Information Fusion最新文献_第10页

Team collaboration-oriented multi-agent pathfinding and probabilistic verification 面向团队协作的多智能体寻路与概率验证

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-10 DOI: 10.1016/j.inffus.2026.104125

Xia Wang , Jun Liu , Chris D. Nugent , Shaobing Xu , Guanfeng Wu

Multi-agent pathfinding and its reliable execution in stochastic environments represent a critical challenge for real-world applications, demanding both the planning of efficient paths and the formal assurance of safe, conflict-free operation. This paper introduces a novel methodology framework to address this dual requirement. To maximize operational efficiency, we introduce a strategy for optimal goal allocation for team collaboration, integrating it with the conflict-based search algorithm to minimize the total move counts required for mission completion. The second component is an integrated verification process grounded in probabilistic model checking. We model the multi-agent path execution process under stochastic uncertainties using a Markov decision process. By leveraging the probabilistic model checker and probabilistic computation tree logic, the framework formally verifies critical safety properties, ensuring conflict-free and deadlock-free path execution. Furthermore, it evaluates the effectiveness of proposed behavioral constraints designed to mitigate stochastic delays, thereby verifying the overall system safety. By fusing multi-agent planning, probabilistic reasoning, and formal logic-based verification, the proposed framework establishes a foundation amenable to natural extension for addressing multi-agent decision-making and uncertainty estimation. Case study results demonstrate that our methodology effectively selects the pathfinding solution with the minimum move count while significantly enhancing overall system safety through these formally verified behavioral constraints.

多智能体寻路及其在随机环境中的可靠执行对现实世界的应用来说是一个关键的挑战，它既要求规划有效的路径，又要求正式保证安全、无冲突的操作。本文介绍了一种新的方法框架来解决这一双重要求。为了使操作效率最大化，我们引入了一种团队协作的最佳目标分配策略，将其与基于冲突的搜索算法相结合，以最小化完成任务所需的总移动次数。第二个组成部分是基于概率模型检查的集成验证过程。利用马尔可夫决策过程对随机不确定性下的多智能体路径执行过程进行建模。通过利用概率模型检查器和概率计算树逻辑，框架正式验证关键安全属性，确保无冲突和无死锁的路径执行。此外，它还评估了旨在减轻随机延迟的行为约束的有效性，从而验证了整个系统的安全性。通过融合多智能体规划、概率推理和基于形式逻辑的验证，该框架为解决多智能体决策和不确定性估计问题建立了一个可自然扩展的基础。案例研究结果表明，我们的方法有效地选择了移动次数最少的寻路解决方案，同时通过这些正式验证的行为约束显著提高了整体系统的安全性。

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引用次数: 0

SHIFT: Enhancing federated learning robustness through client-side backdoor detection SHIFT：通过客户端后门检测增强联邦学习的健壮性

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-10 DOI: 10.1016/j.inffus.2026.104144

Kang Wang , Liangliang Wang , Zhiquan Liu , Yiyuan Luo , Kai Zhang , Weiwei Li

Federated Learning (FL) is vulnerable to backdoor attacks, where hidden triggers in model updates can induce malicious behavior on specific inputs, ultimately compromising the reliability of FL. However, existing backdoor detection methods require decryption of locally uploaded encrypted models on the server before further detection can be performed. In this paper, we propose SHIFT containing three parts: transferring the backdoor detection task to the client side to significantly reduce the computational burden on the server; employing client-side code obfuscation to prevent malicious clients from analyzing or bypassing the detection mechanism; and utilizing a dynamic risk level mapping mechanism to adaptively adjust the results of the backdoor detection output. SHIFT can directly detect unencrypted data on the client side. We evaluated the time overhead of SHIFT compared with various backdoor detection schemes based on different encryption methods. Additionally, we assessed its performance in handwritten digit recognition and image classification tasks under single-client and multi-client backdoor attacks, specifically in non-independent and identically distributed (non-IID) scenarios. Experimental results indicate that SHIFT improves backdoor detection efficiency by a factor ranging from 1.28 to 36.65 over existing schemes, while also demonstrating robust performance in detecting and defending against various backdoor attacks, particularly in large-scale, multi-client distributed federated learning systems.

