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Concept-cognitive learning survey: Mining and fusing knowledge from data Inform. Fusion (IF 18.6) Pub Date : 2024-04-16 Doudou Guo, Weihua Xu, Weiping Ding, Yiyu Yao, Xizhao Wang, Witold Pedrycz, Yuhua Qian
Concept-cognitive learning (CCL), an emerging intelligence learning paradigm, has recently become a popular research subject in artificial intelligence and cognitive computing. A central notion of CCL is cognitive and learning things via concepts. In this process, concepts play a fundamental role when mining and fusing knowledge from data to wisdom. With the in-depth research and expansion of CCL in
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Triple-modality interaction for deepfake detection on zero-shot identity Inform. Fusion (IF 18.6) Pub Date : 2024-04-15 JunHo Yoon, Angel Panizo-LLedot, David Camacho, Chang Choi
Recent advancements in generative AI technology have created more realistic fake data that are utilized in various fields, such as data augmentation. However, the misuse of deepfake technology has led to increased damage. Consequently, ongoing research aims to analyze modality characteristics and detect deepfakes through AI-based methods. Existing AI-based deepfake-detection techniques have limitations
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Automatic speech recognition using advanced deep learning approaches: A survey Inform. Fusion (IF 18.6) Pub Date : 2024-04-15 Hamza Kheddar, Mustapha Hemis, Yassine Himeur
Recent advancements in deep learning (DL) have posed a significant challenge for automatic speech recognition (ASR). ASR relies on extensive training datasets, including confidential ones, and demands substantial computational and storage resources. Enabling adaptive systems improves ASR performance in dynamic environments. DL techniques assume training and testing data originate from the same domain
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Bimodal semantic fusion prototypical network for few-shot classification Inform. Fusion (IF 18.6) Pub Date : 2024-04-15 Xilang Huang, Seon Han Choi
Few-shot classification learns from a small number of image samples to recognize unseen images. Recent few-shot learning exploits auxiliary text information, such as class labels and names, to obtain more discriminative class prototypes. However, most existing approaches rarely consider using text information as a clue to highlight important feature regions and do not consider feature alignment between
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ESVFL: Efficient and secure verifiable federated learning with privacy-preserving Inform. Fusion (IF 18.6) Pub Date : 2024-04-12 Jiewang Cai, Wenting Shen, Jing Qin
Federated learning has been widely applied as a distributed machine learning method in various fields, allowing a global model to be trained by sharing local gradients instead of raw data. However, direct sharing of local gradients still carries the risk of privacy data leakage, and the malicious server might falsify aggregated result to disrupt model updates. To address these issues, a lot of privacy-preserving
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Hand-based multimodal biometric fusion: A review Inform. Fusion (IF 18.6) Pub Date : 2024-04-12 Shuyi Li, Lunke Fei, Bob Zhang, Xin Ning, Lifang Wu
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From the abundance perspective: Multi-modal scene fusion-based hyperspectral image synthesis Inform. Fusion (IF 18.6) Pub Date : 2024-04-10 Erting Pan, Yang Yu, Xiaoguang Mei, Jun Huang, Jiayi Ma
Nowadays, data is of paramount importance for artificial intelligence. However, collecting real-world hyperspectral images (HSIs) with desired characteristics and diversity can be prohibitively expensive and time-consuming, leading to the data scarcity issue in HSI, and further limiting the potential of deep learning-based HSI applications. Existing work to tackle this issue fails to generate abundant
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On-line linguistic Decision Support System based on Citizen Crowd Decision Making Inform. Fusion (IF 18.6) Pub Date : 2024-04-09 Jeronimo Durán, Antonio Francisco Roldán López de Hierro, Francisco Herrera, Rosana Montes
Scientific outreach and efforts to increase citizen participation have brought research closer to society and inspired new careers in STEM. In virtual events, it is now easier to monitor participation and get feedback through online surveys. However, in in-person events and crowded contexts, there is no technological solution to gather attendees’ opinions on a given event. This is why we are presenting
