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Multiscale Graph Redefining: Correlation-Based Multiscale Graph Clustering Network for Human Motion Prediction. 多尺度图重定义:基于关联的多尺度图聚类网络用于人体运动预测。
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-21 DOI: 10.1109/tnnls.2026.3684128
Jianqi Zhong,Junyu Shi,Wenming Cao
Graph Convolutional Networks (GCNs) have exhibited considerable promise in 3-D skeleton-based human motion prediction. Based on the intuitive observation that human motion can be delineated through the physical interconnections among human joints, many previous works have designed multiscale graphs to learn the relationships and constraints between different graph scales, obtaining encouraging results for human motion prediction. However, these fixed multiscale graphs obtain new scale graphs by merging adjacent human joint information, ignoring implicit semantic information during dynamic movements. Furthermore, human joint correlations tend to vary randomly as the depth of the multiscale clustering graph increases, which contradicts the design concept of fixed multiscale graphs. To address these limitations, we explore a novel correlation-based multiscale graph clustering network (CMGC) for adaptive multiscale graph representation learning. Given a human joints graph, the goal of CMGC is first to generate more new graphs representing motion correlations adaptively at different scale levels and then selectively restore the derived graph scales to the original human joints graphs, which enables various motion features extraction. Moreover, we introduce the discrete wavelet transform (DWT) to compensate for the signal loss caused by discrete cosine transform (DCT) domain modeling from human motion. The CMGC gives rise to gratifying performances with the adaptive multiscale graph. Extensive experiments reveal that CMGC outperforms state-of-the-art methods by 11.2%, 10.1%, and 11.2% of 3-D mean per joint position error (MPJPE) on average on Human 3.6M, CMU Mocap, and 3DPW datasets, respectively. We also test the mean angle error (MAE) on Human3.6M, which is lower by 6.5% than previous methods. Our code is released at https://github.com/JunyuShi02/CMGC.
图卷积网络(GCNs)在基于三维骨骼的人体运动预测中显示出相当大的前景。基于对人体运动可以通过人体关节之间的物理联系来描绘的直观观察,许多前人的工作设计了多尺度图来学习不同图尺度之间的关系和约束,在人体运动预测方面取得了令人鼓舞的结果。然而,这些固定的多尺度图通过合并相邻的人体关节信息来获得新的尺度图,忽略了动态运动中隐含的语义信息。此外,随着多尺度聚类图深度的增加,人体关节相关性趋于随机变化,这与固定多尺度图的设计理念相矛盾。为了解决这些限制,我们探索了一种新的基于关联的多尺度图聚类网络(CMGC),用于自适应多尺度图表示学习。给定人体关节图,CMGC的目标是首先在不同尺度上自适应生成更多表示运动关联的新图,然后有选择地将导出的图尺度恢复到原始人体关节图,从而实现各种运动特征的提取。此外,我们引入了离散小波变换(DWT)来补偿离散余弦变换(DCT)对人体运动的域建模所造成的信号损失。该算法通过自适应多尺度图获得了令人满意的性能。大量实验表明,在Human 3.6M、CMU Mocap和3DPW数据集上,CMGC的平均每个关节位置误差(MPJPE)分别比最先进的方法高出11.2%、10.1%和11.2%。我们还在Human3.6M上测试了平均角度误差(MAE),比以前的方法降低了6.5%。我们的代码发布在https://github.com/JunyuShi02/CMGC。
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引用次数: 0
Distributed Inertial k -Winners-Take-All Neural Network Based on Quadratic Optimization Problems 基于二次优化问题的分布式惯性k -赢者通吃神经网络
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-20 DOI: 10.1109/tnnls.2026.3683360
Xiaohan Bo, Song Zhu, Zhen Zhang, Weiwei Luo, Shiping Wen, Chaoxu Mu
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引用次数: 0
Spectral–Spatial–Temporal Kolmogorov–Arnold Network for Hyperspectral Change Detection 高光谱变化检测的光谱-时空Kolmogorov-Arnold网络
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-20 DOI: 10.1109/tnnls.2026.3680585
Puhong Duan, Wenxuan Wang, Xudong Kang, Shutao Li
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引用次数: 0
