Information Fusion最新文献

{"title":"A novel knowledge distillation and hybrid explainability approach for phenology stage classification from multi-source time series","authors":"Naeem Ullah ,&nbsp;Andrés Manuel Chacón-Maldonado ,&nbsp;Francisco Martínez-Álvarez ,&nbsp;Ivanoe De Falco ,&nbsp;Giovanna Sannino","doi":"10.1016/j.inffus.2026.104158","DOIUrl":"10.1016/j.inffus.2026.104158","url":null,"abstract":"<div><div>Accurate phenological stage classification is crucial for addressing global challenges to food security posed by climate change, water scarcity, and land degradation. It enables precision agriculture by optimizing key interventions such as irrigation, fertilization, and pest control. While deep learning offers powerful tools, existing methods face four key limitations: reliance on narrow features and models, limited long-term forecasting capability, computational inefficiency, and opaque, unvalidated explanations. To overcome these limitations, this paper presents a deep learning framework for phenology classification, utilizing multi-source time series data from satellite imagery, meteorological stations, and field observations. The approach emphasizes temporal consistency, spatial adaptability, computational efficiency, and explainability. A feature engineering pipeline extracts temporal dynamics via lag features, rolling statistics, Fourier transforms and seasonal encodings. Feature selection combines incremental strategies with classical filter, wrapper, and embedded methods. Deep learning models across multiple paradigms-feedforward, recurrent, convolutional, and attention-based-are benchmarked under multi-horizon forecasting tasks. To reduce model complexity while preserving performance where possible, the framework employs knowledge distillation, transferring predictive knowledge from complex teacher models to compact and deployable student models. For model interpretability, a new Hybrid SHAP-Association Rule Explainability approach is proposed, integrating model-driven and data-driven explanations. Agreement between views is quantified using trust metrics: precision@k, coverage, and Jaccard similarity, with a retraining-based validation mechanism. Experiments on phenology data from Andalusia demonstrate high accuracy, strong generalizability, trustworthy explanations and resource-efficient phenology monitoring in agricultural systems.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104158"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995208","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}

{"title":"Lifting wavelet transform-guided network with histogram attention for liver segmentation in CT scans","authors":"Huaxiang Liu ,&nbsp;Wei Sun ,&nbsp;Youyao Fu ,&nbsp;Shiqing Zhang ,&nbsp;Jie Jin ,&nbsp;Jiangxiong Fang ,&nbsp;Binliang Wang","doi":"10.1016/j.inffus.2026.104153","DOIUrl":"10.1016/j.inffus.2026.104153","url":null,"abstract":"<div><div>Accurate liver segmentation in computed tomography (CT) scans is crucial for the diagnosis of hepatocellular carcinoma and surgical planning; however, manual delineation is laborious and prone to operator variability. Existing deep learning methods frequently sacrifice precise boundary delineation when expanding receptive fields or fail to leverage frequency-domain cues that encode global shape, while conventional attention mechanisms are less effective in processing low-contrast images. To address these challenges, we introduce LWT-Net, a novel network guided by a trainable lifting wavelet transform, incorporating a frequency-split histogram attention mechanism to enhance liver segmentation. LWT-Net incorporates a trainable lifting wavelet transform within an encoder-decoder framework to hierarchically decompose features into low-frequency components that capture global structure and high-frequency bands that preserve edge and texture details. A complementary inverse lifting stage reconstructs high-resolution features while maintaining spatial consistency. The frequency-spatial fusion module, driven by a histogram-based attention mechanism, performs histogram-guided feature reorganization across global and local bins, while employing self-attention to capture long-range dependencies and prioritize anatomically significant regions. Comprehensive evaluations on the LiTS2017, WORD, and FLARE22 datasets confirm LWT-Net’s superior performance, achieving mean Dice similarity coefficients of 95.96%, 97.15%, and 95.97%.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104153"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995209","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}