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Visual attention methods in deep learning: An in-depth survey Inform. Fusion (IF 18.6) Pub Date : 2024-04-08 Mohammed Hassanin, Saeed Anwar, Ibrahim Radwan, Fahad Shahbaz Khan, Ajmal Mian
Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated
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A general image fusion framework using multi-task semi-supervised learning Inform. Fusion (IF 18.6) Pub Date : 2024-04-08 Wu Wang, Liang-Jian Deng, Gemine Vivone
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A survey of route recommendations: Methods, applications, and opportunities Inform. Fusion (IF 18.6) Pub Date : 2024-04-07 Shiming Zhang, Zhipeng Luo, Li Yang, Fei Teng, Tianrui Li
Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens’ travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal)
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Continuous document layout analysis: Human-in-the-loop AI-based data curation, database, and evaluation in the domain of public affairs Inform. Fusion (IF 18.6) Pub Date : 2024-04-06 Alejandro Peña, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Iñigo Puente, Jorge Cordova, Gonzalo Cordova
In the digital era, the amount of digital documents generated each day have being increasing exponentially with the years, to a point where it is unfeasible to process them manually. Thus, there has been growing interest from different sectors to develop automatic tools to process digital documents in an automatic manner. Yet useful, this task is challenging, due to both the large variability and the
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Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework Inform. Fusion (IF 18.6) Pub Date : 2024-04-06 Elham Nasarian, Roohallah Alizadehsani, U.Rajendra Acharya, Kwok-Leung Tsui
Artificial intelligence (AI)-based medical devices and digital health technologies, including medical sensors, wearable health trackers, telemedicine, mobile health (mHealth), large language models (LLMs), and digital care twins (DCTs), significantly influence the process of clinical decision support systems (CDSS) in healthcare and medical applications. However, given the complexity of medical decisions
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Large-scale group hierarchical DEMATEL method with automatic consensus reaching Inform. Fusion (IF 18.6) Pub Date : 2024-04-06 Yuan-Wei Du, Xin-Lu Shen
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CasFormer: Cascaded transformers for fusion-aware computational hyperspectral imaging Inform. Fusion (IF 18.6) Pub Date : 2024-04-06 Chenyu Li, Bing Zhang, Danfeng Hong, Jun Zhou, Gemine Vivone, Shutao Li, Jocelyn Chanussot
Computational hyperspectral imaging (CHI) is a cutting-edge technique, which plays a pivotal role in breaking through the quality bottleneck of hyperspectral images (HSI). Among the techniques employed in this domain, the coded aperture snapshot spectral imaging (CASSI) system holds widespread recognition. Nevertheless, the imaging capability of CASSI remains limited due to the hardware conditions
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An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes Inform. Fusion (IF 18.6) Pub Date : 2024-04-06 Zhang-peng Tian, Fu-xin Xu, Ru-xin Nie, Xiao-kang Wang, Jian-qiang Wang
Group multiple criteria sorting (MCS) has become a trend in dealing with a variety of practical problems. During the process of managing group MCS, it is critical to reduce conflicts among decision-makers (DMs). Given the key role of DMs’ attitudes in affecting consensus level, this study aims to propose a novel consensus-based approach to solve group MCS problems considering DMs’ attitudes with flexible
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Tensor-based multi-view spectral clustering via shared latent space Inform. Fusion (IF 18.6) Pub Date : 2024-04-06 Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A.K. Suykens
Multi-view Spectral Clustering (MvSC) partitions data into clusters according to multiple views for higher performance. However, most existing works overlook model interpretability and involve iterative and alternating updates on parameters with expensive computations, where out-of-sample predictions are also commonly prohibitive. In this paper, we construct a novel weighted conjugate feature duality
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Spatiotemporal gated traffic trajectory simulation with semantic-aware graph learning Inform. Fusion (IF 18.6) Pub Date : 2024-04-06 Yu Wang, Ji Cao, Wenjie Huang, Zhihua Liu, Tongya Zheng, Mingli Song
