-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
Faster nonconvex low-rank matrix learning for image low-level and high-level vision: A unified framework Inform. Fusion (IF 18.6) Pub Date : 2024-03-13 Hengmin Zhang, Jian Yang, Jianjun Qian, Chen Gong, Xin Ning, Zhiyuan Zha, Bihan Wen
This study introduces a unified approach to tackle challenges in both low-level and high-level vision tasks for image processing. The framework integrates faster nonconvex low-rank matrix computations and continuity techniques to yield efficient and high-quality results. In addressing real-world image complexities like noise, variations, and missing data, the framework exploits the intrinsic low-rank
-
Sarcasm driven by sentiment: A sentiment-aware hierarchical fusion network for multimodal sarcasm detection Inform. Fusion (IF 18.6) Pub Date : 2024-03-13 Hao Liu, Runguo Wei, Geng Tu, Jiali Lin, Cheng Liu, Dazhi Jiang
Sarcasm is a form of sentiment expression that highlights the disparity between a person’s true intentions and the content they explicitly present. With the exponential increase in multimodal data on social platforms, the detection of sarcasm across various modes has become a pivotal area of research. Although previous studies have extensively examined multimodal feature extraction, fusion, and the
-
A novel multi-criteria conflict evidence combination method and its application to pattern recognition Inform. Fusion (IF 18.6) Pub Date : 2024-03-12 Yilin Dong, Ningning Jiang, Rigui Zhou, Changming Zhu, Lei Cao, Tianyu Liu, Yuzhuo Xu, Xinde Li
In recent years, the Dempster–Shafer Theory (DST) has been widely applied in areas such as target classification and multi-modal fusion due to its advantages in uncertain reasoning. However, in DST, when there exists highly conflicts between Sources of Evidence (SoEs), it often leads to counterintuitive fusion results, thereby affecting the performance of the final fused decision-making. To eliminate
-
Large-scale group decision-making involving community representatives: A perspective of combining strong and weak ties Inform. Fusion (IF 18.6) Pub Date : 2024-03-11 Tong Wu
In the era of social media, the issue of large-scale group decision-making (LSGDM) is becoming increasingly prominent. The complexity of large group interactions increases rapidly with the expansion of the group size. Current research has often used cluster analysis to reduce the dimensionality of LSGDM, but the decision agents following dimensionality reduction are not clearly defined, which hinders
-
A semantic-driven coupled network for infrared and visible image fusion Inform. Fusion (IF 18.6) Pub Date : 2024-03-11 Xiaowen Liu, Hongtao Huo, Jing Li, Shan Pang, Bowen Zheng
In order to be adapted to high-level vision tasks, several infrared and visible image fusion methods cascade with the downstream network to enhance the semantic information of fusion results. However, due to the feature-level heterogeneities between fusion and downstream tasks, these methods suffer from the loss of pixel-level information and incomplete reconstruction of semantic-level information
-
Transformer-based vision-language alignment for robot navigation and question answering Inform. Fusion (IF 18.6) Pub Date : 2024-03-11 Haonan Luo, Ziyu Guo, Zhenyu Wu, Fei Teng, Tianrui Li
The task of robot navigation and question answering, which is also known as Embodied Question Answering (EQA), places its emphasis on empowering agents to actively explore their environments and deliver answers to user inquiries. Considering the extensive range of potential applications, particularly in the realms of home robots and personal assistants, the Embodied Question Answering task has attracted
-
A flexible multi-temporal orthoimage mosaicking method based on dynamic variable patches Inform. Fusion (IF 18.6) Pub Date : 2024-03-08 Xiaoyu Yu, Jun Pan, Shengtong Chen, Mi Wang
Orthoimage mosaicking plays an important role in remote sensing applications, such as environment monitoring and protection. However, the cloud coverage in remote sensing images often results in substantial loss of information and degradation of data quality, bringing lots of challenges to orthoimage mosaicking. Therefore, this paper proposes a flexible multi-temporal orthoimage mosaicking method based