{"title":"Dual-layer prompt ensembles: Leveraging system- and user-level instructions for robust LLM-based query expansion and rank fusion","authors":"Minghan Li ,&nbsp;Ercong Nie ,&nbsp;Huiping Huang ,&nbsp;Xinxuan Lv ,&nbsp;Guodong Zhou","doi":"10.1016/j.inffus.2026.104160","DOIUrl":"10.1016/j.inffus.2026.104160","url":null,"abstract":"<div><div>Large Language Models (LLMs) show strong potential for query expansion (QE), but their effectiveness is highly sensitive to prompt design. This paper investigates whether exploiting the system-user prompt distinction in chat-based LLMs improves QE, and how multiple expansions should be combined. We propose Dual-Layer Prompt Ensembles, which pair a behavioural system prompt with varied user prompts to generate diverse expansions, and aggregate their BM25-ranked lists using lightweight SU-RankFusion schemes. Experiments on six heterogeneous datasets show that dual-layer prompting consistently outperforms strong single-prompt baselines. For example, on Touche-2020 a dual-layer configuration improves nDCG@10 from 0.4177 (QE-CoT) to 0.4696, and SU-RankFusion further raises it to 0.4797. On Robust04 and DBPedia, SU-RankFusion improves nDCG@10 over BM25 by 24.7% and 25.5%, respectively, with similar gains on NFCorpus, FiQA, and TREC-COVID. These results demonstrate that system-user prompt ensembles are effective for QE, and that simple fusion transforms prompt-level diversity into stable retrieval improvements.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104160"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995213","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}

{"title":"MSTFDN: An EEG-fNIRS multimodal spatial-temporal fusion decoding network for personalized multi-task scenarios","authors":"Peng Ding ,&nbsp;Liyong Yin ,&nbsp;Zhengxuan Zhou ,&nbsp;Yuwei Su ,&nbsp;Minqian Zhang ,&nbsp;Yingwei Li ,&nbsp;Xiaoli Li","doi":"10.1016/j.inffus.2026.104187","DOIUrl":"10.1016/j.inffus.2026.104187","url":null,"abstract":"<div><div>Multimodal information enables Brain-Computer Interface (BCI) systems to adapt to the differences in individual neural characteristics, overcoming the limitations of each modality. As a result, multimodal fusion technology that integrates non-invasive brain imaging techniques such as electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) has gained widespread attention. However, in the field of hybrid BCI, challenges remain in effectively integrating the heterogeneous information from these two modalities and improving the decoding accuracy and generalization across various task conditions. The core issue lies in the underutilization of each modality’s signal characteristics and the incomplete capture of the potential homogeneity of higher-order hybrid features. Therefore, we propose a novel EEG-fNIRS multimodal spatial-temporal fusion decoding network (MSTFDN). This network combines multi-scale temporal convolution of time series differences and spatial multi-head self-attention mechanism. MSTFDN consists of three core components, including the EEG branch, the fNIRS branch, the EEG-fNIRS fusion branch. A multi-dimensional loss function is constructed based on independent and hybrid space multi-head expression diversity, aiming to achieve high-precision decoding in small sample datasets under multi-task and multiple personalized experimental protocols. In experiments with four motor imagery (MI) and mental workload (MWL) tasks of two public datasets under three personalized experimental protocols, MSTFDN demonstrated state-of-the-art performance. The more comprehensive experimental protocols may establish a benchmark for model performance evaluation for future research in this field. Meanwhile, MSTFDN is also expected to become a new benchmark method for EEG-fNIRS hybrid BCI research.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104187"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033284","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}

{"title":"Speech emotion recognition: A systematic mega-review of techniques and pipelines","authors":"Adil Chakhtouna,&nbsp;Sara Sekkate,&nbsp;Abdellah Adib","doi":"10.1016/j.inffus.2026.104161","DOIUrl":"10.1016/j.inffus.2026.104161","url":null,"abstract":"<div><div>Speech Emotion Recognition (SER) is a rapidly evolving research field that aims to enable machines to automatically identify human emotions from vocal signals. This systematic review presents a comprehensive and structured synthesis of the SER literature, focusing on eleven key research questions that span the theoretical foundations, signal processing pipeline, and methodological advancements in the field. Unlike prior surveys, this review unifies both foundational and state-of-the-art insights across all stages of the SER pipeline through a question-driven structure, offering a clear road-map for both new and experienced researchers in the SER community. We first explore the psychological and computational modeling of emotions, followed by a detailed examination of the different modalities for emotion expression, with a particular emphasis on speech. The review highlights the most widely used emotional speech databases, common pre-processing techniques, and the diverse set of handcrafted and learned features employed in SER. We compare traditional machine learning approaches with recent deep learning models, emphasizing their respective strengths, limitations, and application contexts. Special attention is given to the recent shift toward self-supervised learning (SSL) models such as Wav2Vec2 and HuBERT, which have redefined the state-of-the-art in speech-based representation learning. Special attention is given to evaluation metrics, benchmarking strategies, and real-world deployment challenges, including issues of speaker-independence and environmental variability. The review concludes by identifying key limitations across the literature and articulating future research directions necessary for developing reliable, scalable, and context-aware emotion-aware systems. Overall, this work serves as a central reference for advancing SER research and practical deployment in real-world environments.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104161"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995202","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}

