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FISTNet: FusIon of STyle-path generative Networks for facial style transfer Inform. Fusion (IF 14.7) Pub Date : 2024-07-09 Sunder Ali Khowaja, Lewis Nkenyereye, Ghulam Mujtaba, Ik Hyun Lee, Giancarlo Fortino, Kapal Dev
With the surge in emerging technologies such as Metaverse, spatial computing, and generative AI, the application of facial style transfer has gained much interest from researchers and startups enthusiasts alike. StyleGAN methods have paved the way for transfer-learning strategies that could reduce the dependency on the vast data available for the training process. However, StyleGAN methods tend to
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Distributed credible evidence fusion with privacy-preserving Inform. Fusion (IF 14.7) Pub Date : 2024-07-09 Chaoxiong Ma, Yan Liang, Huixia Zhang, Lianmeng Jiao, Qianqian Song, Yihan Cui
Considering data safety in more and more applied peer-to-peer networks, such as wireless sensor networks, has become the focus of information fusion, this paper proposes the problem of credible evidence fusion (CEF) in a distributed system with privacy-preserving, where agent’s raw evidence is shared only with authenticated neighbors while access or inference by non-neighbors is prevented. This privacy-preserving
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Exploiting game equilibrium mechanisms towards social trust-based group consensus reaching Inform. Fusion (IF 14.7) Pub Date : 2024-07-08 Yuanyuan Fu, Decui Liang, Zeshui Xu, Weiyi Duan
In group decision making (GDM), the real or virtual moderator usually provides the suggestion and compensation based on consensus improvement for the decision maker (DM) to promote group consensus. In order to ensure DMs own interests, there are the game among DMs and the game between DM and moderator to effect the consensus reaching. In this case, we analyze the inherent game reasons and investigate
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Bounded rationality consensus reaching process with prospect theory and preventing individual weight manipulation for multi-attribute group decision making Inform. Fusion (IF 14.7) Pub Date : 2024-07-07 Feifei Jin, Hui Lin, Ligang Zhou
Owing to the development and popularization of social media, multi-attribute group decision-making (MAGDM) adopting social network analysis (SNA) has appealed to widespread focus for a few years. However, most existing research on SNA has not simultaneously considered the expert's weight manipulation behavior and bounded rationality during the consensus-reaching process (CRP). To overcome this limitation
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Drug repositioning by multi-aspect heterogeneous graph contrastive learning and positive-fusion negative sampling strategy Inform. Fusion (IF 14.7) Pub Date : 2024-07-06 Junkai Liu, Fuyuan Hu, Quan Zou, Prayag Tiwari, Hongjie Wu, Yijie Ding
Drug repositioning (DR) is a promising approach for identifying novel indications of existing drugs. Computational methods for drug repositioning have been recognised as effective ways to discover the associations between drugs and diseases. However, most computational DR methods ignore the significance of heterogeneous graph augmentation when conducting contrastive learning, which plays a critical
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From sight to insight: A multi-task approach with the visual language decoding model Inform. Fusion (IF 14.7) Pub Date : 2024-07-05 Wei Huang, Pengfei Yang, Ying Tang, Fan Qin, Hengjiang Li, Diwei Wu, Wei Ren, Sizhuo Wang, Jingpeng Li, Yucheng Zhu, Bo Zhou, Jingyuan Sun, Qiang Li, Kaiwen Cheng, Hongmei Yan, Huafu Chen
Visual neural decoding aims to unlock the mysteries of how the human brain interprets the visual world through predicting perceived visual information from visual neural activity. While early studies made some progress in decoding visual activity for singular type of information, they failed to concurrently reveal the multi-level interweaving linguistic information in the brain. Here, we developed
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PALADIN: A process-based constraint language for data validation Inform. Fusion (IF 14.7) Pub Date : 2024-07-04 Antonio Jesus Diaz-Honrubia, Philipp D. Rohde, Emetis Niazmand, Ernestina Menasalvas, Maria-Esther Vidal
In many processes, ranging from medical treatments to supply chains and employee management, there is a growing need to gather information with the objective of enhancing the efficiency of the process in question. Often, the information gathered from different stages of a process resides in disparate storage systems, necessitating an information fusion process. Post-fusion, it is common to encounter