Traffic trajectories of various vehicles, bicycles and pedestrians can help understand the traffic dynamics in a fine-grained manner like traffic flow, traffic congestion and ride-hailing demand. The comprehensive usage of traffic trajectory data has not been fully investigated due to the prevalent privacy concerns and commercial limitations. The traffic trajectory simulation task has emerged to generate
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ExplainLFS: Explaining neural architectures for similarity learning from local perturbations in the latent feature space Inform. Fusion (IF 18.6) Pub Date : 2024-04-05 Marilyn Bello, Pablo Costa, Gonzalo Nápoles, Pablo Mesejo, Óscar Cordón
Despite the increasing development in recent years of explainability techniques for deep neural networks, only some are dedicated to explaining the decisions made by neural networks for similarity learning. While existing approaches can explain classification models, their adaptation to generate visual similarity explanations is not trivial. Neural architectures devoted to this task learn an embedding
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Maximum expert consensus model with uncertain adjustment costs for social network group decision making Inform. Fusion (IF 18.6) Pub Date : 2024-04-05 Yifan Ma, Ying Ji, Deqiang Qu, Xuyuan Zhang, Lun Wang
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Large-scale aerial scene perception based on self-supervised multi-view stereo via cycled generative adversarial network Inform. Fusion (IF 18.6) Pub Date : 2024-04-04 Kevin W. Tong, Zhiyi Shi, GuangYu Zhu, Ya Duan, Yuhong Hou, Edmond Q. Wu, LiMin Zhu
Unmanned aerial vehicle (UAV) has the characteristics of strong maneuverability and wide field of vision, and its application in real-time terrain perception and target capture is one of the active frontier topics in the field of UAV cooperative situation awareness. LiDAR-based method has unique advantages, but data acquisition is difficult and costly. In comparison, multiple view stereo-based (MVS)
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A lightweight network for abdominal multi-organ segmentation based on multi-scale context fusion and dual self-attention Inform. Fusion (IF 18.6) Pub Date : 2024-04-04 Miao Liao, Hongliang Tang, Xiong Li, P. Vijayakumar, Varsha Arya, Brij B. Gupta
Segmenting the organs from abdominal CT images is a vital procedure for computer-aided diagnosis and treatment. Accurate and simultaneous segmentation of multiple abdominal organs remains challenging due to the complex structures, varying sizes, and fuzzy boundaries. Currently, most methods aiming at improving segmentation accuracy involve either deepening the network or employing large-scale models
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SK-MMFMNet: A multi-dimensional fusion network of remote sensing images and EEG signals for multi-scale marine target recognition Inform. Fusion (IF 18.6) Pub Date : 2024-04-04 Jiawen Long, Zhixiang Fang, Lubin Wang
Intelligent recognition of multi-scale marine targets remains pivotal in studying marine resources and transportation. Multi-scale marine target recognition faces challenges such as blurred image, noise interference, varied target sizes, and random target positions. However, these hardly affect the judgment of human brain which could adeptly capture multi-scale targets and disregard noise interference
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Discovering common information in multi-view data Inform. Fusion (IF 18.6) Pub Date : 2024-04-04 Qi Zhang, Mingfei Lu, Shujian Yu, Jingmin Xin, Badong Chen
We introduce an innovative and mathematically rigorous definition for computing common information from multi-view data, drawing inspiration from Gács-Körner common information in information theory. Leveraging this definition, we develop a novel supervised multi-view learning framework to capture both common and unique information. By explicitly minimizing a total correlation term, the extracted common
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Non-informative noise-enhanced stochastic neural networks for improving adversarial robustness Inform. Fusion (IF 18.6) Pub Date : 2024-04-04 Hao Yang, Min Wang, Qi Wang, Zhengfei Yu, Guangyin Jin, Chunlai Zhou, Yun Zhou
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Distributed event-triggered estimation for dynamic average consensus: A perturbation-injected privacy-preservation scheme Inform. Fusion (IF 18.6) Pub Date : 2024-04-04 Xiaojian Yi, Tao Xu
Previous studies on the distributed estimation problem for dynamic average consensus of multi-agent networks are usually based on the assumption that each agent continuously and honestly shares information with its neighbors. To relax this assumption, this paper focuses on the distributed event-triggered private estimation problem. By injecting random perturbations into the original reference signal
