Information Fusion最新文献

{"title":"Diff-PC: Identity-preserving and 3D-aware controllable diffusion for zero-shot portrait customization","authors":"Yifang Xu, Benxiang Zhai, Chenyu Zhang, Ming Li, Yang Li, Sidan Du","doi":"10.1016/j.inffus.2024.102869","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102869","url":null,"abstract":"Portrait customization (PC) has recently garnered significant attention due to its potential applications. However, existing PC methods lack precise identity (ID) preservation and face control. To address these tissues, we propose <ce:bold>Diff-PC</ce:bold>, a <ce:bold>diff</ce:bold>usion-based framework for zero-shot <ce:bold>PC</ce:bold>, which generates realistic portraits with high ID fidelity, specified facial attributes, and diverse backgrounds. Specifically, our approach employs the 3D face predictor to reconstruct the 3D-aware facial priors encompassing the reference ID, target expressions, and poses. To capture fine-grained face details, we design ID-Encoder that fuses local and global face features. Subsequently, we devise ID-Ctrl using the 3D face to guide the alignment of ID features. We further introduce ID-Injector to enhance ID fidelity and facial controllability. Finally, training on our collected ID-centric dataset improves face similarity and text-to-image (T2I) alignment. Extensive experiments demonstrate that Diff-PC surpasses state-of-the-art methods in ID preservation, face control, and T2I consistency. Notably, the face similarity improves by about +3% on all datasets. Furthermore, our method is compatible with multi-style foundation models.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"22 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[formula omitted]-MGSVM: Controllable multi-granularity support vector algorithm for classification and regression","authors":"Yabin Shao, Youlin Hua, Zengtai Gong, Xueqin Zhu, Yunlong Cheng, Laquan Li, Shuyin Xia","doi":"10.1016/j.inffus.2024.102867","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102867","url":null,"abstract":"The <mml:math altimg=\"si140.svg\" display=\"inline\"><mml:mi>ν</mml:mi></mml:math> support vector machine (<mml:math altimg=\"si140.svg\" display=\"inline\"><mml:mi>ν</mml:mi></mml:math>-SVM) is an enhanced algorithm derived from support vector machines using parameter <mml:math altimg=\"si140.svg\" display=\"inline\"><mml:mi>ν</mml:mi></mml:math> to replace the original penalty coefficient <mml:math altimg=\"si4.svg\" display=\"inline\"><mml:mi>C</mml:mi></mml:math>. Because of the narrower range of <mml:math altimg=\"si140.svg\" display=\"inline\"><mml:mi>ν</mml:mi></mml:math> compared with the infinite range of <mml:math altimg=\"si4.svg\" display=\"inline\"><mml:mi>C</mml:mi></mml:math>, <mml:math altimg=\"si140.svg\" display=\"inline\"><mml:mi>ν</mml:mi></mml:math>-SVM generally outperforms the standard SVM. Granular ball computing is an information fusion method that enhances system robustness and reduces uncertainty. To further improve the efficiency and robustness of support vector algorithms, this paper introduces the concept of multigranularity granular balls and proposes the controllable multigranularity SVM (<mml:math altimg=\"si436.svg\" display=\"inline\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math>-MGSVM) and the controllable multigranularity support vector regression machine (<mml:math altimg=\"si436.svg\" display=\"inline\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math>-MGSVR). These models use granular computing theory, replacing original fine-grained points with coarse-grained “granular balls” as inputs to a classifier or regressor. By introducing control parameter <mml:math altimg=\"si140.svg\" display=\"inline\"><mml:mi>ν</mml:mi></mml:math>, the number of support granular balls can be further reduced, thereby enhancing computational efficiency and improving robustness and interpretability. Furthermore, this paper derives and solves the dual models of <mml:math altimg=\"si436.svg\" display=\"inline\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math>-MGSVM and <mml:math altimg=\"si436.svg\" display=\"inline\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math>-MGSVR and conducts a comparative study on the relationship between the granular ball SVM (GBSVM) and the <mml:math altimg=\"si436.svg\" display=\"inline\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math>-MGSVM model, elucidating the importance of control parameters. Experimental results demonstrate that <mml:math altimg=\"si436.svg\" display=\"inline\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math>-MGSVM and <mml:math altimg=\"si436.svg\" display=\"inline\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math>-MGSVR not only improve accuracy and fitting performance but also effectively reduce the number of support granular balls.