联邦学习（FL）容易受到后门攻击，其中模型更新中的隐藏触发器可能会导致特定输入的恶意行为，最终损害FL的可靠性。然而，现有的后门检测方法需要在服务器上对本地上传的加密模型进行解密，然后才能执行进一步的检测。在本文中，我们提出SHIFT包含三个部分：将后门检测任务转移到客户端，以显着减少服务器的计算负担；采用客户端代码混淆，防止恶意客户端分析或绕过检测机制；并利用动态风险等级映射机制自适应调整后门检测输出结果。SHIFT可以直接检测客户端上未加密的数据。我们评估了SHIFT与基于不同加密方法的各种后门检测方案的时间开销。此外，我们评估了其在单客户端和多客户端后门攻击下手写数字识别和图像分类任务中的性能，特别是在非独立和同分布（non-IID）场景下。实验结果表明，与现有方案相比，SHIFT将后门检测效率提高了1.28到36.65倍，同时在检测和防御各种后门攻击方面也表现出了强大的性能，特别是在大规模、多客户端分布式联邦学习系统中。

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引用次数: 0

Region-based deep metric learning for tackling class overlap in online semi-supervised data stream classification 基于区域的深度度量学习处理在线半监督数据流分类中的类重叠

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-09 DOI: 10.1016/j.inffus.2026.104126

Zhonglin Wu , Hongliang Wang , Tongze Zhang , Hongyuan Liu , Jinxia Guo , Qinli Yang , Junming Shao

Class overlap in data streams presents a significant challenge for real-time classification, particularly when confronted with the high dimensionality and evolving distributions inherent in such streams. Traditional classification methods, typically designed for static datasets, struggle to adapt to the dynamic nature of data streams, where both high-dimensional feature spaces and class imbalance exacerbate the complexity of classifying overlapping regions. In this paper, we propose a novel deep metric learning framework specifically tailored to address the challenges of class overlap in high-dimensional data streams. Our approach introduces two key innovations. First, we develop a multi-anchor sample mining mechanism based on neighborhood rough set theory, which partitions the data into non-overlapping and overlapping regions. By utilizing region-specific triplet-margin losses and hinge embedding loss, we construct a more refined discriminative metric space that significantly enhances the separation of overlapping classes. Furthermore, we introduce a dynamic, density-aware real-time label propagation mechanism with class-imbalance compensation. This component integrates real-time distribution estimation with a nonlinear adaptive threshold controller, enabling dual adaptivity: (1) dynamically re-weighting density contributions via inverse-frequency scaling to mitigate the dominance of majority classes and (2) adjusting threshold boundaries for frequent classes while relaxing propagation criteria for rare classes through nonlinear adjustments. Empirical evaluations on both synthetic and real-world data streams demonstrate that our method not only improves balanced accuracy but also enhances robustness in the presence of class overlap and class imbalance, outperforming state-of-the-art techniques.

数据流中的类重叠对实时分类提出了重大挑战，特别是当面对此类流中固有的高维和不断发展的分布时。传统的分类方法通常是针对静态数据集设计的，难以适应数据流的动态特性，高维特征空间和类不平衡加剧了重叠区域分类的复杂性。在本文中，我们提出了一种新的深度度量学习框架，专门用于解决高维数据流中类重叠的挑战。我们的方法引入了两个关键的创新。首先，提出了一种基于邻域粗糙集理论的多锚点样本挖掘机制，将数据划分为非重叠和重叠区域；通过利用特定区域的三重边缘损失和铰链嵌入损失，我们构建了一个更精细的判别度量空间，显著增强了重叠类的分离。此外，我们还引入了一种具有类不平衡补偿的动态、密度感知的实时标签传播机制。该组件将实时分布估计与非线性自适应阈值控制器集成在一起，实现了双重自适应：(1)通过反频率缩放动态地重新加权密度贡献，以减轻多数类的优势地位；(2)通过非线性调整调整频繁类的阈值边界，同时放宽罕见类的传播标准。对合成数据流和真实数据流的实证评估表明，我们的方法不仅提高了平衡的准确性，而且在类重叠和类不平衡的情况下增强了鲁棒性，优于最先进的技术。

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引用次数: 0

Tokenized EEG signals with large language models for epilepsy detection via multimodal information fusion 基于多模态信息融合的大语言模型脑电信号标记化检测

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-07 DOI: 10.1016/j.inffus.2026.104128

XingchiChen , Fushen Xie , Fa Zhu , Shuanglong Zhang , Xiaoyang Lu , Qing Li , Rong Chen , Dazhou Li , David Camacho