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FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation Inform. Fusion (IF 14.7) Pub Date : 2024-07-03 Weiping Ding, Sheng Geng, Haipeng Wang, Jiashuang Huang, Tianyi Zhou
In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of
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Correlation filter based single object tracking: A review Inform. Fusion (IF 14.7) Pub Date : 2024-07-02 Ashish Kumar, Rubeena Vohra, Rachna Jain, Muyu Li, Chenquan Gan, Deepak Kumar Jain
In recent years, correlation filter-based (CF) tracking algorithms have gained momentum in the field of visual tracking. CF tracking algorithms have achieved compelling performance by addressing its limitations such as boundary effect and filter corruption during various tracking and target appearance variations. Many researchers have attempted to provide better efficiency and tracking results by extracting
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Security of target recognition for UAV forestry remote sensing based on multi-source data fusion transformer framework Inform. Fusion (IF 14.7) Pub Date : 2024-07-02 Hailin Feng, Qing Li, Wei Wang, Ali Kashif Bashir, Amit Kumar Singh, Jinshan Xu, Kai Fang
Unmanned Aerial Vehicle (UAV) remote sensing object recognition plays a vital role in a variety of sectors including military, agriculture, forestry, and construction. Accurate object recognition is critical to the advancement in these fields. While object recognition methods using multimodal remote sensing imagery can improve accuracy and robustness, existing research often ignores object recognition
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Coarse to fine-based image–point cloud fusion network for 3D object detection Inform. Fusion (IF 14.7) Pub Date : 2024-07-02 Meilan Hao, Zhongkang Zhang, Lei Li, Kejian Dong, Long Cheng, Prayag Tiwari, Xin Ning
Enhancing original LiDAR point cloud features with virtual points has gained widespread attention in multimodal information fusion. However, existing methods struggle to leverage image depth information due to the sparse nature of point clouds, hindering proper alignment with camera-derived features. We propose a novel 3D object detection method that refines virtual point clouds using a coarse-to-fine
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Embodied navigation with multi-modal information: A survey from tasks to methodology Inform. Fusion (IF 14.7) Pub Date : 2024-07-02 Yuchen Wu, Pengcheng Zhang, Meiying Gu, Jin Zheng, Xiao Bai
Embodied AI aims to create agents that complete complex tasks by interacting with the environment. A key problem in this field is embodied navigation which understands multi-modal information and reaches desired positions for manipulations. For this, we present a comprehensive survey of existing embodied navigation techniques. As various embodied navigation tasks have been designed for different scenarios
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Multimodal Aspect-Based Sentiment Analysis: A survey of tasks, methods, challenges and future directions Inform. Fusion (IF 14.7) Pub Date : 2024-07-01 Tianyu Zhao, Ling-ang Meng, Dawei Song
With the development of social media, users increasingly tend to express their sentiments (broadly including sentiment polarities, emotions and sarcasm, etc.) associated with fine-grained aspects (e.g., entities) in multimodal content (mostly encompassing images and texts). Consequently, automated recognition of sentiments within multimodal content over different aspects, namely Multimodal Aspect-Based
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Bootstrap Latent Prototypes for graph positive-unlabeled learning Inform. Fusion (IF 14.7) Pub Date : 2024-06-29 Chunquan Liang, Yi Tian, Dongmin Zhao, Mei Li, Shirui Pan, Hongming Zhang, Jicheng Wei
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X-TF-GridNet: A time–frequency domain target speaker extraction network with adaptive speaker embedding fusion Inform. Fusion (IF 14.7) Pub Date : 2024-06-28 Fengyuan Hao, Xiaodong Li, Chengshi Zheng
Target speaker extraction (TSE) which has the capability to directly extract desired speech given enrollment utterances of the target speaker has attracted more and more attention for its potential applications in solving the cocktail-party problem. Despite the considerable progress made by existing time-domain methods, which have become the dominant approach for TSE, these methods often significantly