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Periodic Distribution Entropy: Unveiling the complexity of physiological time series through multidimensional dynamics Inform. Fusion (IF 18.6) Pub Date : 2024-04-03 Xiao Yu, Weimin Li, Bing Yang, Xiaorong Li, Jie Chen, Guohua Fu
Physiological signals, manifested as time series, reflect the internal transitions of physiological systems. Analyzing their complexity provides insights into the system’s core characteristics. However, traditional techniques based on one-dimensional time series waveforms are limited, especially in the presence of noise. We introduce the Periodic Distribution Entropy (PDEn) as a solution. PDEn employs
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Physics-inspired multimodal machine learning for adaptive correlation fusion based rotating machinery fault diagnosis Inform. Fusion (IF 18.6) Pub Date : 2024-03-30 Dingyi Sun, Yongbo Li, Zheng Liu, Sixiang Jia, Khandaker Noman
Multimodality is a universal characteristic of multi-source monitoring data for rotating machinery. The correlation fusion of multimodal information is a general law to strengthen the cognition of fault features, and an effective way to improve the reliability and robustness of fault diagnosis methods. However, the physical connotation gaps between multimodal information hinder the construction of
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Adversarial attacks on GAN-based image fusion Inform. Fusion (IF 18.6) Pub Date : 2024-03-29 Hui Sun, Siman Wu, Lijun Ma
Image fusion has achieved significant success, owing to the rapid development of digital computing and Generative Adversarial Networks (GANs). GAN-based fusion techniques fuse latent codes through spatial or arithmetic operations to achieve real image fusion, facilitated by encoders. However, security concerns have arisen due to the vulnerability of deep neural networks to adversarial perturbations
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mm-CasGAN: A cascaded adversarial neural framework for mmWave radar point cloud enhancement Inform. Fusion (IF 18.6) Pub Date : 2024-03-28 Kareeb Hasan, Beng Oh, Nithurshan Nadarajah, Mehmet Rasit Yuce
Handling and interpreting sparse 3D point clouds, especially from mmWave radar, presents unique challenges due to the inherent data sparsity and the vast domain difference compared to denser point clouds like those from LiDAR. In this paper, we introduce a novel cascaded generative adversarial network (GAN) approach to bridge this domain gap. The core principle is to progressively refine the radar-based
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Multi-view contrastive clustering via integrating graph aggregation and confidence enhancement Inform. Fusion (IF 18.6) Pub Date : 2024-03-28 Jintang Bian, Xiaohua Xie, Jian-Huang Lai, Feiping Nie
Multi-view clustering endeavors to effectively uncover consistent clustering patterns across multiple data sources or feature spaces. This field grapples with two key challenges: (1) the effective integration and utilization of consistency and complementarity information from diverse view spaces, and (2) the capturing of structural correlations between data samples in the multi-view context. To address
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SingleS2R: Single sample driven Sim-to-Real transfer for Multi-Source Visual-Tactile Information Understanding using multi-scale vision transformers Inform. Fusion (IF 18.6) Pub Date : 2024-03-28 Jing Tang, Zeyu Gong, Bo Tao, Zhouping Yin
Due to variations in light transmission and wear on the contact head, existing visual-tactile dataset building methods typically require a large amount of real-world data, making the dataset building process time-consuming and labor-intensive. Sim-to-Real learning has been proposed to realize Multi-Source Visual-Tactile Information Understanding (MSVTIU) in simulate and real environment, which can
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DBFFT: Adversarial-robust dual-branch frequency domain feature fusion in vision transformers Inform. Fusion (IF 18.6) Pub Date : 2024-03-28 Jia Zeng, Lan Huang, Xingyu Bai, Kangping Wang
Vision transformers (ViTs) have been successful in image recognition. However, it is difficult for ViTs to capture comprehensive information and resist adversarial perturbations by learning features from the spatial domain alone. Features with frequency domain information also play an important role in image classification and robustness improvement. In particular, the relative importance of spatial
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Multi-task multi-objective evolutionary network for hyperspectral image classification and pansharpening Inform. Fusion (IF 18.6) Pub Date : 2024-03-28 Xiande Wu, Jie Feng, Ronghua Shang, JinJian Wu, Xiangrong Zhang, Licheng Jiao, Paolo Gamba