-
Online multi-hypergraph fusion learning for cross-subject emotion recognition Inform. Fusion (IF 18.6) Pub Date : 2024-03-07 Tongjie Pan, Yalan Ye, Yangwuyong Zhang, Kunshu Xiao, Hecheng Cai
-
Local feature matching using deep learning: A survey Inform. Fusion (IF 18.6) Pub Date : 2024-03-07 Shibiao Xu, Shunpeng Chen, Rongtao Xu, Changwei Wang, Peng Lu, Li Guo
Local feature matching enjoys wide-ranging applications in the realm of computer vision, encompassing domains such as image retrieval, 3D reconstruction, and object recognition. However, challenges persist in improving the accuracy and robustness of matching due to factors like viewpoint and lighting variations. In recent years, the introduction of deep learning models has sparked widespread exploration
-
Model-level attention and batch-instance style normalization for federated learning on medical image segmentation Inform. Fusion (IF 18.6) Pub Date : 2024-03-05 Fubao Zhu, Yanhui Tian, Chuang Han, Yanting Li, Jiaofen Nan, Ni Yao, Weihua Zhou
Federated learning (FL) offers an effective privacy protection mechanism for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, FL allows each medical site to act as a client, forming its own data domain. FL has the potential to enhance the performance of models on known domains. However, practical deployment faces the challenge of domain generalization (DG)
-
Self-paced semi-supervised feature selection with application to multi-modal Alzheimer’s disease classification Inform. Fusion (IF 18.6) Pub Date : 2024-03-05 Chao Zhang, Wentao Fan, Bo Wang, Chunlin Chen, Huaxiong Li
Semi-supervised multi-modal learning has attracted much attention due to the expense and scarcity of data labels, especially in disease diagnosis field. Most existing methods follow the paradigm by iteratively inferring the pseudo-labels of unlabeled data and add them into training sequence, but they ignore the reliability of those pseudo-labels, where inaccurate and wrong supervision will lead to
-
Hierarchical relationship modeling in multi-agent reinforcement learning for mixed cooperative–competitive environments Inform. Fusion (IF 18.6) Pub Date : 2024-03-05 Shaorong Xie, Yang Li, Xinzhi Wang, Han Zhang, Zhenyu Zhang, Xiangfeng Luo, Hang Yu
In multi-agent reinforcement learning (MARL), information fusion through relationship modeling can effectively learn behavior strategies. However, the high dynamics among heterogeneous interactive agents in mixed cooperative–competitive environments pose difficulties for relational modeling. Traditional MARL solutions concatenate all agents’ states based on the global relationship, which is unrealistic
-
FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems Inform. Fusion (IF 18.6) Pub Date : 2024-03-05 Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Ivan DeAndres-Tame, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Weisong Zhao, Xiangyu Zhu, Zheyu Yan, Xiao-Yu Zhang, Jinlin Wu, Zhen Lei, Suvidha Tripathi, Mahak Kothari, Md Haider Zama, Debayan Deb, Bernardo Biesseck, Pedro Vidal, Roger Granada, Guilherme Fickel, Gustavo Führ, David Menotti
This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard experimental protocols. FRCSyn-onGoing is based on the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first face
-
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting Inform. Fusion (IF 18.6) Pub Date : 2024-03-04 Wei Ju, Yusheng Zhao, Yifang Qin, Siyu Yi, Jingyang Yuan, Zhiping Xiao, Xiao Luo, Xiting Yan, Ming Zhang
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous downstream applications such as urban planning and transportation management. However, the efficacy of existing methods remains sub-optimal due to their
-
MamlFormer: Priori-experience Guiding Transformer Network via Manifold Adversarial Multi-modal Learning for Laryngeal Histopathological Grading Inform. Fusion (IF 18.6) Pub Date : 2024-03-02 Pan Huang, Chentao Li, Peng He, Hualiang Xiao, Yifang Ping, Peng Feng, Sukun Tian, Hu Chen, Francesco Mercaldo, Antonella Santone, Hui-yuan Yeh, Jing Qin
