Information Fusion最新文献_第10页

EPSO-net: A multi-objective evolutionary neural architecture search with PSO-guided mutation fusion for explainable brain tumor segmentation EPSO-Net：基于pso引导的突变融合的多目标进化神经结构搜索，用于可解释的脑肿瘤分割

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.2025.104119

Farhana Yasmin , Yu Xue , Mahade Hasan , Ghulam Muhammad

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) remains a significant challenge due to early loss of spatial detail, inadequate contextual representation, and ineffective decoder fusion. In this paper, we propose EPSO-Net, a multi-objective evolutionary neural architecture search (NAS) framework that integrates three specialized modules: UTSA for preserving spatial encoding and enhancing low-level feature representation, Astra for capturing semantic abstraction and multi-scale context, and Revo for improving decoder refinement through attention-guided fusion of feature maps. These modules work synergistically within a flexible modular 3D search space, enabling dynamic architecture optimization during the evolutionary process. EPSO-Net utilizes a particle swarm optimization (PSO)-guided mutation fusion mechanism that enables efficient exploration of the search space, adjusting mutation behavior based on performance feedback. To the best of our knowledge, this is the first multi-objective evolutionary NAS framework employing PSO-guided mutation fusion to adapt mutation strategies, driving the search towards optimal solutions in a resource-efficient manner. Experiments on the BraTS 2021, BraTS 2020, and MSD Brain Tumor datasets demonstrate that EPSO-Net outperforms nine state-of-the-art methods, achieving high dice similarity coefficients (DSC) of 93.89%, 95.02%, and 91.25%, low Hausdorff distance (HD95) of 1.14 mm, 1.02 mm, and 1.44 mm, and strong Grad-CAM IoU (GIoU) of 89.32%, 90.12%, and 85.68%, respectively. EPSO-Net also demonstrates reliable generalization to the CHAOS, PROMISE12, and ACDC datasets. Furthermore, it significantly reduces model complexity, lowers FLOPS, accelerates inference, and enhances interpretability. The full code will be publicly available at: https://github.com/Farhana005/EPSO-Net.

由于早期空间细节的丢失、上下文表示的不足和无效的解码器融合，从磁共振成像（MRI）中准确分割脑肿瘤仍然是一个重大挑战。在本文中，我们提出了EPSO-Net，这是一个多目标进化神经结构搜索（NAS）框架，它集成了三个专门的模块：用于保留空间编码和增强低级特征表示的UTSA，用于捕获语义抽象和多尺度上下文的Astra，以及用于通过注意引导融合特征图来改进解码器优化的Revo。这些模块在一个灵活的模块化3D搜索空间中协同工作，在进化过程中实现动态架构优化。EPSO-Net利用粒子群优化（PSO）引导的突变融合机制，能够有效地探索搜索空间，并根据性能反馈调整突变行为。据我们所知，这是第一个采用pso引导的突变融合来适应突变策略的多目标进化NAS框架，以资源高效的方式推动了对最佳解决方案的搜索。在BraTS 2021， BraTS 2020和MSD脑肿瘤数据集上的实验表明，EPSO-Net优于9种最先进的方法，实现了高dice相似系数（DSC）为93.89%，95.02%和91.25%，低Hausdorff距离（HD95）为1.14 mm， 1.02 mm和1.44 mm，高Grad-CAM IoU （GIoU）分别为89.32%，90.12%和85.68%。EPSO-Net还展示了对CHAOS， PROMISE12和ACDC数据集的可靠泛化。此外，它显著降低了模型复杂性，降低了FLOPS，加速了推理，增强了可解释性。完整的代码将在https://github.com/Farhana005/EPSO-Net上公开。

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引用次数: 0

Progressive temporal compensation and semantic enhancement for Exo-to-Ego video generation 渐进式时间补偿和语义增强的外自我视频生成