The detection of epileptic seizures using multi-sensor EEG signals is a challenging task due to the inherent complexity of the signals, the variability in sensor configurations, and the difficulty in distinguishing the weak inter-class difference. To address these challenges, we propose a novel multimodal information fusion framework that integrates a large language model (LLM) and a multimodal EEG feature tokenization method for enhanced epilepsy detection. This paper adopts a multimodal feature extraction (MFE) method to effectively generate multimodal feature representations from EEG signals and extract different feature representations of EEG signals from different signal domains. In addition, we design a multimodal EEG feature tokenization method to tokenize EEG signal features and fuse the semantic information, solving the problem of fusing epileptic EEG features with semantic information in prompt words. We use the powerful reasoning and pattern recognition capabilities of pre-trained LLMs to accurately and robustly detect epileptic events. The proposed method is evaluated on a public dataset. Extensive experimental results show that the proposed method outperforms the current comparative methods in multiple performance indicators.

由于信号固有的复杂性、传感器配置的可变性以及难以区分微弱的类间差异，利用多传感器脑电图信号检测癫痫发作是一项具有挑战性的任务。为了解决这些挑战，我们提出了一种新的多模态信息融合框架，该框架集成了大语言模型（LLM）和多模态EEG特征标记化方法，以增强癫痫检测。本文采用多模态特征提取（multimodal feature extraction， MFE）方法，有效地从脑电信号中生成多模态特征表示，并从不同的信号域中提取脑电信号的不同特征表示。此外，我们设计了一种多模态脑电信号特征标记方法，对脑电信号特征进行标记并融合语义信息，解决了癫痫脑电信号特征与提示词语义信息的融合问题。我们使用预训练llm的强大推理和模式识别能力来准确和稳健地检测癫痫事件。在一个公共数据集上对该方法进行了评估。大量的实验结果表明，该方法在多个性能指标上优于现有的比较方法。

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引用次数: 0

SG-DGLF: A similarity-guided dual-graph learning framework SG-DGLF：一个相似引导的双图学习框架

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-07 DOI: 10.1016/j.inffus.2026.104127

Menglin Yu , Shuxia Lu , Jiacheng Cong

Graph neural networks (GNNs) perform exceptionally well in node classification, but graph neural networks face severe challenges when dealing with imbalanced node classification. On the one hand, the model is prone to overfitting due to the small number of minority class samples. GNN’s message passing mechanism amplifies this problem, causing the model to overfit specific features and local neighborhood structures of minority class nodes rather than learning general patterns, resulting in poor generalization ability. On the other hand, the scarcity of samples leads to high variance in model training. Model performance is highly dependent on specific training samples and local graph structures, and is extremely sensitive to data partitioning, ultimately resulting in severe performance fluctuations and unstable results. In this work, to address the issues of minority class overfitting and high model variance faced by GNNs in imbalanced scenarios, we propose the dual-graph framework, A similarity-Guided Dual-Graph Learning Framework (SG-DGLF). To address the problem of overfitting for minority classes, the framework introduces a dynamic threshold random capture mechanism based on similarity, which supplements minority class samples by generating pseudo labels. Secondly, we leverage graph diffusion-based propagation and random edge dropping strategy to create new graphs, thereby increasing node diversity to alleviate the problem of excessive model variance. Empirically, SG-DGLF significantly outperforms advanced baseline methods on multiple imbalanced datasets. This validates the effectiveness of our framework in mitigating the problems of overfitting minority classes and high model variance.

图神经网络（Graph neural network, gnn）在节点分类方面表现优异，但在处理不平衡节点分类时面临严峻挑战。一方面，由于少数类样本数量较少，模型容易出现过拟合。GNN的消息传递机制放大了这一问题，导致模型过度拟合少数类节点的特定特征和局部邻域结构，而不是学习一般模式，导致泛化能力差。另一方面，样本的稀缺性导致模型训练的方差很大。模型性能高度依赖于特定的训练样本和局部图结构，对数据划分极其敏感，最终导致性能波动严重，结果不稳定。在这项工作中，为了解决gnn在不平衡场景下面临的少数类过拟合和高模型方差问题，我们提出了双图框架，即相似度引导的双图学习框架（SG-DGLF）。为了解决少数类的过拟合问题，该框架引入了基于相似性的动态阈值随机捕获机制，该机制通过生成伪标签来补充少数类样本。其次，我们利用基于图扩散的传播和随机丢边策略来创建新图，从而增加节点多样性，以缓解模型方差过大的问题。从经验上看，SG-DGLF在多个不平衡数据集上显著优于先进的基线方法。这验证了我们的框架在缓解过拟合少数类和高模型方差问题方面的有效性。