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"43 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in perception system with multi-sensor fusion for embodied agents","authors":"Hao Du, Lu Ren, Yuanda Wang, Xiang Cao, Changyin Sun","doi":"10.1016/j.inffus.2024.102859","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102859","url":null,"abstract":"The multi-sensor data fusion perception technology, as a pivotal technique for achieving complex environmental perception and decision-making, has been garnering extensive attention from researchers. To date, there has been a lack of comprehensive review articles discussing the research progress of multi-sensor fusion perception systems for embodied agents, particularly in terms of analyzing the agent’s perception of itself and the surrounding scene. To address this gap and encourage further research, this study defines key terminology and analyzes datasets from the past two decades, focusing on advancements in multi-sensor fusion SLAM and multi-sensor scene perception. These key designs can aid researchers in gaining a better understanding of the field and initiating research in the domain of multi-sensor fusion perception for embodied agents. In this survey, we begin with a brief introduction to common sensor types and their characteristics. We then delve into the multi-sensor fusion perception datasets tailored for the domains of autonomous driving, drones, unmanned ground vehicles, and unmanned surface vehicles. Following this, we discuss the classification and fundamental principles of existing multi-sensor data fusion SLAM algorithms, and present the experimental outcomes of various classical fusion frameworks. Subsequently, we comprehensively review the technologies of multi-sensor data fusion scene perception, including object detection, semantic segmentation, instance segmentation, and panoramic understanding. Finally, we summarize our findings and discuss potential future developments in multi-sensor fusion perception technology.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"47 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insight at the right spot: Provide decisive subgraph information to Graph LLM with reinforcement learning","authors":"Tiesunlong Shen, Erik Cambria, Jin Wang, Yi Cai, Xuejie Zhang","doi":"10.1016/j.inffus.2024.102860","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102860","url":null,"abstract":"Large language models (LLMs) cannot see or understand graphs. The current Graph LLM method transform graph structures into a format LLMs understands, utilizing LLM as a predictor to perform graph-learning task. However, these approaches have underperformed in graph-learning tasks. The issues arise because these methods typically rely on a fixed neighbor hop count for the target node set by expert experience, limiting the LLM’s access to only a certain range of neighbor information. Due to the black-box nature of LLM, it is challenging to determine which specific pieces of neighborhood information can effectively assist LLMs in making accurate inferences, which prevents LLMs from generating correct inferences. This study proposes to assist LLM in gaining insight at the right <ce:bold><ce:italic>s</ce:italic></ce:bold>pot by <ce:bold><ce:italic>p</ce:italic></ce:bold>rov<ce:bold><ce:italic>i</ce:italic></ce:bold>ding <ce:bold><ce:italic>de</ce:italic></ce:bold>cisive subgraph information to Graph LLM with <ce:bold><ce:italic>r</ce:italic></ce:bold>einforcement learning (<ce:bold><ce:italic>Spider</ce:italic></ce:bold>). A reinforcement subgraph detection module was designed to search for essential neighborhoods that influence LLM’s predictions. A decisive node-guided network was then applied to guide the reinforcement subgraph network, allowing LLMs to rely more on crucial nodes within the essential neighborhood for predictions. Essential neighborhood and decisive node information are provided to LLM in text form without the requirement of retraining. Experiments on five graph learning datasets demonstrate the effectiveness of the proposed model against all baselines, including GNN and LLM methods.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"50 5 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel hybrid model combining Vision Transformers and Graph Convolutional Networks for monkeypox disease effective diagnosis","authors":"Bihter Das, Huseyin Alperen Dagdogen, Muhammed Onur Kaya, Resul Das","doi":"10.1016/j.inffus.2024.102858","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102858","url":null,"abstract":"Accurate diagnosis of monkeypox is challenging due to the limitations of current diagnostic techniques, which struggle to account for skin lesions’ complex visual and structural characteristics. This study aims to develop a novel hybrid model that combines the strengths of Vision Transformers (ViT), ResNet50, and AlexNet with Graph Convolutional Networks (GCN) to improve monkeypox diagnostic accuracy. Our method captures both the visual features and structural relationships within skin lesions, offering a more comprehensive approach to classification. Rigorous testing on two distinct datasets demonstrated that the ViT+GCN model achieved superior accuracy, particularly excelling in binary classification with 100% accuracy and multi-class classification with a 97% accuracy rate. These findings indicate that integrating visual and structural information enhances diagnostic reliability. While promising, this model requires further development, including larger datasets and optimization for real-time applications. Overall, this approach advances dermatological diagnostics and holds potential for broader applications in diagnosing other skin-related diseases.