Pathologic grading of laryngeal squamous cell carcinoma (LSCC) plays a crucial role in diagnosis, prognosis, and migration. However, the grading performance and interpretability of the intelligent grading model based on LSCC low magnification images are poor. This is because it lacks the delicate nuclear information and information more relevant to grading contained in the high magnification images
-
A graph neural approach for group recommendation system based on pairwise preferences Inform. Fusion (IF 18.6) Pub Date : 2024-03-02 Roza Abolghasemi, Enrique Herrera Viedma, Paal Engelstad, Youcef Djenouri, Anis Yazidi
Pairwise preference information, which involves users expressing their preferences by comparing items, plays a crucial role in decision-making and has recently found application in recommendation systems. In this study, we introduce GcPp, a clustering algorithm that leverages pairwise preference data to generate recommendations for user groups. Initially, we construct individual graphs for each user
-
Hierarchical damage correlations for old photo restoration Inform. Fusion (IF 18.6) Pub Date : 2024-03-02 Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He
Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two
-
Temporal-aware structure-semantic-coupled graph network for traffic forecasting Inform. Fusion (IF 18.6) Pub Date : 2024-03-01 Mao Chen, Liangzhe Han, Yi Xu, Tongyu Zhu, Jibin Wang, Leilei Sun
The spatial–temporal graph neural networks have been a critical approach to capturing the complicated spatial–temporal dependencies inherent in traffic series for more accurate forecasting. However, the issue of graph indistinguishability demands further attention, as graphs learned by existing methods tend to converge to implicit and indistinguishable representations, deviating from the genuine distribution
-
Multi-modality MRI fusion with patch complementary pre-training for internet of medical things-based smart healthcare Inform. Fusion (IF 18.6) Pub Date : 2024-03-01 Jun Lyu, Xiudong Chen, Salman A. AlQahtani, M. Shamim Hossain
Magnetic Resonance Imaging (MRI) is a pivotal neuroimaging technique capable of generating images with various contrasts, known as multi-modal images. The integration of these diverse modalities is essential for improving model performance across various tasks. However, in real clinical scenarios, acquiring MR images for all modalities is frequently hindered by factors such as patient comfort and scanning
-
Boosting urban prediction tasks with domain-sharing knowledge via meta-learning Inform. Fusion (IF 18.6) Pub Date : 2024-02-29 Dongkun Wang, Jieyang Peng, Xiaoming Tao, Yiping Duan
Urban prediction tasks refer to predicting urban indicators (, traffic, temperature, etc.) using urban big data, which is crucial for understanding the urban patterns, and further benefits the urban public administration. An empirical study indicates that there are correlated patterns among urban prediction tasks from various domains, which suggests the existence of domain-sharing knowledge. Aggregating
-
FAST-CA: Fusion-based Adaptive Spatial–Temporal Learning with Coupled Attention for airport network delay propagation prediction Inform. Fusion (IF 18.6) Pub Date : 2024-02-29 Chi Li, Xixian Qi, Yuzhe Yang, Zhuo Zeng, Lianmin Zhang, Jianfeng Mao
The issue of delay propagation prediction in airport networks has garnered increasing global attention, particularly due to its profound impact on operational efficiency and passenger satisfaction in modern air transportation systems. Despite research advancements in this domain, existing methodologies often fall short of comprehensively addressing the challenges associated with predicting delay propagation
-
Unsupervised hyperspectral pansharpening via low-rank diffusion model Inform. Fusion (IF 18.6) Pub Date : 2024-02-28 Xiangyu Rui, Xiangyong Cao, Li Pang, Zeyu Zhu, Zongsheng Yue, Deyu Meng
Hyperspectral pansharpening is a process of merging a high-resolution panchromatic (PAN) image and a low-resolution hyperspectral (LRHS) image to create a single high-resolution hyperspectral (HRHS) image. Existing Bayesian-based HS pansharpening methods require designing handcraft image prior to characterize the image features, and deep learning-based HS pansharpening methods usually require a large