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引用次数: 0

WDASR: A wavelet-based deformable attention network for cardiac cine MRI super-resolution with spatiotemporal motion modeling WDASR：一种基于小波的可变形注意网络，用于心脏电影MRI超分辨率的时空运动建模

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-06 DOI: 10.1016/j.inffus.2025.104116

Jun Lyu , Xunkang Zhao , Jing Qin , Chengyan Wang

Cardiac cine MRI is the clinical gold standard for dynamic cardiac assessment, but reducing k-space sampling to accelerate acquisition results in low-resolution images that fail to depict fine anatomical details. Existing super-resolution methods struggle to preserve spatial details and temporal coherence due to limitations in handling non-rigid cardiac deformations and lossy feature downsampling. This paper proposes a Wavelet-based Deformable Attention Super-Resolution Network (WDASR) that addresses these limitations through two key innovations. First, a Frequency Subband Adaptive Alignment (FSAA) module applies deformable convolution to wavelet-decomposed frequency subbands, enabling lossless downsampling that prevents offset over-shifting and allows targeted alignment across neighboring and remote frames. Second, a Cross-Resolution Wavelet Attention (CRWA) module uses temporally-aggregated frequency subbands as low-resolution keys and values, and the current frame as high-resolution query, reducing computational complexity by 75% while effectively integrating multi-scale spatiotemporal information for enhanced texture representation. A bidirectional recurrent mechanism further propagates the enhanced features to maintain temporal consistency. Experiments on public and private datasets demonstrate that WDASR achieves 4 ×  super-resolution with state-of-the-art performance and potential for clinical application.

心脏电影MRI是动态心脏评估的临床金标准，但减少k空间采样以加速采集会导致低分辨率图像无法描绘精细的解剖细节。由于处理非刚性心脏变形和有损特征下采样的局限性，现有的超分辨率方法难以保持空间细节和时间相干性。本文提出了一种基于小波的可变形注意力超分辨率网络（WDASR），通过两个关键创新解决了这些限制。首先，频率子带自适应对准（FSAA）模块对小波分解的频率子带进行可变形卷积，实现无损下采样，防止偏移过移，并允许在相邻帧和远程帧之间进行目标对准。其次，交叉分辨率小波注意（Cross-Resolution Wavelet Attention， CRWA）模块采用时间聚合的频率子带作为低分辨率键和值，当前帧作为高分辨率查询，在有效整合多尺度时空信息的同时，将计算复杂度降低了75%，增强了纹理表征。双向循环机制进一步传播增强的特征以保持时间一致性。在公共和私有数据集上的实验表明，WDASR达到了4 × 超分辨率，具有最先进的性能和临床应用潜力。

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引用次数: 0

Rethink: reveal the impact of semantic distribution transfer from the cross-modal hashing perspective 重新思考：从跨模态散列的角度揭示语义分布转移的影响

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-05 DOI: 10.1016/j.inffus.2026.104123

Yinan Li , Zhi Liu , Jiajun Tang , Binghong Chen , Mingjin Kuai , Jun Long , Zhan Yang

Hashing has been extensively applied in cross-modal retrieval by mapping diverse modalities data into binary codes. Semantic transfer aims to enhance the relevance of heterogeneous representations through migrating valuable information from one modality to another in the unsupervised paradigm. The combination of semantic transfer and hash learning substitutes the dense vector search with Hamming distance, significantly reducing storage requirements and increasing retrieval efficiency. However, the current unsupervised mechanism demonstrates ordinary performance in retrieval precision, which requires more improvement from semantic annotation. Particularly, the mediocre information fusion strategy directly affects the quality of learned hash codes. In this paper, we propose a novel Semantic Transfer framework for Semi-supervised Cross-modal Hashing, denoted as STSCH. Initially, we utilize multiple auto-encoders to learn the high-level semantic representation of each modality. To guarantee the completeness of heterogeneous data, we incorporate them via semantic transfer and analyse the feature distribution of diverse modalities. Furthermore, an asymmetric hash learning framework between individual modality-specific representation and minor semantic labels is constructed. Finally, an effective optimization algorithm is proposed. Comprehensive experiments on Wiki, MIRFlickr, and NUS-WIDE datasets demonstrate the superior performance of STSCH to state-of-the-art hashing approaches.