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.2025.104117

Xingyue Wang , Weipeng Hu , Jiun Tian Hoe , Jianhui Li , Ping Hu , Yap-Peng Tan

Transforming video perspectives from exocentric (third-person) to egocentric (first-person) is challenging due to limited overlap between two perspectives. Existing approaches often neglect the temporal dynamics-critical for capturing motion cues and reappearing objects-and do not fully exploit source-view inferred semantics. To address these limitations, we propose a Progressive Temporal Compensation and Semantic Enhancement (PCSE) framework for Exocentric-to-Egocentric Video Generation. The Progressive Temporal Compensation (PTC) module focuses on long-term temporal dependencies, progressively aligning exocentric temporal patterns with egocentric representations. By employing a reliance-shifting mechanism with a progression mask, PTC gradually reduces dependence on egocentric supervision, enabling more robust target-view learning. Moreover, to leverage high-level scene context, we introduce a Hierarchical Dual-channel Transformer (HDT), which jointly generates egocentric frames and their corresponding semantic layouts via dual encoder-decoder architectures with hierarchically processed transformer blocks. To further enhance structural coherence and semantic consistency, the generated semantic layouts guide frame refinement through an Uncertainty-aware Semantic Enhancement (USE) module. USE dynamically estimates uncertainty masks to locate and refine ambiguous regions, yielding more coherent and visually accurate results. Extensive experiments demonstrate that PCSE achieves leading performance among cue-free methods.

将视频视角从外部中心（第三人称）转换为自我中心（第一人称）是具有挑战性的，因为两种视角之间的重叠有限。现有的方法往往忽略了时间动态——捕捉运动线索和再现对象的关键——并且没有充分利用源-视图推断语义。为了解决这些限制，我们提出了一个渐进式时间补偿和语义增强（PCSE）框架，用于外心到自我中心的视频生成。进行性时间补偿（PTC）模块侧重于长期时间依赖性，逐步将外心时间模式与自我中心表征对齐。通过采用一种带有递进掩模的依赖转移机制，PTC逐渐减少了对自我中心监督的依赖，从而实现了更稳健的目标视图学习。此外，为了利用高级场景上下文，我们引入了分层双通道变压器（HDT），它通过分层处理变压器块的双编码器-解码器架构共同生成以自我为中心的帧及其相应的语义布局。为了进一步增强结构一致性和语义一致性，生成的语义布局通过不确定性感知语义增强（USE）模块指导框架优化。USE动态估计不确定性掩模来定位和细化模糊区域，产生更连贯和视觉上准确的结果。大量的实验表明，PCSE在无线索方法中具有领先的性能。

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引用次数: 0

FedEGL: Edge-assisted federated graph learning FedEGL：边缘辅助联邦图学习

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

Information Fusion

Pub Date : 2026-01-02 DOI: 10.1016/j.inffus.2025.104118

Haitao Wang , Aojie Luo , Wenchao Xu , Haozhao Wang , Yichen Li , Yining Qi , Rui Zhang , Ruixuan Li

Federated graph learning excels in learning graph-structured data that are distributed across multiple clients. However, the partition of graph data results in each client only possessing a subgraph, lacking its neighbor nodes, which significantly degrades accuracy. Although exchanging original nodes can address this issue, it requires interaction with a remote server, not only causing significant communication delays but also leaking data privacy. To tackle this, this paper proposes an edge-server-assisted federated graph learning approach, namely FedEGL, which aggregates and exchanges intermediate features of approximated nodes through a third-party edge server, performing cross-client feature alignment and dynamic weighted aggregation while dynamically allocating privacy budgets with adaptive differential privacy to preserve node privacy. Additionally, differential privacy is introduced to protect the privacy of approximated node features by dynamically allocating privacy budgets. Experimental results show that our method achieves accuracy close to that in centralized settings, with the classification accuracy improved by up to 8% compared to the latest baseline. This method can improve model accuracy while protecting privacy, providing an effective solution to the subgraph partitioning problem in federated graph learning.

联邦图学习擅长学习分布在多个客户机上的图结构数据。然而，图数据的分区导致每个客户端只拥有一个子图，而缺乏它的邻居节点，这大大降低了精度。虽然交换原始节点可以解决这个问题，但它需要与远程服务器进行交互，这不仅会导致严重的通信延迟，还会泄露数据隐私。为了解决这个问题，本文提出了一种边缘服务器辅助的联邦图学习方法，即FedEGL，该方法通过第三方边缘服务器聚合和交换近似节点的中间特征，进行跨客户端特征对齐和动态加权聚合，同时使用自适应差分隐私动态分配隐私预算，以保护节点隐私。此外，引入差分隐私，通过动态分配隐私预算来保护近似节点特征的隐私。实验结果表明，该方法的准确率接近集中式设置，与最新基线相比，分类准确率提高了8%。该方法在保护隐私的同时提高了模型的准确性，为联邦图学习中的子图划分问题提供了一种有效的解决方案。