-
Multi-modal heart failure risk estimation based on short ECG and sampled long-term HRV Inform. Fusion (IF 18.6) Pub Date : 2024-02-28 Sergio González, Abel Ko-Chun Yi, Wan-Ting Hsieh, Wei-Chao Chen, Chun-Li Wang, Victor Chien-Chia Wu, Shang-Hung Chang
Cardiovascular diseases, including Heart Failure (HF), remain a leading global cause of mortality, often evading early detection. In this context, accessible and effective risk assessment is indispensable. Traditional approaches rely on resource-intensive diagnostic tests, typically administered after the onset of symptoms. The widespread availability of electrocardiogram (ECG) technology and the power
-
Reliable trajectory prediction in scene fusion based on spatio-temporal Structure Causal Model Inform. Fusion (IF 18.6) Pub Date : 2024-02-28 Jianmin Liu, Hui Lin, Xiaoding Wang, Lizhao Wu, Sahil Garg, Mohammad Mehedi Hassan
Existing methods for trajectory prediction predominantly employ scene fusion to enhance model performance. However, they fail to provide a rational explanation as to why the fusion of the scene context and trajectories improves model performance, which prevents them from identifying the fundamental factors limiting model performance. Hence, this paper introduces a Structured Causal Model for trajectory
-
Trustworthy multi-view clustering via alternating generative adversarial representation learning and fusion Inform. Fusion (IF 18.6) Pub Date : 2024-02-27 Wenqi Yang, Minhui Wang, Chang Tang, Xiao Zheng, Xinwang Liu, Kunlun He
Multi-view clustering (MVC) has attached extensive attention as it provides an effective approach to deal with the unlabeled data in real-world applications. To enjoy the strong feature extraction capacity of deep learning, traditional shallow MVC methods are further extended to deep version. Though achieve superiorities in many fields, most existing deep MVC models are still limited by their lacking
-
CD-GAN: A robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors Inform. Fusion (IF 18.6) Pub Date : 2024-02-24 Jin-Ju Wang, Nicolas Dobigeon, Marie Chabert, Ding-Cheng Wang, Ting-Zhu Huang, Jie Huang
In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e.g., optical or radar). Even when considering only optical images, this task has proven to be challenging as soon as the sensors differ by their spatial and/or spectral resolutions. This paper
-
Multi-sensor fusion federated learning method of human posture recognition for dual-arm nursing robots Inform. Fusion (IF 18.6) Pub Date : 2024-02-24 Jiaxin Wang, Huanyu Deng, Yulong Wang, Jiexin Xie, Hui Zhang, Yang Li, Shijie Guo
Human posture estimation plays a significant role in the growth of intelligent nursing robot, a field that demands high accuracy and respect for privacy. Nevertheless, traditional approaches to enhancing data-driven studies in this domain often face challenges, primarily due to privacy concerns in sensitive healthcare environments. Federated Learning rises as the solution to the problem, as it not
-
The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0 Inform. Fusion (IF 18.6) Pub Date : 2024-02-23 Xiao Wang, Yutong Wang, Jing Yang, Xiaofeng Jia, Lijun Li, Weiping Ding, Fei-Yue Wang
As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer “Human-Centric” Safe, Secure, Sustainable, Sensitive, Service, and Smartness “6S” manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental
-
Diverse semantic information fusion for Unsupervised Person Re-Identification Inform. Fusion (IF 18.6) Pub Date : 2024-02-23 Qingsong Hu, Huafeng Li, Zhanxuan Hu, Feiping Nie
Unsupervised Person Re-Identification (Re-ID) has achieved considerable success through leveraging various approaches that rely on hard pseudo-labels. Prior work mainly focused on improving the quality of pseudo-labels or enhancing the robustness of representation learning model. However, there has been little focus on exploring the contextual semantic information, which can reveal rich relations within
-