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Fusion social network and regret theory for a consensus model with minority opinions in large-scale group decision making Inform. Fusion (IF 14.7) Pub Date : 2024-06-28 Yufeng Shen, Xueling Ma, Hengjie Zhang, Jianming Zhan
The change in data structure and social paradigm have promoted the rapid development of decision sciences. Large-scale group decision making (LSGDM) has gained widespread attention as an effective approach for addressing complex decision making issues. However, a common challenge in LSGDM scenarios is how to appropriately deal with minority and non-consensus opinions of decision makers, since they
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Consensus-oriented linguistic multi-criteria group sorting method incorporating dynamic trust management Inform. Fusion (IF 14.7) Pub Date : 2024-06-28 Shitao Zhang, Fengli Zhu, Muhammet Deveci, Xiaodi Liu
Multi-criteria group sorting (MCGS) concerns the multi-person process of evaluating alternatives and assigning them to predetermined ordered categories under certain criteria. Identifying class thresholds and coordinating conflicting opinions are two primary challenges within this process. Taking into account the 2-tuple linguistic preferences of decision-makers (DMs), we propose a consensus-oriented
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CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis Inform. Fusion (IF 14.7) Pub Date : 2024-06-27 Xiaoan Yan, Dong Jiang, Ling Xiang, Yadong Xu, Yulin Wang
When the machinery device operates abnormally, it is not sufficient for fault detection only via extracting fault features from a single sensor due to the latent fault information may be scattered across multiple sensors. Multi-sensory fusion techniques with deep learning framework have attracted increasing attention from researchers due to the exploiting and integration of fault information between
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Dual hypergraphs with feature weighted and latent space learning for the diagnosis of Alzheimer’s disease Inform. Fusion (IF 14.7) Pub Date : 2024-06-26 Yu Luo, Hongmei Chen, Tengyu Yin, Shi-Jinn Horng, Tianrui Li
In recent years of research on the diagnosis of Alzheimer’s disease, capturing data relationships can help improve model performance. However, the simple graph structure can only capture pairwise relationships between data, which cannot model the complex data relationships in real situations. In addition, the redundant features and noise from the original data space also harm the model performance
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Deep learning based multimodal biomedical data fusion: An overview and comparative review Inform. Fusion (IF 14.7) Pub Date : 2024-06-26 Junwei Duan, Jiaqi Xiong, Yinghui Li, Weiping Ding
Multimodal biomedical data fusion plays a pivotal role in distilling comprehensible and actionable insights by seamlessly integrating disparate biomedical data from multiple modalities, effectively circumventing the constraints of single-modal approaches. Over recent decades, the proliferation of biomedical data availability and the advent of advanced techniques such as deep learning and large-scale
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Vision-language joint representation learning for sketch less facial image retrieval Inform. Fusion (IF 14.7) Pub Date : 2024-06-26 Dawei Dai, Shiyu Fu, Yingge Liu, Guoyin Wang
The traditional sketch-based facial image retrieval (SBFIR) framework assumes that a high-quality facial sketch has been prepared prior to the retrieval task. However, drawing such a sketch requires considerable skills and is time consuming, resulting in limited applicability. Sketch less facial image retrieval (SLFIR) framework aims to break these barriers through human–computer interaction during
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Prediction consistency regularization for Generalized Category Discovery Inform. Fusion (IF 14.7) Pub Date : 2024-06-25 Yu Duan, Junzhi He, Runxin Zhang, Rong Wang, Xuelong Li, Feiping Nie
Generalized Category Discovery (GCD) is a recently proposed open-world problem that aims to automatically discover and cluster based on partially labeled data. The mainstream GCD methods typically involve two steps: representation learning and classification assignment. Some methods focus on representation and design effective contrastive learning strategies and subsequently utilize clustering methods
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PnT: Born-again tree-based model via fused decision path encoding Inform. Fusion (IF 14.7) Pub Date : 2024-06-24 Noy Cohen-Shapira, Lior Rokach