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"3 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient self-supervised heterogeneous graph representation learning with reconstruction","authors":"Yujie Mo, Heng Tao Shen, Xiaofeng Zhu","doi":"10.1016/j.inffus.2024.102846","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102846","url":null,"abstract":"Heterogeneous graph representation learning (HGRL), as one of powerful techniques to process the heterogeneous graph data, has shown superior performance and attracted increasing attention. However, existing HGRL methods still face issues to be addressed: (i) They capture the consistency among different meta-path-based views to induce expensive computation costs and possibly cause dimension collapse. (ii) They ignore the complementarity within each meta-path-based view to degrade the model’s effectiveness. To alleviate these issues, in this paper, we propose a new self-supervised HGRL framework to capture the consistency among different views, maintain the complementarity within each view, and avoid dimension collapse. Specifically, the proposed method investigates the correlation loss to capture the consistency among different views and reduce the dimension redundancy, as well as investigates the reconstruction loss to maintain complementarity within each view to benefit downstream tasks. We further theoretically prove that the proposed method can effectively incorporate task-relevant information into node representations, thereby enhancing performance in downstream tasks. Extensive experiments on multiple public datasets validate the effectiveness and efficiency of the proposed method on downstream tasks.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"116 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PHIM-MIL: Multiple instance learning with prototype similarity-guided feature fusion and hard instance mining for whole slide image classification","authors":"Yining Xie, Zequn Liu, Jing Zhao, Jiayi Ma","doi":"10.1016/j.inffus.2024.102847","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102847","url":null,"abstract":"The large size of whole slide images (WSIs) in pathology makes it difficult to obtain fine-grained annotations. Therefore, multi-instance learning (MIL) methods are typically utilized to classify histopathology WSIs. However, current models overly focus on local features of instances, neglecting connection between local features and global features. Additionally, they tend to recognize simple instances while struggling to distinguish hard instances. To address the above issues, we design a two-stage MIL model training approach (PHIM-MIL). In the first stage, a downstream aggregation model is pre-trained to equip it with the ability to recognize simple instances. In the second stage, we integrate global information and make the model focus on mining hard instances. First, the similarity between instances and prototypes is leveraged for weighted aggregation and hence obtaining semi-global features, which helps model understand the relationship between each instance and the global features. Then, instance features and semi-global features are fused to enhance instance feature information, bringing similar instances closer while alienating dissimilar ones. Finally, the hard instance mining strategy is employed to process the fused features, improving the pre-trained aggregation model’s capability to recognize and handle hard instances. Extensive experimental results on the GastricCancer and Camelyon16 datasets demonstrate that PHIM-MIL outperforms other latest state-of-the-art methods in terms of performance and computing cost. Meanwhile, PHIM-MIL continues to deliver consistent performance improvements when the feature extraction network is replaced.