Adversarial filtering based evasion and backdoor attacks to EEG-based brain-computer interfaces Inform. Fusion (IF 18.6) Pub Date : 2024-02-23 Lubin Meng, Xue Jiang, Xiaoqing Chen, Wenzhong Liu, Hanbin Luo, Dongrui Wu
A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recent studies have shown that machine learning models in BCIs are vulnerable to adversarial
-
A multi-criteria group decision-making method based on consistency of interval-valued distributed preference relations Inform. Fusion (IF 18.6) Pub Date : 2024-02-22 Wenjun Chang, Chao Fu
In multi-criteria group decision making (MCGDM) with interval-valued distributed preference relations (IDPRs), the consistency of IDPRs is important to guarantee the rationality of IDPRs and the rationality of group-satisfactory solutions. In existing studies, the consistency of the score intervals of IDPRs was considered to ensure the consistency of IDPRs. Meanwhile, a comparison of the score intervals
-
Comprehensive systematic review of information fusion methods in smart cities and urban environments Inform. Fusion (IF 18.6) Pub Date : 2024-02-21 Mohammed A. Fadhel, Ali M. Duhaim, Ahmed Saihood, Ahmed Sewify, Mokhaled N.A. Al-Hamadani, A.S. Albahri, Laith Alzubaidi, Ashish Gupta, Sayedali Mirjalili, Yuantong Gu
Smart cities result from integrating advanced technologies and intelligent sensors into modern urban infrastructure. The Internet of Things (IoT) and data integration are pivotal in creating interconnected and intelligent urban spaces. In this literature review, we explore the different methods of information fusion used in smart cities, along with their advantages and challenges. However, there are
-
Decoupling semantic and localization for semantic segmentation via magnitude-aware and phase-sensitive learning Inform. Fusion (IF 18.6) Pub Date : 2024-02-21 Qingqing Yan, Shu Li, Zongtao He, Xun Zhou, Mengxian Hu, Chengju Liu, Qijun Chen
Semantic segmentation requires the simultaneous generation of strong semantic and precise localization segmentation results. However, their inherent paradox drives most existing methods to perform trade-offs or overcompensation between high-level semantics and fine localization during resolution reconstruction, which may lead to limited performance or enormous computation costs. To this end, inspired
-
Adversarial attacks and defenses in explainable artificial intelligence: A survey Inform. Fusion (IF 18.6) Pub Date : 2024-02-19 Hubert Baniecki, Przemyslaw Biecek
Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions. However, recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods, putting their security and trustworthiness into question. The possibility
-
Region-based online selective examination for weakly supervised semantic segmentation Inform. Fusion (IF 18.6) Pub Date : 2024-02-17 Qi Chen, Yun Chen, Yuheng Huang, Xiaohua Xie, Lingxiao Yang
Current weakly supervised semantic segmentation methods usually generate noisy pseudo-labels. Training segmentation models with these labels tends to overfit the noise, leading to poor performance. Existing approaches often rely on iterative updates of pseudo-labels at pixel or image-level, ignoring the importance of region-level characteristics. The recently introduced Segment Anything Model (SAM)
-
Average consensus of whole-process privacy protection: A scale parameter method Inform. Fusion (IF 18.6) Pub Date : 2024-02-17 Jing Zhang, Jianquan Lu, Jinling Liang, Jie Zhong
Privacy-preserving average consensus is a distributed control/estimation framework where a group of agents cooperatively compute the average of initial state. Meanwhile, the initial state and real-time state (such as initial opinion, initial location, real-time location, real-time active power ratio) in the communication and computation process may contain some sensitive information, which do not want
-
Few-shot object detection: Research advances and challenges Inform. Fusion (IF 18.6) Pub Date : 2024-02-17 Zhimeng Xin, Shiming Chen, Tianxu Wu, Yuanjie Shao, Weiping Ding, Xinge You
Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such methods rely on large-scale labeled training samples for each object category to ensure accurate detection, but obtaining extensive annotated data is a labor-intensive and expensive process in many real-world scenarios. To