Decision forests, such as random forest (RF) are widely used for tabular data, mainly due to their predictive performance and ease of usage. However, given that the forest’s trees may produce contradictory predictions for a certain sample, the usage of decision forests in applications that involve decision-making necessitates further reliability assessment of the predictions for generating a trustworthy
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Cross-modal semantic aligning and neighbor-aware completing for robust text–image person retrieval Inform. Fusion (IF 14.7) Pub Date : 2024-06-24 Tiantian Gong, Junsheng Wang, Liyan Zhang
Most existing text–image person re-identification (TIReID) methods are performed in an ideal environment where both image and text instances are fully intact and identity annotated. However, in real-world open environments, these ideal assumptions often cannot be satisfied. In this study, we are the first to explore how to enhance the robustness of the TIReID model to better adapt to open environments
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Distributed fusion filtering for multi-sensor nonlinear networked systems with multiple fading measurements via stochastic communication protocol Inform. Fusion (IF 14.7) Pub Date : 2024-06-24 Jun Hu, Zhibin Hu, Raquel Caballero-Águila, Xiaojian Yi
This paper studies the distributed fusion filtering (DFF) issue for a class of nonlinear delayed multi-sensor networked systems (MSNSs) subject to multiple fading measurements (MFMs) under stochastic communication protocol (SCP). The phenomenon of MFMs occurs randomly in the network communication channels and is characterized by a diagonal matrix with certain statistical information. In order to decrease
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KaTaGCN: Knowledge-Augmented and Time-Aware Graph Convolutional Network for efficient traffic forecasting Inform. Fusion (IF 14.7) Pub Date : 2024-06-21 Yuyan Wang, Jie Hu, Fei Teng, Lilan Peng, Shengdong Du, Tianrui Li
Dynamic spatio-temporal dependencies and temporal patterns in traffic series are critical factors affecting traffic forecasting accuracy. Due to the intrinsic challenges of incorporating explicit, logical knowledge into the implicit black-box learning process of neural networks, only a few methods effectively use prior knowledge to improve the learning of traffic forecasting. To tackle this problem
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Generative adversarial networks for multi-fidelity matrix completion with massive missing entries Inform. Fusion (IF 14.7) Pub Date : 2024-06-21 Zongqi Liu, Xueguan Song, Jie Yang, Chao Zhang, Dacheng Tao
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Multi-modal visual tracking based on textual generation Inform. Fusion (IF 14.7) Pub Date : 2024-06-20 Jiahao Wang, Fang Liu, Licheng Jiao, Hao Wang, Shuo Li, Lingling Li, Puhua Chen, Xu Liu
Multi-modal tracking has garnered significant attention due to its wide range of potential applications. Existing multi-modal tracking approaches typically merge data from different visual modalities on top of RGB tracking. However, focusing solely on the visual modality is insufficient due to the scarcity of tracking data. Inspired by the recent success of large models, this paper introduces a Multi-modal
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Evidence representation of uncertain information on a frame of discernment with semantic association Inform. Fusion (IF 14.7) Pub Date : 2024-06-20 Xinyang Deng, Xiang Li, Wen Jiang
Belief functions as a powerful model to represent and deal with uncertain information are widely used in information fusion. However, semantic association within a frame of discernment is not well defined in traditional framework of belief function theory. To solve the problem, in this work models and methods for evidence representation of uncertain information on a frame of discernment with semantic
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Towards zero-shot object counting via deep spatial prior cross-modality fusion Inform. Fusion (IF 14.7) Pub Date : 2024-06-18 Jinyong Chen, Qilei Li, Mingliang Gao, Wenzhe Zhai, Gwanggil Jeon, David Camacho
Existing counting models predominantly operate on a specific category of objects, such as crowds and vehicles. The recent emergence of multi-modal foundational models, , Contrastive Language-Image Pre-training (CLIP), has facilitated class-agnostic counting. This involves counting objects of any given class from a single image based on textual instructions. However, CLIP-based class-agnostic counting
