world generalization due to significant model capacity and large-scale datasets. However, current crop segmentation models mostly learn from limited data due to expensive pixel-level labelling cost, often performing well only under specific crop types or controlled environment. In this work, we follow the vein of our previous work DepthCropSeg, an almost unsupervised approach to crop segmentation, to scale up a cross-species and cross-scene crop segmentation dataset, with 28,406 images across 30+ species and 15 environmental conditions. We build upon a state-of-the-art semantic segmentation architecture ViT-Adapter, enhance it with dynamic upsampling for improved. detail awareness, and train it with a two-stage self-training pipeline. To systematically validate model performance, we conduct comprehensive experiments to justify the effectiveness and generalization capabilities across multiple crop datasets. Results demonstrate that DepthCropSeg++ achieves 93.11% mIoU on a comprehensive testing set, outperforming both supervised baselines and general-purpose vision foundation models like Segmentation Anything Model (SAM) by significant margins (<inline-formula><tex-math>$+0.36%$</tex-math></inline-formula> and +48.57% respectively). The model particularly excels in challenging scenarios including night-time environment (86.90% mIoU), high-density canopies (99.86% mIoU), and unseen crop varieties (90.09% mIoU), indicating a new state of the art for crop segmentation.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"129-141"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A Multidimensional Tactile Feature Information Fusion Method for Food Graininess Evaluation 食品粒度评价的多维触觉特征信息融合方法

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1109/JSTSP.2026.3665106

Liwen Huang;Feng Gao;Gaofeng Li;Jun Wang;Dongdong Du

{"title":"A Multidimensional Tactile Feature Information Fusion Method for Food Graininess Evaluation","authors":"Liwen Huang;Feng Gao;Gaofeng Li;Jun Wang;Dongdong Du","doi":"10.1109/JSTSP.2026.3665106","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3665106","url":null,"abstract":"Conventional texture instruments rely on limited feature parameters, which poses challenges for the accurate quantitative evaluation of food graininess. In this study, a multidimensional tactile feature fusion method was proposed to assess food graininess, by using a self-developed tactile information acquisition system. A total of 180 sets of tactile friction and vibration data were collected based on six types of food-saliva mixture samples. Four machine learning algorithms were applied for feature extraction, and the convolutional neural network (CNN) exhibited the best performance, enabling the identification of nine key tactile friction and vibration features. These features, together with sensory evaluation scores, were utilized for correlation analysis, qualitative discrimination, and quantitative prediction. The results demonstrated that tactile vibration features exhibited a stronger correlation with sensory graininess than friction features. Principal component analysis (PCA) effectively discriminated food samples with different graininess levels, achieving an accuracy exceeding 95.8%. Furthermore, graininess was accurately predicted by a stepwise multiple linear regression (SMLR) model (<inline-formula><tex-math>$R_{p} = 0.889$</tex-math></inline-formula>, RMSEP = 0.622). Overall, these findings confirm the effectiveness of multidimensional tactile feature information fusion methods for evaluating food graininess and highlight their potential for the quantitative characterization of food texture.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"168-176"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-03-30 DOI: 10.1109/JSTSP.2026.3675759

引用次数: 0

IEEE Signal Processing Society Publication Information IEEE信号处理学会出版物信息

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-03-30 DOI: 10.1109/JSTSP.2026.3675763

引用次数: 0