-
Deep learning-based low overlap point cloud registration for complex scenario: The review Inform. Fusion (IF 18.6) Pub Date : 2024-02-16 Yuehua Zhao, Jiguang Zhang, Shibiao Xu, Jie Ma
Most studies on point cloud registration have established the problem in the case of ideal point cloud data. Although the state-of-the-art approaches have achieved amazing results on multiple public datasets, the issue of low overlap point cloud data invalidating state-of-the-art methods is acting as a latent challenge that has not been solved. Therefore, a profound analysis about why existing registration
-
Ensemble methods and semi-supervised learning for information fusion: A review and future research directions Inform. Fusion (IF 18.6) Pub Date : 2024-02-16 José Luis Garrido-Labrador, Ana Serrano-Mamolar, Jesús Maudes-Raedo, Juan J. Rodríguez, César García-Osorio
Advances over the past decade at the intersection of information fusion methods and Semi-Supervised Learning (SSL) are investigated in this paper that grapple with challenges related to limited labelled data. To do so, a bibliographic review of papers published since 2013 is presented, in which ensemble methods are combined with new machine learning algorithms. A total of 128 new proposals using SSL
-
Data augmentation for deep visual recognition using superpixel based pairwise image fusion Inform. Fusion (IF 18.6) Pub Date : 2024-02-16 D. Sun, F. Dornaika
Data augmentation is an important paradigm for boosting the generalization capability of deep learning in image classification tasks. Image augmentation using cut-and-paste strategies has shown very good performance improvement for deep learning. However, these existing methods often overlook the image’s discriminative local context and rely on ad hoc regions consisting of square or rectangular local
-
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions Inform. Fusion (IF 18.6) Pub Date : 2024-02-15 Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, Simone Stumpf
Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the
-
Fusing pairwise modalities for emotion recognition in conversations Inform. Fusion (IF 18.6) Pub Date : 2024-02-15 Chunxiao Fan, Jie Lin, Rui Mao, Erik Cambria
Multimodal fusion has the potential to significantly enhance model performance in the domain of Emotion Recognition in Conversations (ERC) by efficiently integrating information from diverse modalities. However, existing methods face challenges as they directly integrate information from different modalities, making it difficult to assess the individual impact of each modality during training and to
-
Atlantis: Aesthetic-oriented multiple granularities fusion network for joint multimodal aspect-based sentiment analysis Inform. Fusion (IF 18.6) Pub Date : 2024-02-15 Luwei Xiao, Xingjiao Wu, Junjie Xu, Weijie Li, Cheng Jin, Liang He
Joint Multi-modal Aspect-based Sentiment Analysis (JMASA) is a challenging task that seeks to identify all aspect-sentiment pairs from multimodal data. Current JMASA studies are insufficient in bridging the representational gap between textual and visual modalities. Additionally, they largely emphasize image feature extraction, neglecting the exploration of image presentation forms, like aesthetic
-
Shared contents alignment across multiple granularities for robust SAR-optical image matching Inform. Fusion (IF 18.6) Pub Date : 2024-02-15 Hong Zhang, Yuxin Yue, Haojie Li, Pan Liu, Yusheng Jia, Wei He, Zhihui Wang
The matching of SAR and optical images is crucial for various remote sensing applications, such as monitoring natural disasters and change detection. However, the significant differences in geometric and radiometric properties between these two sensors pose challenges for robust and accurate matching. Recent deep learning-based approaches mitigate modality differences by aligning all contents on a
-
Enhancing multi-modal fusion in visual dialog via sample debiasing and feature interaction Inform. Fusion (IF 18.6) Pub Date : 2024-02-14 Chenyu Lu, Jun Yin, Hao Yang, Shiliang Sun