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"46 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion-enhanced multi-label feature selection with sparse supplementation","authors":"Yonghao Li, Xiangkun Wang, Xin Yang, Wanfu Gao, Weiping Ding, Tianrui Li","doi":"10.1016/j.inffus.2024.102813","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102813","url":null,"abstract":"The exponential increase of multi-label data over various domains demands the development of effective feature selection methods. However, current sparse-learning-based feature selection methods that use LASSO-norm and <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm fail to handle two crucial issues for multi-label data. Firstly, LASSO-based methods remove features with zero-weight values during the feature selection process, some of which may have a certain degree of classification ability. Secondly, <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm-based methods may select redundant features that lead to inefficient classification results. To overcome these issues, we propose a novel sparse supplementation norm that combines inner product regularization and <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm as a novel fusion norm. This innovative fusion norm is designed to enhance the sparsity of feature selection models by leveraging the inherent row-sparse property in the <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm. Specifically, the inner product regularization norm can maintain features with potentially useful classification information, which may be discarded in traditional LASSO-based methods. At the same time, the inner product regularization norm can remove redundant features, which is introduced in traditional <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm-based methods. By incorporating this fusion norm into the Sparse-supplementation Regularized multi-label Feature Selection (SRFS) model, our method mitigates feature omission and feature redundancy, ensuring more effective and efficient feature selection for multi-label classification tasks. The experimental results on various benchmark datasets validate the efficiency and effectiveness of our proposed SRFS model.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"83 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative DDoS defense for SDN-based AIoT with autoencoder-enhanced federated learning","authors":"Jie Ma, Wei Su","doi":"10.1016/j.inffus.2024.102820","DOIUrl":"https://doi.org/10.1016/j.inffus.2024.102820","url":null,"abstract":"The massive number of edge-connected IoT devices currently in SD-AIoT can be weaponized to launch Distributed Denial of Service attacks. Nevertheless, centralized DDoS defense schemes that excessively rely on up-to-date labeled training data are significantly inefficient due to the scarcity of such datasets. The privacy of these datasets and the widespread emergence of adversarial attacks make it difficult for autonomous system collaborators to share such sensitive data. To this end, we propose a novel decentralized defense scheme based on a trusted Federated Learning framework for AIoT scenarios. In particular, it consists of: (1) an outlier-aware Semi-supervised attack detection model for anomaly detection based on a Federated Learning framework that supports the robust identification of attack classes with a limited number of labeled outliers to reduce the false alarm rate; (2) a novel Secure Multiparty Computation method for trusted aggregation of local model updates to enhance the transmission privacy of collaborators’ parameters; (3) a mitigation mechanism based on horizontal cooperation to reduce the impact of packet loss on normal traffic by deploying differentiated speed-limiting policies with attack path pushback. Our evaluation of various attack scenarios and traces from real datasets CICIDS2017 and InSDN shows that the proposed scheme shows significant improvement in terms of accuracy, effectiveness, etc., compared to state-of-the-art SDN-based defense schemes.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"20 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical bipartite graph based multi-view subspace clustering","authors":"Jie Zhou ,&nbsp;Feiping Nie ,&nbsp;Xinglong Luo ,&nbsp;Xingshi He","doi":"10.1016/j.inffus.2024.102821","DOIUrl":"10.1016/j.inffus.2024.102821","url":null,"abstract":"<div><div>Multi-view subspace clustering has attracted much attention because of its effectiveness in unsupervised learning. The high time consumption and hyper-parameters are the main obstacles to its development. In this paper, we present a novel method to effectively solve these two defects. First, we employ the bisecting k-means method to generate anchors and construct the hierarchical bipartite graph, which greatly reduce the time consumption. Moreover, we adopt an auto-weighted allocation strategy to learn appropriate weight factors for each view, which can avoid the influence of hyper-parameters. Furthermore, by imposing low rank constraints on the fusion graph, our proposed method can directly obtained the cluster indicators without any post-processing operations. Finally, numerous experiments verify the superiority of proposed method.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"117 ","pages":"Article 102821"},"PeriodicalIF":14.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}