{"title":"Fusion of quantum computing with smart agriculture: A systematic review of methods, implementation, applications, and challenges","authors":"Sumit Kumar ,&nbsp;Shashank Sheshar Singh ,&nbsp;Gourav Bathla ,&nbsp;Swati Sharma ,&nbsp;Manisha Panjeta","doi":"10.1016/j.inffus.2026.104159","DOIUrl":"10.1016/j.inffus.2026.104159","url":null,"abstract":"<div><div>The growing global population and the severity of environmental issues are driving the agriculture sector to adopt innovative technological advances for sustainable food production. Classical computing approaches frequently struggle with the volume and complexity of agricultural data when performing tasks such as crop yield prediction, disease detection, soil analysis, and weather forecasting. This Systematic Literature Review (SLR) provides an in-depth analysis of the evolving significance of quantum computing in smart agriculture. Quantum algorithms have the potential to reduce computational complexity and create novel data representation methods for high-dimensional challenges by leveraging quantum mechanics principles such as superposition and entanglement. This paper employs a structured research methodology based on eight specific research questions to comprehensively investigate over 100 peer-reviewed studies on quantum computing and smart agriculture published between 2012 and 2025. It demonstrates the effectiveness of Quantum Machine Learning (QML), quantum optimization, and hybrid quantum-classical models in various agricultural applications. The survey examines real-world implementations and compares existing quantum initiatives to classical benchmarks for the classification and prediction tasks. The presented work identifies challenges and limitations of current quantum approaches. The paper outlines directions for future work, including the accessibility of quantum hardware and the development of domain-specific algorithms. To the best of our knowledge, this is the first research question-driven SLR that provides an in-depth analysis of how quantum computing can be applied in agricultural applications.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104159"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995251","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}

{"title":"Controlled subspace fusion for language model continual learning","authors":"Xingcan Bao ,&nbsp;Jianzhou Feng ,&nbsp;Yiru Huo ,&nbsp;Huaxiao Qiu ,&nbsp;Haoran Yu ,&nbsp;Shenyuan Ren ,&nbsp;Jiadong Ren","doi":"10.1016/j.inffus.2026.104184","DOIUrl":"10.1016/j.inffus.2026.104184","url":null,"abstract":"<div><div>Large language models (LLMs) have demonstrated remarkable performance across diverse natural language processing tasks. However, they still face significant challenges in multi-task continual learning, particularly in dynamic environments where tasks evolve sequentially and resources are constrained. Existing approaches typically learn separate adapter modules for each task, leading to a linear increase in parameters as tasks accumulate and thus hindering scalability and deployment efficiency. In this paper, we propose Controlled Subspace Fusion (CSF), a rehearsal-free and task-agnostic continual learning framework for language models that integrates knowledge across tasks while preventing parameter explosion. CSF introduces a shared low-rank projection subspace to provide a unified representational foundation, thereby enhancing consistency and facilitating cross-task knowledge transfer. In addition, we design an incremental subspace fusion mechanism that adaptively merges new task adapters with previously fused representations, while suppressing redundant parameter growth. As a result, the framework achieves scalable and robust knowledge fusion across sequential tasks. We evaluate CSF on mainstream architectures, including LLaMA and T5, across model scales ranging from 220M to 13B parameters. Experimental results on continual learning benchmarks demonstrate that CSF not only achieves superior average accuracy and parameter efficiency compared to existing approaches, but also provides a scalable and deployment-friendly solution that supports efficient knowledge fusion.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104184"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047984","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}