Multi-task learning has commonly been used and performed well at joint visual perception tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract high-frequency information to enhance edges and classify samples, offering potential for performance improvements in multi-task learning. However, differences between tasks can make it challenging to balance their performances
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Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview Inform. Fusion (IF 18.6) Pub Date : 2024-03-27 Yingbai Hu, Fares J. Abu-Dakka, Fei Chen, Xiao Luo, Zheng Li, Alois Knoll, Weiping Ding
Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL
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ADRNet-S*: Asymmetric depth registration network via contrastive knowledge distillation for RGB-D mirror segmentation Inform. Fusion (IF 18.6) Pub Date : 2024-03-26 Wujie Zhou, Yuqi Cai, Xiena Dong, Fangfang Qiang, Weiwei Qiu
Mirrors are frequently encountered in computer vision segmentation scenes and can considerably impact salient object detection (SOD) accuracy owing to their complex and variable appearances. In recent studies, several methods have achieved good performance. However, most of their structures are complex and heavy, while lightweight structures fall short in terms of accuracy. In this study, a novel asymmetric
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HiCMAE: Hierarchical Contrastive Masked Autoencoder for self-supervised Audio-Visual Emotion Recognition Inform. Fusion (IF 18.6) Pub Date : 2024-03-26 Licai Sun, Zheng Lian, Bin Liu, Jianhua Tao
Audio-Visual Emotion Recognition (AVER) has garnered increasing attention in recent years for its critical role in creating emotion-aware intelligent machines. Previous efforts in this area are dominated by the supervised learning paradigm. Despite significant progress, supervised learning is meeting its bottleneck due to the longstanding data scarcity issue in AVER. Motivated by recent advances in
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Zero-shot stance detection based on multi-perspective transferable feature fusion Inform. Fusion (IF 18.6) Pub Date : 2024-03-26 Xuechen Zhao, Jiaying Zou, Jinfeng Miao, Lei Tian, Liqun Gao, Bin Zhou, Shengnan Pang
Zero-shot stance detection involves predicting stances that have not previously been encountered by adapting models to learn transferable features by aligning the source and destination target spaces. The acquisition of transferable target-invariant features is crucial for zero-shot stance detection. This work proposes a stance detection technique that can effectively adapt to new unseen targets, and
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PODB: A learning-based polarimetric object detection benchmark for road scenes in adverse weather conditions Inform. Fusion (IF 18.6) Pub Date : 2024-03-26 Zhen Zhu, Xiaobo Li, Jingsheng Zhai, Haofeng Hu
Due to its insensitivity to light intensity and the capability to capture multidimensional information, polarimetric imaging technology has been proven to have advantages over traditional intensity-based imaging techniques for object detection tasks in adverse environmental conditions, particularly in road traffic scenarios. Recently, with the rapid development of artificial intelligence technology
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IMPORTANT-Net: Integrated MRI multi-parametric increment fusion generator with attention network for synthesizing absent data Inform. Fusion (IF 18.6) Pub Date : 2024-03-26 Tianyu Zhang, Tao Tan, Luyi Han, Xin Wang, Yuan Gao, Jarek van Dijk, Antonio Portaluri, Abel Gonzalez-Huete, Anna D’Angelo, Chunyao Lu, Jonas Teuwen, Regina Beets-Tan, Yue Sun, Ritse Mann
Magnetic resonance imaging (MRI) is highly sensitive for lesion detection. Sequences obtained with different settings can capture specific characteristics of lesions. Such multi-parametric MRI information has been shown to aid radiologist performance in lesion classification, as well as improving the performance of artificial intelligence models in various tasks. However, obtaining multi-parametric
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Privacy-preserving data integration and sharing in multi-party IoT environments: An entity embedding perspective Inform. Fusion (IF 18.6) Pub Date : 2024-03-24 Junyu Lu, Henry Leung, Nan Xie
The increasing prevalence of IoT applications highlights the urgency for insightful data fusion and information acquisition, boosting data integration and sharing needs. However, challenges arise in multi-party data sharing due to inherent data heterogeneity and privacy concerns. To address these issues, this paper discusses the feasibility of using embedding vectors as the semantic representation