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An interpretable and flexible fusion prior to boost hyperspectral imaging reconstruction Inform. Fusion (IF 14.7) Pub Date : 2024-06-18 Wei He, Zongliang Wu, Naoto Yokoya, Xin Yuan
Hyperspectral image (HSI) reconstruction from the compressed measurement captured by the coded aperture snapshot spectral imager system remains a hot topic. Recently, deep-learning-based methods for HSI reconstruction have become the mainstream due to their high performance and efficiency in the testing inference. However, these learning methods do not fully utilize the abundant spectral information
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Multiple rumor source identification in social networks leveraging community and monitor information Inform. Fusion (IF 14.7) Pub Date : 2024-06-17 Ravi Kishore Devarapalli, Soumita Das, Anupam Biswas
Multiple rumor source identification (MRSI) in social networks has become a challenging problem for controlling rumors from spreading automatically. Even though several techniques have been introduced for MRSI, most of them were introduced based on the fact that they knew the underlying diffusion model in advance, which is mostly not possible in real-world scenarios. So, this paper proposes a new algorithm
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Leveraging a self-adaptive mean teacher model for semi-supervised multi-exposure image fusion Inform. Fusion (IF 14.7) Pub Date : 2024-06-16 Qianjun Huang, Guanyao Wu, Zhiying Jiang, Wei Fan, Bin Xu, Jinyuan Liu
Deep learning-based methods have recently shown remarkable advancements in multi-exposure image fusion (MEF), demonstrating significant achievements in improving the fusion quality. Despite their success, the majority of reference images in MEF are artificially generated, inevitably introducing a portion of low-quality ones. Existing methods either utilize these mixed-quality reference images for supervised
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ODFormer: Semantic fundus image segmentation using Transformer for optic nerve head detection Inform. Fusion (IF 14.7) Pub Date : 2024-06-15 Jiayi Wang, Yi-An Mao, Xiaoyu Ma, Sicen Guo, Yuting Shao, Xiao Lv, Wenting Han, Mark Christopher, Linda M. Zangwill, Yanlong Bi, Rui Fan
Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks. Despite the numerous recent advancements in general-purpose semantic
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Multimodal image fusion for the detection of diabetic retinopathy using optimized explainable AI-based Light GBM classifier Inform. Fusion (IF 14.7) Pub Date : 2024-06-13 Pooja Bidwai, Shilpa Gite, Natasha Pahuja, Kishore Pahuja, Ketan Kotecha, Neha Jain, Sheela Ramanna
Diabetic Retinopathy (DR) is a widespread ocular condition and a significant contributor to global blindness. Timely identification and precise diagnosis of DR are essential for successfully managing and avoiding vision impairment. Visualizing and studying the intricate vascular network and other retinal structures has notable difficulties due to several factors that complicate the process. To overcome
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Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers Inform. Fusion (IF 14.7) Pub Date : 2024-06-13 Rahul Yumlembam, Biju Issac, Seibu Mary Jacob, Longzhi Yang
Botnets are computer networks controlled by malicious actors that present significant cybersecurity challenges. They autonomously infect, propagate, and coordinate to conduct cybercrimes, necessitating robust detection methods. This research addresses the sophisticated adversarial manipulations posed by attackers, aiming to undermine machine learning-based botnet detection systems. We introduce a flow-based
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Quantum entanglement and self-attention neural networks: An investigation into passengers and stops characteristics for optimal bus stop localization Inform. Fusion (IF 14.7) Pub Date : 2024-06-11 Hongjie Liu, Tengfei Yuan, Xinhuan Zhang, Hongzhe Xu
Urban transportation significantly contributes to global carbon emissions, thus emphasizing the importance of green and efficient alternatives like public transportation. Enhancing the appeal of public transport through the strategic placement of bus stops can mitigate carbon emissions concurrently. This research proposes a Quantum Entangled Self-Attention Neural Networks (QESANN) model that considers
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Revolutionizing healthcare: IoMT-enabled digital enhancement via multimodal ADL data fusion Inform. Fusion (IF 14.7) Pub Date : 2024-06-11 Hemant Ghayvat, Muhammad Awais, Rebakah Geddam, Prayag Tiwari, Welf Löwe