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引用次数: 0

Regional defeats global: An efficient regional feature fusion via convolutional architecture for multispectral object detection 区域战胜全局：基于卷积结构的高效区域特征融合多光谱目标检测

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

Information Fusion

Pub Date : 2026-01-02 DOI: 10.1016/j.inffus.2025.104110

Zhenhao Wang, Tian Tian

Multispectral object detection continues to face significant challenges in achieving a balanced optimization between accuracy and efficiency. Most existing approaches rely heavily on global modeling, which, although capable of integrating multi-band information, incurs substantial computational overhead and fails to fully exploit the spatial correlations across spectral bands. To address this issue, this paper introduces a convolutional architecture-based region feature computation mechanism that leverages the inherent advantage of convolutional operations in preserving spatial structure, enabling spatial cues to be fully retained during feature representation learning and explicitly incorporated into multispectral feature interaction. Meanwhile, by reconstructing global attention computation into localized regional modeling, the proposed method markedly reduces computational cost while maintaining effective feature fusion, thereby facilitating a lightweight architectural design. Experimental results demonstrate that the proposed module achieves the lowest computational overhead while improving mAP@50 by 1.97% and 1.66% on the DroneVehicle and VEDAI remote-sensing datasets, respectively, compared with state-of-the-art methods. Moreover, it exhibits strong applicability on the pedestrian detection datasets FLIR and LLVIP. The code is available https://github.com/wzh326/LMFFM_CARFCOM.git.

在实现精度和效率之间的平衡优化方面，多光谱目标检测仍然面临着重大挑战。大多数现有方法严重依赖于全局建模，尽管能够集成多波段信息，但会产生大量的计算开销，并且不能充分利用光谱波段间的空间相关性。为了解决这一问题，本文引入了一种基于卷积架构的区域特征计算机制，该机制利用卷积运算在保留空间结构方面的固有优势，使空间线索在特征表示学习过程中得到充分保留，并明确地融入到多光谱特征交互中。同时，该方法通过将全局注意力计算重构为局部区域建模，在保持有效特征融合的同时显著降低了计算成本，从而实现了轻量化的建筑设计。实验结果表明，与现有方法相比，该模块在无人机和VEDAI遥感数据集上分别提高了1.97%和1.66%的mAP@50，实现了最低的计算开销。此外，该方法在行人检测数据集FLIR和LLVIP上具有较强的适用性。代码可以在https://github.com/wzh326/LMFFM_CARFCOM.git上找到。

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引用次数: 0

Adversarial and generative AI-based anti-forensics in audio-visual deepfake detection: A comprehensive review and analysis 视听深度伪造检测中基于对抗和生成人工智能的反取证：综合回顾和分析

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

Information Fusion

Pub Date : 2026-01-01 DOI: 10.1016/j.inffus.2025.104120

Qurat Ul Ain , Fatima Khalid , Hafsa Ilyas , Ali Javed , Khalid Mahmood Malik , Khan Muhammad , Aun Irtaza

As the technology behind deepfakes advances, detecting audio-visual deepfakes becomes more and more crucial, and the rise of traditional and generative AI-based adversarial/anti-forensics attacks and generative AI-based anti-forensics attacks on deepfake detection technologies is a growing concern. Securing applications against adversarial and generative AI-based attacks is critical for accurate and robust deepfake detection tools. Therefore, this paper provides a comprehensive overview of various adversarial and generative AI-based anti-forensic attacks, which represent one of the core elements of trustworthiness alongside transparency, explainability, and fairness, as well as defensive countermeasures for audio-visual deepfake generation and detection. It covers topics such as adversarial attacks on deepfake detection algorithms and defensive methods, including model fusion and decoy-based approaches, to mitigate these threats. Although extensive research has been conducted in recent years on adversarial attacks and defense on deepfake detection, there have been few attempts to compare existing work qualitatively and quantitatively. This paper aims to help identify and address key issues that need to be considered to bring transferable adversarial attacks and their countermeasures particularly through techniques such as generative defense, knowledge distillation, and beyond.