{"title":"SU-RMT: Toward bridging semantic representation and structural detail modeling for medical image segmentation","authors":"Peibo Song ,&nbsp;Zihao Wang ,&nbsp;Jinshuo Zhang ,&nbsp;Shujun Fu ,&nbsp;Yunfeng Zhang ,&nbsp;Wei Wu ,&nbsp;Fangxun Bao","doi":"10.1016/j.inffus.2026.104182","DOIUrl":"10.1016/j.inffus.2026.104182","url":null,"abstract":"<div><div>Accurate medical image segmentation requires models that capture high-level semantics while preserving fine-grained structural details, due to anatomical heterogeneity and subtle textures in clinical scenarios. However, existing U-shaped networks usually lack a unified perspective to reconcile semantic representation with structural detail. To this end, we present <strong>SU-RMT</strong>, a U-shaped network that embodies this unified perspective by redesigning the encoder, bottleneck, and skip connection. The encoder employs the <strong>Dy</strong>namic <strong>S</strong>patial <strong>A</strong>ttention <strong>(DySA)</strong> mechanism to capture global context with spatial priors. The bottleneck introduces a <strong>H</strong>ybrid <strong>S</strong>pectral <strong>A</strong>daptive <strong>(HSA)</strong> module to transform abstract semantics into structure-aware features. The first skip connection incorporates a <strong>F</strong>requency-<strong>F</strong>used <strong>(F</strong>²<strong>)</strong> block to enhance boundary details without amplifying noise. Across several medical image segmentation tasks, SU-RMT demonstrates strong performance. The code is at the <span><span>link</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104182"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048053","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}

{"title":"Unsupervised multimodal graph completion networks with multi-level contrastiveness for modality-missing conversation understanding","authors":"Sichao Fu ,&nbsp;Songren Peng ,&nbsp;Bin Zou ,&nbsp;Xiao-Yuan Jing ,&nbsp;Wei Yu ,&nbsp;Qinmu Peng ,&nbsp;Xinge You","doi":"10.1016/j.inffus.2026.104197","DOIUrl":"10.1016/j.inffus.2026.104197","url":null,"abstract":"<div><div>Multimodal conversation understanding has received increasing research interest in recent years, which aims to integrate multimodal conversation information to improve the accuracy of computer understanding of user intentions. However, the existing multimodal conversation understanding methods often suffer from a conversation modality missing challenge, which seriously damages their superior performance. Recently emerged imputation-based incomplete multimodal learning (I²ML) provides an effective solution, which aims to reconstruct the missing modality features under the supervision of a downstream task. Such reliance on labels causes both the bias of the reconstructed modality features and the limitation of their scope of application. Besides, these proposed I²ML methods independently consider the missing modality features reconstruction process between different utterances, which further leads to a specific utterance over-reliance (model sub-optimal) issue. To address the above-mentioned issues, a more general unsupervised I²ML is proposed to effectively improve the performance of the modality-missing conversation understanding (M²CU) task, termed unsupervised multimodal graph completion networks (UMGCN). Specifically, to improve the accuracy of each reconstructed modality feature, an effective missing modality recovery module is designed to enhance the information interaction process between different utterances for generating robust missing modality recovery features. Then, a multi-level graph contrastive loss on the cross-structure and cross-view level is proposed to learn utterance-general conversation representations by maximizing the mutual information between the same conversation representations across different structures and views. Finally, the learned utterance-general conversation representations can be applied to arbitrary M²CU tasks. Extensive experiments on four datasets, seven missing rates and two M²CU tasks show that our proposed UMGCN outperforms the existing incomplete multimodal learning methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104197"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071716","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}

{"title":"MRFNet: Multi-reference fusion for image deblurring","authors":"Tingrui Guo ,&nbsp;Chi Xu ,&nbsp;Kaifeng Tang ,&nbsp;Hao Qian","doi":"10.1016/j.inffus.2026.104169","DOIUrl":"10.1016/j.inffus.2026.104169","url":null,"abstract":"<div><div>Motion blur is a persistent challenge in visual data processing. While single-image deblurring methods have made significant progress, using multiple reference images from the same scene for deblurring remains an overlooked problem. Existing methods struggle to integrate information from multiple reference images with differences in lighting, color, and perspective. Herein, we propose a novel framework MRFNet which leverages any number of discontinuous reference images for deblurring. The framework consists of two key components: (1) the Offset Fusion Module (OFM) guided by dense matching, which aggregates features from discontinuous reference images through high-frequency detail enhancement and permutation-invariant units; and (2) the Deformable Enrichment Module (DEM), which refines misaligned features using deformable convolutions for precise detail recovery. Quantitative and qualitative evaluations on synthetic and real-world datasets show that the proposed method outperforms state-of-the-art deblurring approaches. Additionally, a new real-world dataset is provided to fill the gap in evaluating discontinuous reference problems.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104169"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033291","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}