通过将不同模态的数据映射成二进制码，哈希在跨模态检索中得到了广泛应用。语义迁移旨在通过在无监督范式中将有价值的信息从一种模态迁移到另一种模态来增强异构表示的相关性。语义转移与哈希学习的结合替代了基于汉明距离的密集向量搜索，显著降低了存储需求，提高了检索效率。然而，目前的无监督机制在检索精度上表现一般，还需要语义标注的进一步改进。其中，信息融合策略的平庸性直接影响了学习到的哈希码的质量。在本文中，我们提出了一种新的半监督跨模态哈希语义转移框架，称为STSCH。首先，我们使用多个自编码器来学习每个模态的高级语义表示。为了保证异构数据的完整性，我们通过语义转移对异构数据进行整合，并分析了不同模态的特征分布。此外，在单个模态特定表示和次要语义标签之间构建了一个非对称哈希学习框架。最后，提出了一种有效的优化算法。在Wiki、MIRFlickr和NUS-WIDE数据集上的综合实验表明，STSCH比最先进的哈希方法性能优越。

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引用次数: 0

GULSTSVM: A fusion of graph information and universum learning in twin SVM 双支持向量机中图信息与全和学习的融合

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-04 DOI: 10.1016/j.inffus.2025.104114

Bharat Richhariya , M. Tanveer , Weiping Ding

In several applications, the datasets have an underlying graphical structure, and geometric information about the data is needed in the learning algorithm. Universum data serves as a useful resource for classification problems by providing prior information about the data distribution. However, the graph connectivity information embedded in the universum data has not been utilized in previous algorithms. To address this problem, a novel graph based algorithm is proposed in this work to infuse connectivity information of universum in the optimization problem of the classifier. The proposed algorithm is termed as graph based universum least squares twin support vector machine (GULSTSVM). The proposed algorithm involves manifold regularization on the universum graph to provide geometric information to the classifier. The solution of the proposed algorithm involves a system of linear equations, making it efficient in terms of training time. Moreover, to efficiently capture local and global connectivity information of universum data, a novel multi-hop connectivity method is also proposed in this work. The multi-hop approach provides a fusion of local and global graph connectivity. A concept of minimum spanning tree is presented to capture local connectivity, and feature aggregation is performed to obtain global connectivity information. Experimental results on synthetic and real-world benchmark datasets show the advantages and applicability of the proposed algorithm.

在一些应用中，数据集具有底层的图形结构，并且在学习算法中需要有关数据的几何信息。Universum数据通过提供有关数据分布的先验信息，为分类问题提供了有用的资源。然而，在以前的算法中，没有利用嵌入在universum数据中的图连接信息。为了解决这一问题，本文提出了一种新的基于图的算法，将宇宙和的连通性信息注入分类器的优化问题中。该算法被称为基于图的全和最小二乘双支持向量机（GULSTSVM）。该算法通过对全和图进行流形正则化，为分类器提供几何信息。该算法的求解涉及一个线性方程组，使其在训练时间方面效率很高。此外，为了有效地捕获universum数据的本地和全局连接信息，本文还提出了一种新的多跳连接方法。多跳方法提供了局部和全局图连接的融合。提出了最小生成树的概念来获取局部连通性，并通过特征聚合来获取全局连通性信息。在综合和实际基准数据集上的实验结果表明了该算法的优越性和适用性。

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引用次数: 0

DAK-Pose: Dual-augmentor knowledge fusion for generalizable video-based 3D human pose estimation DAK-Pose：基于广义视频的三维人体姿态估计的双增强知识融合

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-03 DOI: 10.1016/j.inffus.2025.104100

Yachuan Wang, Bin Zhang, Hao Yuan

Real-world deployment of video-based 3D human pose estimation remains challenging, as limited annotated data collected in constrained lab settings cannot fully capture the complexity of human motion. While motion synthesis for data augmentation has emerged as a mainstream solution to enhance generalization, existing synthesis methods suffer from inherent trade-offs: kinematics-based motion synthesis approaches preserve anatomical plausibility but sacrifice temporal coherence, while coordinate-based methods ensure motion smoothness but violate biomechanical constraints. This results in persistent domain gaps when synthetic data is directly used in the observation space to train pose estimation models. To overcome this, we propose DAK-Pose, which shifts augmentation to the feature space. We disentangle motion into structural and dynamic features, and design two complementary augmentors: (1) A structure-prioritized module enforces kinematic constraints for anatomical validity, and (2) a dynamic-prioritized module generates diverse temporal patterns. Auxiliary encoders trained on synthetic motions generated by these augmentors transfer domain-invariant knowledge to the pose estimator through adversarial alignment. Experiments on Human3.6M, MPI-INF-3DHP, and 3DPW datasets show that DAK-Pose achieves state-of-the-art cross-dataset performance.