随着深度伪造背后技术的进步，检测视听深度伪造变得越来越重要，传统的和基于生成人工智能的对抗/反取证攻击以及基于生成人工智能的反取证攻击对深度伪造检测技术的兴起越来越受到关注。保护应用程序免受对抗性和基于生成人工智能的攻击对于准确和强大的深度伪造检测工具至关重要。因此，本文全面概述了各种基于对抗性和生成性人工智能的反取证攻击，这些攻击代表了可信度的核心要素之一，以及透明度、可解释性和公平性，以及用于视听深度伪造生成和检测的防御对策。它涵盖了诸如深度假检测算法和防御方法的对抗性攻击等主题，包括模型融合和基于诱饵的方法，以减轻这些威胁。尽管近年来对深度伪造检测的对抗性攻击和防御进行了广泛的研究，但很少有人尝试对现有工作进行定性和定量的比较。本文旨在帮助识别和解决需要考虑的关键问题，以带来可转移的对抗性攻击及其对策，特别是通过诸如生成防御，知识蒸馏等技术。

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引用次数: 0

Subgraph-focused biomedical knowledge embedding with bi-semantic encoder for multi-type drug-drug interaction prediction 基于双语义编码器的生物医学知识嵌入多类型药物-药物相互作用预测

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

Information Fusion

Pub Date : 2025-12-31 DOI: 10.1016/j.inffus.2025.104109

Xiangpeng Bi , Wenjian Ma , Huasen Jiang , Qing Cai , Jie Nie , Zhiqiang Wei , Shugang Zhang

Identifying multi-type drug-drug interactions (DDIs) enables more precise assessment of drug safety risks and provides targeted guidance for combination therapy, making it a critical task in pharmacology. Given it can directly integrate diverse biomedical information and effectively model the intricate mechanisms underlying drug interaction, knowledge graph (KG)-based approaches have emerged for predicting DDIs. Recent advances have shown great promise in this regard; however, existing solutions still overlook three critical issues: 1) neglect of information sparsity, 2) neglect of polyadic interactions, and 3) lack of fusion paradigm, which severely hinder the comprehensive identification and understanding of drug interaction patterns. To address these issues, we introduce a Bi-Semantic encoDer-dRiven knowledge sUbGraph representation learning framework (Bi-SemDRUG) for multi-type DDI prediction. Bi-SemDRUG proposes a multi-view knowledge subgraph partitioning strategy to extract drug-related refined topological structures from large-scale knowledge graphs, thereby reducing the interference of irrelevant information. Furthermore, Bi-SemDRUG incorporates a bi-semantic subgraph encoder that effectively uncovers multi-order semantic relationships embedded within the knowledge subgraphs. Finally, we propose a general paradigm for information fusion to facilitate the integration of multi-level drug-related information. Exhaustive experiments on three benchmark datasets demonstrate that our proposed model achieves state-of-the-art performance compared to other baseline methods and exhibits good generalization in large-scale DDI prediction. Additionally, case studies emphasize its capacity to offer a more comprehensive insight into the underlying mechanisms of DDIs.

多类型药物相互作用（ddi）的识别可以更精确地评估药物安全风险，为联合治疗提供有针对性的指导，是药理学中的一项重要任务。基于知识图（knowledge graph， KG）的ddi预测方法可以直接整合多种生物医学信息，有效地模拟药物相互作用的复杂机制。最近的进展在这方面显示出很大的希望；然而，现有的解决方案仍然忽视了三个关键问题：1)忽视信息稀疏性；2)忽视多元相互作用；3)缺乏融合范式，严重阻碍了对药物相互作用模式的全面识别和理解。为了解决这些问题，我们引入了一个用于多类型DDI预测的双语义编码器驱动的知识子图表示学习框架（Bi-SemDRUG）。Bi-SemDRUG提出了一种多视图知识子图划分策略，从大规模知识图中提取与药物相关的精细拓扑结构，从而减少不相关信息的干扰。此外，Bi-SemDRUG集成了一个双语义子图编码器，可以有效地揭示嵌入在知识子图中的多阶语义关系。最后，我们提出了一种通用的信息融合范式，以促进多层次药物相关信息的整合。在三个基准数据集上的详尽实验表明，与其他基准方法相比，我们提出的模型达到了最先进的性能，并且在大规模DDI预测中表现出良好的泛化。此外，案例研究强调其提供对ddi潜在机制的更全面洞察的能力。

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引用次数: 0

Generating vision-language navigation instructions incorporated fine-grained alignment annotations 生成包含细粒度对齐注释的视觉语言导航指令