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Imputation of missing values in multi-view data Inform. Fusion (IF 14.7) Pub Date : 2024-06-11 Wouter van Loon, Marjolein Fokkema, Frank de Vos, Marisa Koini, Reinhold Schmidt, Mark de Rooij
Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all features in a view are likely to be missing simultaneously. This may lead to very large quantities of missing data which, especially when combined with high-dimensionality, can make the application of conditional imputation methods
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Interest-driven community detection on attributed heterogeneous information networks Inform. Fusion (IF 14.7) Pub Date : 2024-06-10 Mengyue Liu, Jun Liu, Yixiang Dong, Rui Mao, Erik Cambria
Community structures within attributed heterogeneous information networks (AHINs) serve as valuable tools for comprehending the functional properties inherent in the real-world systems they mirror. The diverse semantics embedded in AHINs play a pivotal role in shaping distinct community formations. Many existing methods detect communities in AHINs based on the same-type nodes without specifying semantic
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Enhancement and optimisation of human pose estimation with multi-scale spatial attention and adversarial data augmentation Inform. Fusion (IF 14.7) Pub Date : 2024-06-09 Tong Zhang, Qilin Li, Jingtao Wen, C.L. Philip Chen
Human pose estimation, a vital pursuit in the realm of computer vision, aims to predict the spatial coordinates of key points within images. Despite the advancements achieved by employing a Convolution Neural Network (CNN), this task still faces considerable challenges, especially in handling occlusion and overfitting issues. This paper introduces a new human pose estimation network designed to address
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Multi-dimensional classification via class space fusion and comprehensive label correlations Inform. Fusion (IF 14.7) Pub Date : 2024-06-08 Xinyuan Liu, Jihua Zhu, Zhiqiang Tian, Zhongyu Li
Multi-dimensional Classification (MDC) is a new multi-output learning paradigm, where each instance in this framework is annotated with labels from multiple semantic class spaces. The vital challenge in MDC problem is how to address the heterogeneity of output space. Existing methods typically focus on the decomposition of output space, rarely consider the comprehensive fine-grained label correlations
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STFDiff: Remote sensing image spatiotemporal fusion with diffusion models Inform. Fusion (IF 14.7) Pub Date : 2024-06-07 He Huang, Wei He, Hongyan Zhang, Yu Xia, Liangpei Zhang
Spatiotemporal fusion (STF) methods aim to blend satellite images with different spatial and temporal resolutions to support more frequent and precise monitoring. In the past decades, amounts of STF methods have been developed with remarkable success. However, among the existing methods, the traditional methods rely on the linear assumption and fail for complex and diverse scenes with great dynamics
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Unified multimodal fusion transformer for few shot object detection for remote sensing images Inform. Fusion (IF 14.7) Pub Date : 2024-06-07 Abdullah Azeem, Zhengzhou Li, Abubakar Siddique, Yuting Zhang, Shangbo Zhou
Object detection is a fundamental computer vision task with wide applications in remote sensing, but traditional methods strongly rely on large annotated datasets which are difficult to obtain, especially for novel object classes. Few-shot object detection (FSOD) aims to address this by using detectors to learn from very limited labeled data. Recent work fuse multi-modalities like image–text pairs
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CAM: A cross-lingual adaptation framework for low-resource language speech recognition Inform. Fusion (IF 14.7) Pub Date : 2024-06-06 Qing Hu, Yan Zhang, Xianlei Zhang, Zongyu Han, Xilong Yu
In this paper, a novel ross-lingual daptation fraework called CAM is presented for low-resource language speech recognition (LLSR). It is based on the recent popular adapter method. CAM is achieved by adapting self-supervised speech models (SSMs) from source languages to target low-resource languages in a two-stage process. CAM fills two research gaps existing in current methods: (i) language similarity