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Transformer-based self-supervised image super-resolution method for Rotating Synthetic Aperture system via multi-temporal fusion Inform. Fusion (IF 18.6) Pub Date : 2024-03-24 Yu Sun, Xiyang Zhi, Shikai Jiang, Guanghua Fan, Tianjun Shi, Xu Yan
Rotating Synthetic Aperture (RSA) technology is one of the distinctly advantageous Earth geostationary orbit optical remote sensing technologies. However, the continuous rotation of the RSA system’s rectangular primary mirror results in a discernible drop in resolution along the shorter side of the mirror. Additionally, the captured images exhibit periodic and time-varying characteristics. To improve
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Transformer-based multimodal change detection with multitask consistency constraints Inform. Fusion (IF 18.6) Pub Date : 2024-03-24 Biyuan Liu, Huaixin Chen, Kun Li, Michael Ying Yang
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A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management Inform. Fusion (IF 18.6) Pub Date : 2024-03-22 Sayed Pedram Haeri Boroujeni, Abolfazl Razi, Sahand Khoshdel, Fatemeh Afghah, Janice L. Coen, Leo O’Neill, Peter Fule, Adam Watts, Nick-Marios T. Kokolakis, Kyriakos G. Vamvoudakis
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Efficient networks for textureless feature registration via free receptive field Inform. Fusion (IF 18.6) Pub Date : 2024-03-21 Yuandong Ma, Meng Yu, Hezheng Lin, Chun Liu, Mengjie Hu, Qing Song
In the real-world scenarios, challenges such as changes in viewpoint, variations in lighting conditions, and the presence of blur effects are pervasive issues faced in the field of image registration, making the rapid and accurate establishment of correspondences between images exceedingly difficult. Presently, encoder-based methods often entail larger parameter sizes and longer execution times. Additionally
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GPT-4V with emotion: A zero-shot benchmark for Generalized Emotion Recognition Inform. Fusion (IF 18.6) Pub Date : 2024-03-21 Zheng Lian, Licai Sun, Haiyang Sun, Kang Chen, Zhuofan Wen, Hao Gu, Bin Liu, Jianhua Tao
Recently, GPT-4 with Vision (GPT-4V) has demonstrated remarkable visual capabilities across various tasks, but its performance in emotion recognition has not been fully evaluated. To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: , , , , , and . This paper collectively refers to these tasks as “Generalized Emotion Recognition (GER)”
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Recursive filtering for two-dimensional systems with amplify-and-forward relays: Handling degraded measurements and dynamic biases Inform. Fusion (IF 18.6) Pub Date : 2024-03-20 Fan Wang, Zidong Wang, Jinling Liang, Quanbo Ge, Steven X. Ding
This paper is concerned with the recursive filtering issue for a type of two-dimensional shift-varying systems communicated via imperfect networks. The communication network undergoes degraded measurements, stochastic biases, and channel noises, all reflecting real-world constraints. The degraded measurements are governed by three random variable sets with known statistical details, while the stochastic
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Multimodal fusion-based spatiotemporal incremental learning for ocean environment perception under sparse observation Inform. Fusion (IF 18.6) Pub Date : 2024-03-20 Lei Lei, Jie Huang, Yu Zhou
Accurate ocean environment perception is crucial for weather and climate prediction. Environmental limitations and deployment costs constrain satellite and buoy real-time observation, leading to sparse data availability. This paper proposes a novel approach, multimodal fusion-based spatiotemporal incremental learning, enhancing the ocean environment perception under sparse observations. This method
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Joint Semantic Segmentation using representations of LiDAR point clouds and camera images Inform. Fusion (IF 18.6) Pub Date : 2024-03-20 Yue Wu, Jiaming Liu, Maoguo Gong, Qiguang Miao, Wenping Ma, Cai Xu
LiDAR and camera are two common vision sensors used in the real world, producing complementary point cloud and image data. While multimodal data has previously been found mostly in 3D detection and tracking, we aim to study large-scale semantic segmentation by multimodal data fusion rather than only knowledge transfer or distillation. We show that fusing LiDAR features with camera features and abandoning