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

Information Fusion

Pub Date : 2025-12-30 DOI: 10.1016/j.inffus.2025.104107

Yibo Cui , Liang Xie , Yu Zhao , Jiawei Sun , Erwei Yin

Vision-Language Navigation (VLN) enables intelligent agents to navigate environments by integrating visual perception and natural language instructions, yet faces significant challenges due to the scarcity of fine-grained cross-modal alignment annotations. Existing datasets primarily focus on global instruction-trajectory matching, neglecting sub-instruction-level and entity-level alignments critical for accurate navigation action decision-making. To address this limitation, we propose FCA-NIG, a generative framework that automatically constructs navigation instructions with dual-level fine-grained cross-modal annotations. In this framework, an augmented trajectory is first divided into sub-trajectories, which are then processed through GLIP-based landmark detection, crafted instruction construction, OFA-Speaker based R2R-like instruction generation, and CLIP-powered entity selection, generating sub-instruction-trajectory pairs with entity-landmark annotations. Finally, these sub-pairs are aggregated to form a complete instruction-trajectory pair. The framework generates the FCA-R2R dataset, the first large-scale augmentation dataset featuring precise sub-instruction-sub-trajectory and entity-landmark alignments. Extensive experiments demonstrate that training with FCA-R2R significantly improves the performance of multiple state-of-the-art VLN agents, including SF, EnvDrop, RecBERT, HAMT, DUET, and BEVBERT. Incorporating sub-instruction-trajectory alignment enhances agents’ state awareness and decision accuracy, while entity-landmark alignment further boosts navigation performance and generalization. These results highlight the effectiveness of FCA-NIG in generating high-quality, scalable training data without manual annotation, advancing fine-grained cross-modal learning in complex navigation tasks.

视觉语言导航（VLN）使智能代理能够通过集成视觉感知和自然语言指令来导航环境，但由于缺乏细粒度跨模态对齐注释，因此面临重大挑战。现有数据集主要关注全局指令-轨迹匹配，而忽略了子指令级和实体级对齐，这对精确的导航行动决策至关重要。为了解决这一限制，我们提出了FCA-NIG，这是一个生成框架，可以自动构建带有双级细粒度跨模态注释的导航指令。在该框架中，首先将增强轨迹划分为子轨迹，然后通过基于glip的地标检测、精心设计的指令构建、基于OFA-Speaker的类r2r指令生成和基于clip的实体选择对子轨迹进行处理，生成带有实体地标注释的子指令轨迹对。最后，将这些子对聚合成一个完整的指令轨迹对。该框架生成FCA-R2R数据集，这是第一个具有精确子指令-子轨迹和实体-地标对齐的大规模增强数据集。大量实验表明，使用FCA-R2R进行训练可以显著提高多个最先进的VLN智能体的性能，包括SF、EnvDrop、RecBERT、HAMT、DUET和BEVBERT。子指令-轨迹对齐提高了智能体的状态感知和决策精度，而实体-地标对齐进一步提高了导航性能和泛化。这些结果突出了FCA-NIG在生成高质量、可扩展的训练数据方面的有效性，无需人工注释，推进了复杂导航任务中细粒度跨模态学习。

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引用次数: 0

Bridging the sim-to-real gap in RF localization with large-scale synthetic pretraining 用大规模合成预训练弥合射频定位的模拟与真实差距

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

Information Fusion

Pub Date : 2025-12-30 DOI: 10.1016/j.inffus.2025.104104

Armen Manukyan , Rafayel Mkrtchyan , Ararat Saribekyan , Theofanis P. Raptis , Hrant Khachatrian