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Cross-modal interaction and multi-source visual fusion for video generation in fetal cardiac screening Inform. Fusion (IF 14.7) Pub Date : 2024-06-05 Guosong Zhu, Erqiang Deng, Zhen Qin, Fazlullah Khan, Wei Wei, Gautam Srivastava, Hu Xiong, Saru Kumari
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SegLD: Achieving universal, zero-shot and open-vocabulary segmentation through multimodal fusion via latent diffusion processes Inform. Fusion (IF 14.7) Pub Date : 2024-06-05 Hongtao Zheng, Yifei Ding, Zilong Wang, Xinyan Huang
Open-vocabulary learning can identify categories marked during training (seen categories) and generalize to categories not annotated in the training set (unseen categories). It could theoretically extend segmentation systems to more universal applications. However, current open-vocabulary segmentation frameworks are primarily suited for specific tasks or require retraining according to the task, and
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A hierarchical consensus learning model for deep multi-view document clustering Inform. Fusion (IF 14.7) Pub Date : 2024-06-05 Ruina Bai, Ruizhang Huang, Yanping Chen, Yongbin Qin, Yong Xu, Qinghua Zheng
Document clustering, a fundamental task in natural language processing, aims to divede large collections of documents into meaningful groups based on their similarities. Multi-view document clustering (MvDC) has emerged as a promising approach, leveraging information from diverse views to improve clustering accuracy and robustness. However, existing multi-view clustering methods suffer from two issues:
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Distributed multi-robot source term estimation with coverage control and information theoretic based coordination Inform. Fusion (IF 14.7) Pub Date : 2024-06-03 Rohit V. Nanavati, Matthew J. Coombes, Cunjia Liu
In this paper, we introduce a novel coordination strategy for a group of autonomous robots tasked with estimating the source term of an airborne chemical release. This strategy integrates distributed Bayesian filtering, coverage control, information-theoretic sampling, and proximity constraint handling, forming an efficient and fully distributed coordination protocol. In the proposed framework, each
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A comprehensive evaluation method for frailty based on semi-supervised learning and transfer-learning Inform. Fusion (IF 14.7) Pub Date : 2024-06-01 Jiaxi Li, Zhelong Wang, Zheng Wang, Sen Qiu, Daoyong Peng, Ke Zhang, Fang Lin
Frailty evaluation is of great significance for the specific population, which can speed up the treatment process and reduce the adverse effects after treatment. In this article, in order to make up for the shortcomings of the traditional evaluation methods, a comprehensive intelligent evaluation method that integrates physiological data from multiple perspectives, such as mobility, muscle level, and
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Multi-valued verification of commitment systems with uncertainty and inconsistency in multi-source data settings Inform. Fusion (IF 14.7) Pub Date : 2024-06-01 Ghalya Alwhishi, Jamal Bentahar, Ahmed Elwhishi, Witold Pedrycz
In the dynamic landscape of Internet of Things (IoT) applications within multi-source data environments, ensuring the reliability and correctness of system communications has become a paramount concern. This is particularly evident in the presence of commitment protocols with inconsistency and uncertainty. This paper tackles these challenges by introducing a new logic, termed Six-Values Computation
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End-to-end multiview fusion for building mapping from aerial images Inform. Fusion (IF 14.7) Pub Date : 2024-06-01 Qi Chen, Wenxiang Gan, Pengjie Tao, Penglei Zhang, Rongyong Huang, Lei Wang
In the domain of photogrammetry, the fusion of information from multiple views holds the potential to significantly enhance the accuracy and robustness of building mapping. While multiview observation and stereoscopic imaging form the bedrock of photogrammetric projects, current deep learning methodologies predominantly focus on orthophotos and digital surface models (DSMs), often sidelining the rich
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Robust tensor ring-based graph completion for incomplete multi-view clustering Inform. Fusion (IF 14.7) Pub Date : 2024-05-31 Lei Xing, Badong Chen, Changyuan Yu, Jing Qin
Incomplete multi-view clustering (IMVC) aims to enhance clustering performance by leveraging complementary information from multi-view data, even in the presence of missing instances. This is challenging due to the interference caused by these missing data points. Current IMVC algorithms generally adopt two main strategies: either disregarding the missing instances and focusing on observable data for