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A novel intuitionistic fuzzy generator for low-contrast color image enhancement technique Inform. Fusion (IF 18.6) Pub Date : 2024-03-20 Chithra Selvam, Reegan Jebadass Johnson Jebadass, Dhanasekar Sundaram, Lakshmanan Shanmugam
Fuzzy logic systems play a significant role in various fields including image processing. Addressing the challenge of enhancing low-illumination images, which often contain a high degree of uncertain information, is a complex task. To overcome this issue, this study introduces a novel intuitionistic fuzzy generator for enhancing low-contrast images. Initially, a low-illumination image is considered
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A review of cancer data fusion methods based on deep learning Inform. Fusion (IF 18.6) Pub Date : 2024-03-20 Yuxin Zhao, Xiaobo Li, Changjun Zhou, Hao Peng, Zhonglong Zheng, Jun Chen, Weiping Ding
With advancements in modern medical technology, an increasing amount of cancer-related information can be acquired through various means, such as genomics, proteomics, imaging, and pathology. However, these datasets come from diverse sources and possess heterogeneity and complexity in terms of data types, formats, and quality, which pose challenges for cancer diagnosis, treatment, and prognosis evaluation
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Learning emotional prompt features with multiple views for visual emotion analysis Inform. Fusion (IF 18.6) Pub Date : 2024-03-19 Qinfu Xu, Yiwei Wei, Shaozu Yuan, Jie Wu, Leiquan Wang, Chunlei Wu
Visual emotion analysis(VEA) aiming to detect the emotions behind images, has gained increasing attention with the development of online social media. Recent studies in prompt learning have significantly advanced visual emotion classification. However, these methods usually utilize random vectors or non-emotional texts as the initialization for prompt optimization. This restricts the emotional semantic
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Federated fusion learning with attention mechanism for multi-client medical image analysis Inform. Fusion (IF 18.6) Pub Date : 2024-03-18 Muhammad Irfan, Khalid Mahmood Malik, Khan Muhammad
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Frequency Integration and Spatial Compensation Network for infrared and visible image fusion Inform. Fusion (IF 18.6) Pub Date : 2024-03-18 Naishan Zheng, Man Zhou, Jie Huang, Feng Zhao
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A novel federated multi-view clustering method for unaligned and incomplete data fusion Inform. Fusion (IF 18.6) Pub Date : 2024-03-16 Yazhou Ren, Xinyue Chen, Jie Xu, Jingyu Pu, Yonghao Huang, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, Lifang He
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Self-supervised multi-frame depth estimation with visual-inertial pose transformer and monocular guidance Inform. Fusion (IF 18.6) Pub Date : 2024-03-16 Xiang Wang, Haonan Luo, Zihang Wang, Jin Zheng, Xiao Bai
Self-supervised monocular depth estimation has been a popular topic since it does not need labor-intensive depth ground truth collection. However, the accuracy of monocular network is limited as it can only utilize context provided in the single image, ignoring the geometric clues resided in videos. Most recently, multi-frame depth networks are introduced to the self-supervised depth learning framework
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Multi-modal fusion for business process prediction in call center scenarios Inform. Fusion (IF 18.6) Pub Date : 2024-03-16 Long Cheng, Li Du, Cong Liu, Yang Hu, Fang Fang, Tomas Ward
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Fusion decision strategies for multiple criterion preferences based on three-way decision Inform. Fusion (IF 18.6) Pub Date : 2024-03-16 Zhaohui Qi, Hui Li, Fang Liu, Tao Chen, Jianhua Dai
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Deconfounded multi-organ weakly-supervised semantic segmentation via causal intervention Inform. Fusion (IF 18.6) Pub Date : 2024-03-15 Kaitao Chen, Shiliang Sun, Youtian Du
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Fusion-based intelligent decision method with factor code and factor pedigree in the system fault evolution process Inform. Fusion (IF 18.6) Pub Date : 2024-03-15 Shasha Li, Tiejun Cui, Amar Jain
The key to information fusion lies in the clarification and integration of the information domains comprising all information and using factor space is an effective way to achieve the above goals. We studied the intelligent representation and logical reasoning of the system fault evolution process (SFEP), determined the relationship between factor influence and fault state and proposed an SFEP intelligent