Visual dialog aims to accomplish multiple rounds of dialog by fusing information extracted from images, captions, and previous question-answer pairs. As a vision-language task, visual dialog encounters challenges related to language bias and vision bias. These biases create an imbalance in multi-modal fusion, resulting in shortcut learning and significantly compromising the model’s robustness. Moreover
-
Emotion detection for misinformation: A review Inform. Fusion (IF 18.6) Pub Date : 2024-02-12 Zhiwei Liu, Tianlin Zhang, Kailai Yang, Paul Thompson, Zeping Yu, Sophia Ananiadou
With the advent of social media, an increasing number of netizens are sharing and reading posts and news online. However, the huge volumes of misinformation (e.g., fake news and rumors) that flood the internet can adversely affect people’s lives, and have resulted in the emergence of rumor and fake news detection as a hot research topic. The emotions and sentiments of netizens, as expressed in social
-
Fusion of standard and ordinal dropout techniques to regularise deep models Inform. Fusion (IF 18.6) Pub Date : 2024-02-10 Francisco Bérchez-Moreno, Juan C. Fernández, César Hervás-Martínez, Pedro A. Gutiérrez
Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to
-
Fusing consensus knowledge: A federated learning method for fault diagnosis via privacy-preserving reference under domain shift Inform. Fusion (IF 18.6) Pub Date : 2024-02-10 Baoxue Li, Pengyu Song, Chunhui Zhao
Recently, federated fault diagnosis has garnered growing attention due to its promising capabilities in information fusion with privacy preservation. However, most of the existing approaches are based on the assumptions of no domain shift between multiple factories and no unseen domains for online applications. In actual industry, these assumptions are generally unsatisfied due to prominent environmental
-
Managing heterogeneous preferences and multiple consensus behaviors with self-confidence in large-scale group decision making Inform. Fusion (IF 18.6) Pub Date : 2024-02-08 Wenqi Liu, Yuzhu Wu, Xin Chen, Francisco Chiclana
With the rapid increase of experts, groups or organizations involved in decision making, the problem of large-scale group decision making (LSGDM) has attracted increasing attention in the whole research field. Behavioral management and heterogeneous preference representation structures are two fundamental aspects of LSGDM problems. However, psychological functioning has been less considered in existing
-
Pansharpening via semi-framelet-guided sparse reconstruction Inform. Fusion (IF 18.6) Pub Date : 2024-02-08 Zhong-Cheng Wu, Gemine Vivone, Ting-Zhu Huang, Liang-Jian Deng
Pansharpening involves the spatial super-resolution of a low-resolution multispectral (LR-MS) image by leveraging a simultaneously acquired panchromatic (PAN) image, aiming to generate a high-resolution multispectral (HR-MS) image. Such an inverse problem mainly requires more accurately establishing the relation between the underlying HR-MS image and the PAN image. Because of the high redundancy of
-
Time-dependent trip generation for bike sharing planning: A multi-task memory-augmented graph neural network Inform. Fusion (IF 18.6) Pub Date : 2024-02-07 Yuebing Liang, Zhan Zhao, Fangyi Ding, Yihong Tang, Zhengbing He
Due to its various social and environmental benefits, bike sharing has been gaining popularity worldwide and, in response, many cities have gradually expanded their bike sharing systems (BSSs). For a growing station-based BSS, it is essential to plan new stations based on existing ones, which requires predicting not only the overall trip intensity at each station but also its temporal distribution
-
Representation, optimization and generation of fuzzy measures Inform. Fusion (IF 18.6) Pub Date : 2024-02-07 Gleb Beliakov, Jian-Zhang Wu, Weiping Ding
We review recent literature on three aspects of fuzzy measures: their representations, learning optimal fuzzy measures and random generation of various types of fuzzy measures. These three aspects are interdependent: methods of learning fuzzy measures depend on their representation, and may also include random generation as one of the steps, on the other hand different representations also affect generation