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Escaping the neutralization effect of modality features fusion in multimodal Fake News Detection Inform. Fusion (IF 14.7) Pub Date : 2024-05-31 Bing Wang, Ximing Li, Changchun Li, Shengsheng Wang, Wanfu Gao
Fake news spreads at unprecedented speeds through online social media, raising many concerns and negative impacts on a variety of domains. To control this issue, Fake News Detection (FND) naturally becomes the chief task while multimodal FND has recently attracted more attention due to the fast-growing multimodal content in online social media. Commonly, the existing multimodal FND methods directly
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Track initialization and re-identification for 3D multi-view multi-object tracking Inform. Fusion (IF 14.7) Pub Date : 2024-05-31 Linh Van Ma, Tran Thien Dat Nguyen, Ba-Ngu Vo, Hyunsung Jang, Moongu Jeon
We propose a 3D multi-object tracking (MOT) solution using only 2D detections from monocular cameras, which automatically initiates/terminates tracks as well as resolves track appearance–reappearance and occlusions. Moreover, this approach does not require detector retraining when cameras are reconfigured but only the camera matrices of reconfigured cameras need to be updated. Our approach is based
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Generating high-resolution hyperspectral time series datasets based on unsupervised spatial-temporal-spectral fusion network incorporating a deep prior Inform. Fusion (IF 14.7) Pub Date : 2024-05-28 Weiwei Sun, Kai Ren, Xiangchao Meng, Gang Yang, Qiang Liu, Lin Zhu, Jiangtao Peng, Jiancheng Li
Over the past decade, image fusion has emerged as an indispensable tool for surface monitoring due to its capability to reconstruct high-quality surface reflectance. While satisfactory progress has been made in the spatial-spectral and spatial-temporal fusion of remote sensing images, the fusion of spatial-temporal-spectral data remains a challenging task. This challenge arises from the presence of
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Individual entity induced label concept set for classification: An information fusion viewpoint Inform. Fusion (IF 14.7) Pub Date : 2024-05-25 Zhonghui Liu, Xiaofei Zeng, Jinhai Li, Fan Min
Formal concept analysis has seldom been employed for classification. This is mainly due to (1) the high time and space complexity of concept lattice construction, and (2) the difficulty of concept lattice based prediction. Inspired by information fusion, this paper introduces a new algorithm named CECS, which constructs a concept set instead of a lattice to ensure efficiency and enable direct classification
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Decoupled variational retinex for reconstruction and fusion of underwater shallow depth-of-field image with parallax and moving objects Inform. Fusion (IF 14.7) Pub Date : 2024-05-25 Jingchun Zhou, Shiyin Wang, Dehuan Zhang, Qiuping Jiang, Kui Jiang, Yi Lin
Underwater imaging often suffers from poor quality due to the complex underwater environment and limitations of hardware equipment, leading to images with shallow depth of field and moving objects, which pose a challenge for information fusion of image sequences from the same underwater scene. To effectively address these problems, we propose a decoupled variational Retinex method for reconstructing
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MRFTrans: Multimodal Representation Fusion Transformer for monocular 3D semantic scene completion Inform. Fusion (IF 14.7) Pub Date : 2024-05-25 Rongtao Xu, Jiguang Zhang, Jiaxi Sun, Changwei Wang, Yifan Wu, Shibiao Xu, Weiliang Meng, Xiaopeng Zhang
The complete understanding of 3D scenes is crucial in robotic visual perception, impacting tasks such as motion planning and map localization. However, due to the limited field of view and scene occlusion constraints of sensors, inferring complete scene geometry and semantic information from restricted observations is challenging. In this work, we propose a novel Multimodal Representation Fusion Transformer
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Sustainable transparency on recommender systems: Bayesian ranking of images for explainability Inform. Fusion (IF 14.7) Pub Date : 2024-05-24 Jorge Paz-Ruza, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Brais Cancela, Carlos Eiras-Franco
Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for recommendations. Among the existing approaches