Radio frequency (RF) fingerprinting is a promising localization technique for GPS-denied environments, yet it tends to suffer from a fundamental limitation: Poor generalization to previously unmapped areas. Traditional methods such as k-nearest neighbors (k-NN) perform well where data is available but may fail on unseen streets, limiting real-world deployment. Deep learning (DL) offers potential remedies by learning spatial-RF patterns that generalize, but requires far more training data than what simple real-world measurement campaigns can provide. In this paper, we investigate whether synthetic data can bridge this generalization gap. Using (i) a real-world dataset from Rome and (ii) NVIDIA’s open-source ray-tracing simulator Sionna, we generate synthetic datasets under varying realism and scale conditions. Specifically, we use Dataset A containing real-world measurements with real base stations (BS) and real signals, and create Dataset B using real BS locations but simulated signals, Dataset C with both simulated BS locations and signals, and Dataset B’ which represents an optimized version of Dataset B where BS parameters are calibrated via Gaussian Process to maximize signal correlation with Dataset A. Our evaluation reveals a pronounced sim-to-real gap: Models achieving 25m error on synthetic data degrade to 184m on real data. Nonetheless, pretraining on synthetic data reduces real-world localization error from 323m to 162m; a 50% improvement over real-only training. Notably, simulation fidelity proves more important than scale: A smaller calibrated dataset (53K samples) outperforms a larger uncalibrated one (274K samples). To further evaluate the generalization capabilities of the models, we conduct experiments on an unseen geographical region using a real-world dataset from Oslo. In the zero-shot setting, the models achieve a root mean square error (RMSE) of 132.2m on the entire dataset, and 61.5m on unseen streets after fine-tuning on Oslo data. While challenges remain before meeting more practical localization accuracy, this work provides a systematic study in the field of wireless communication of synthetic-to-real transfer in RF localization and highlights the value of simulation-aware pretraining for generalizing DL models to real-world scenarios.

射频（RF）指纹识别是一种很有前途的定位技术，适用于没有gps的环境，但它往往有一个基本的局限性：对以前未映射的区域的泛化能力差。传统的方法，如k近邻（k-NN）在数据可用的情况下表现良好，但在未知的街道上可能会失败，限制了现实世界的部署。深度学习（DL）通过学习泛化的空间射频模式提供了潜在的补救措施，但需要更多的训练数据，而不是简单的现实世界测量活动所能提供的。在本文中，我们研究了合成数据是否可以弥补这一泛化差距。使用(i)来自罗马的真实世界数据集和（ii） NVIDIA的开源光线追踪模拟器Sionna，我们在不同的现实主义和规模条件下生成合成数据集。具体来说，我们使用包含真实基站（BS）和真实信号的真实测量数据集A，并使用真实BS位置但模拟信号创建数据集B，使用模拟BS位置和信号创建数据集C，以及代表数据集B的优化版本的数据集B，其中BS参数通过高斯过程校准以最大化与数据集A的信号相关性。我们的评估揭示了明显的模拟与真实差距：在合成数据上达到25m误差的模型在真实数据上降低到184m。尽管如此，对合成数据的预训练将真实世界的定位误差从323m减少到162m；比真实训练提高了50%。值得注意的是，模拟保真度比规模更重要：较小的校准数据集（53K样本）优于较大的未校准数据集（274K样本）。为了进一步评估模型的泛化能力，我们使用来自奥斯陆的真实数据集在一个未知的地理区域进行了实验。在零射击设置下，模型在整个数据集上的均方根误差（RMSE）为132.2m，在对奥斯陆数据进行微调后，在看不见的街道上的均方根误差（RMSE）为61.5m。虽然在满足更实际的定位精度之前仍然存在挑战，但这项工作提供了射频定位中合成到真实传输的无线通信领域的系统研究，并强调了模拟感知预训练对将DL模型推广到现实场景的价值。

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引用次数: 0

Dimensional compensation for small-sample and small-size insulator burn mark via RGB-point cloud fusion in power grid inspection 基于rgb点云融合的电网检测中小样本小尺寸绝缘子烧痕尺寸补偿

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

Information Fusion

Pub Date : 2025-12-30 DOI: 10.1016/j.inffus.2025.104105

Junqiu Tang , Zhikang Yuan , Zixiang Wei , Shuojie Gao , Changyong Shen

To address the challenge of scarce burn mark samples in power infrastructure inspection, we introduce the Insulator Burn Mark RGB-Point Cloud (IBMR) dataset, the first publicly available benchmark featuring RGB-point clouds with pixel-level annotations for both insulators and burn marks. To tackle the critical issue of severe class imbalance caused by the vast number of background points and the small size of burn marks, we propose a novel two-stage RGB-point cloud segmentation framework. This framework integrates DCCU-Sampling, an innovative downsampling algorithm that effectively suppresses background points while preserving critical structures of the targets, and BB-Backtracking, a geometric recovery method that reconstructs fine-grained burn mark details lost during downsampling process. Experimental results validate the framework’s effectiveness, achieving 81.21% mIoU with 32 training samples and 68.37% mIoU with only 14 samples. The dataset is publicly available at https://huggingface.co/datasets/Junqiu-Tang/IBMR.