基于视频的3D人体姿态估计在现实世界中的部署仍然具有挑战性，因为在受限的实验室环境中收集的有限注释数据无法完全捕捉到人体运动的复杂性。虽然用于数据增强的运动合成已成为增强泛化的主流解决方案，但现有的合成方法存在固有的权衡：基于运动学的运动合成方法保留了解剖学的合理性，但牺牲了时间一致性，而基于坐标的方法确保了运动的平滑性，但违反了生物力学约束。当直接在观测空间中使用合成数据来训练姿态估计模型时，这会导致持久的域间隙。为了克服这个问题，我们提出了DAK-Pose，它将增强转移到特征空间。我们将运动分解为结构特征和动态特征，并设计了两个互补的增强器：(1)结构优先模块执行解剖学有效性的运动学约束；(2)动态优先模块生成多种时间模式。辅助编码器对这些增强量生成的合成运动进行训练，通过对抗性对齐将域不变知识传递给姿态估计器。在Human3.6M、MPI-INF-3DHP和3DPW数据集上的实验表明，DAK-Pose实现了最先进的跨数据集性能。

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引用次数: 0

A hierarchical information policy fusion framework with multimodal large language models for autonomous guidewire navigation in endovascular procedures 基于多模态大语言模型的血管内导丝自主导航分层信息策略融合框架

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-03 DOI: 10.1016/j.inffus.2025.104115

Haoyu Wang , Taylor Yiu , Serena Lee , Ka Gao , Hangling Sun , Chenyu Zhou , Anji Li , Qiangqiang Fu , Yu Wang , Bin Chen

Robotic-assisted endovascular interventions promise to transform cardiovascular therapy by improving procedural precision and minimizing cardiologists’ exposure to occupational risks. However, current systems are limited by their reliance on manual control and lack of adaptability to complex vascular anatomies. To address these challenges, we propose a novel Hierarchical Autonomous Guidewire Navigation and Delivery (HAG-ND) framework that leverages the strengths of multimodal large language models (MLLMs) and a novel reinforcement learning module inspired by Deep Q-Networks (DQNs). The high-level MLLM is trained on diverse blood vessel and guidewire scenarios from various angles and positions, enabling it to assess the suitability and timing of substance release at the target location. Within the MLLM, a parliamentary mechanism is introduced, where multiple specialized models, each focusing on a specific aspect of the vascular environment, vote on the optimal course of action. The low-level reinforcement learning module focuses on optimizing autonomous guidewire navigation to the designated target site by learning from the rich semantic understanding provided by the MLLM. Experimental evaluations demonstrate that the HAG-ND framework significantly improves the accuracy and reliability of guidewire positioning and targeted delivery compared to existing methods. By harnessing the complementary capabilities of MLLMs and novel reinforcement learning techniques in a hierarchical architecture, HAG-ND represents a significant step towards fully autonomous and adaptive robotic-assisted endovascular interventions.

机器人辅助血管内介入有望通过提高手术精度和减少心脏病专家的职业风险来改变心血管治疗。然而，目前的系统受到人工控制的限制，缺乏对复杂血管解剖结构的适应性。为了解决这些挑战，我们提出了一种新的分层自主导线导航和交付（HAG-ND）框架，该框架利用了多模态大语言模型（mllm）的优势和受深度q网络（dqn）启发的新型强化学习模块。高水平MLLM从不同角度和位置对不同的血管和导丝情景进行训练，使其能够评估目标位置物质释放的适宜性和时间。在MLLM中，引入了议会机制，其中多个专门模型，每个模型都关注血管环境的一个特定方面，对最佳行动方案进行投票。底层强化学习模块通过学习MLLM提供的丰富语义理解，优化导丝自主导航到指定目标位置。实验评估表明，与现有方法相比，HAG-ND框架显著提高了导丝定位和定向投放的准确性和可靠性。通过在分层结构中利用mllm的互补能力和新型强化学习技术，HAG-ND代表了向完全自主和自适应机器人辅助血管内干预迈出的重要一步。

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引用次数: 0