为了解决电力基础设施检测中烧痕样本稀缺的挑战，我们引入了绝缘子烧痕rgb点云（IBMR）数据集，这是第一个公开可用的基准测试，该数据集具有针对绝缘子和烧痕的像素级注释的rgb点云。为了解决大量背景点和小尺寸烧伤痕迹导致的严重类别不平衡的关键问题，我们提出了一种新的两阶段rgb -点云分割框架。该框架集成了DCCU-Sampling和BB-Backtracking， DCCU-Sampling是一种创新的下采样算法，可以有效地抑制背景点，同时保留目标的关键结构；BB-Backtracking是一种几何恢复方法，可以重建下采样过程中丢失的细粒度烧伤痕迹细节。实验结果验证了该框架的有效性，32个训练样本的mIoU达到81.21%，14个样本的mIoU达到68.37%。该数据集可在https://huggingface.co/datasets/Junqiu-Tang/IBMR上公开获取。

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引用次数: 0

Style-augmented large-scale vision model with domain-generalized knowledge fusion for anomaly detection in powder bed additive manufacturing 基于领域广义知识融合的风格增强大尺度视觉模型用于粉末床增材制造异常检测

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

Information Fusion

Pub Date : 2025-12-30 DOI: 10.1016/j.inffus.2025.104108

Kang Wang , Xuan Liang , Jinghua Xu , Shuyou Zhang , Jianrong Tan

Metal Additive Manufacturing (AM) has revolutionized the production of complex parts across various industries, yet ensuring consistent quality remains a significant challenge. This study addresses the critical problem of reliable and efficient anomaly detection in metal AM processes, which is essential for maintaining product quality and reducing costly post-production inspections. In this study, we propose a novel full-life-cycle generalization method, namely style-augmented large-scale vision model (SLVM), for anomaly detection in metal additive manufacturing. Our approach leverages the power of large-scale vision models and incorporates style-based augmentation techniques to enhance the detection of anomalies in AM processes. A pre-trained large-scale vision model serves as the backbone in SLVM, providing robust feature extraction capabilities essential for capturing intricate details in AM images. Building upon this foundation, a style augmentation module generates diverse stylized versions of input images, significantly improving the model’s generalization across different AM processes and materials. An anomaly detection head utilizes these style-augmented features to effectively identify and localize defects, completing the comprehensive approach to AM quality control. We evaluate our SLVM on multiple metal AM datasets, including laser powder bed fusion and binder jetting processes, demonstrating its superior performance compared to existing state-of-the-art methods. Our experiments show that SLVM achieves higher detection accuracy, better generalization across different AM processes, and improved robustness to variations in part geometry and material properties. The proposed SLVM exhibits a promising solution for enhancing quality control in metal AM, potentially reducing the need for costly post-production inspections and improving overall manufacturing efficiency.

金属增材制造（AM）已经彻底改变了各个行业复杂零件的生产，但确保始终如一的质量仍然是一个重大挑战。本研究解决了金属增材制造过程中可靠和高效的异常检测的关键问题，这对于保持产品质量和减少昂贵的生产后检查至关重要。在本研究中，我们提出了一种新的全生命周期泛化方法，即风格增强大尺度视觉模型（SLVM），用于金属增材制造中的异常检测。我们的方法利用了大规模视觉模型的力量，并结合了基于风格的增强技术，以增强对增材制造过程中异常的检测。预训练的大规模视觉模型作为SLVM的主干，为捕获AM图像中的复杂细节提供了强大的特征提取功能。在此基础上，风格增强模块生成输入图像的不同风格版本，显着提高了模型在不同AM工艺和材料中的泛化性。异常检测头利用这些样式增强功能有效地识别和定位缺陷，完成AM质量控制的综合方法。我们在多个金属增材制造数据集上评估了我们的SLVM，包括激光粉末床熔化和粘合剂喷射工艺，与现有的最先进方法相比，证明了它的优越性能。我们的实验表明，SLVM实现了更高的检测精度，更好的跨不同增材制造工艺的泛化，并且提高了对零件几何形状和材料性能变化的鲁棒性。提出的SLVM展示了一个有前途的解决方案，可以加强金属AM的质量控制，潜在地减少昂贵的生产后检查的需要，提高整体制造效率。

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