A Deep Neural Network Optimization Framework Based on Optimal Transport Bridge Feature Selection and Sparse Representation. 基于最优传输桥特征选择和稀疏表示的深度神经网络优化框架。
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-17 DOI: 10.1109/tnnls.2026.3678220
Guipeng Lan,Shuai Xiao,Jiabao Wen,Jiachen Yang,Wen Lu,Baihua Li,Qinggang Meng,Xinbo Gao
The performance of deep neural networks (DNNs) in accomplishing tasks heavily relies on feature selection and sparse representation of high-dimensional data. Previous work has treated feature selection and sparse representation as separate mechanisms for improving DNNs performance, focusing on identifying and leveraging informative features to enhance task-specific outcomes. However, few studies have established a connection between feature selection and sparse representation. To address this gap, this article proposes an optimization framework termed informative sparse transport (IST), which integrates feature selection and sparse coding into a unified multiobjective optimization framework. Using optimal transport as a bridge, the IST framework harmonizes the relationship between feature selection and sparse representation, offering an informational advantage. In the IST framework, feature selection aims to identify an optimal subset of features to maximize mutual information or minimize redundancy, while sparse representation seeks to approximate data with the fewest possible features. Although these objectives differ, they are fundamentally complementary, as both emphasize extracting task-relevant information while eliminating redundancy. By unifying feature selection and sparse representation, the IST framework effectively mitigates challenges posed by high-dimensional data, delivering a robust solution for enhanced feature extraction and representation. We validate the IST framework on generative and classification tasks, demonstrating IST framework improves model performance through the complementary synergy of feature selection and sparse representation.
深度神经网络在完成任务时的性能很大程度上依赖于高维数据的特征选择和稀疏表示。以前的工作将特征选择和稀疏表示视为提高dnn性能的独立机制,重点是识别和利用信息特征来增强特定任务的结果。然而,很少有研究将特征选择与稀疏表示联系起来。为了解决这一差距,本文提出了一种称为信息稀疏传输(IST)的优化框架,该框架将特征选择和稀疏编码集成到一个统一的多目标优化框架中。IST框架以最优传输为桥梁,协调了特征选择和稀疏表示之间的关系,提供了信息优势。在IST框架中,特征选择旨在识别特征的最优子集,以最大化互信息或最小化冗余,而稀疏表示寻求用最少可能的特征来近似数据。尽管这些目标不同,但它们从根本上是互补的,因为它们都强调在消除冗余的同时提取与任务相关的信息。通过统一特征选择和稀疏表示,IST框架有效地缓解了高维数据带来的挑战,为增强特征提取和表示提供了一个鲁棒的解决方案。我们在生成和分类任务上验证了IST框架,证明了IST框架通过特征选择和稀疏表示的互补协同作用提高了模型性能。
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引用次数: 0
A Dual-Network Framework With Adversarial GMM Augmentation and Frequency-Mamba Fusion for Hyperspectral Target Detection. 基于对抗GMM增强和频率-曼巴融合的双网络框架高光谱目标检测。
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-16 DOI: 10.1109/tnnls.2026.3682921
Zhiru Yang,Mengmeng Zhang,Junjie Wang,Yunhao Gao,Wenzhi Liao,Wei Li
Hyperspectral target detection (HTD) involves identifying target pixels from complex backgrounds using known or inferred spectral signatures. With advances in hyperspectral imaging technology, HTD has found widespread applications in both military and civilian domains. However, it still faces challenges such as sample imbalance and spectral variability. To address these challenges, we propose a coherent pipeline that couples data, representation, and modeling. First, we develop AdvGMM, which fits a Gaussian mixture model (GMM) to high-confidence target spectra and applies adversarial reweighting against hard backgrounds to synthesize diverse, structurally constrained pseudotargets, thereby alleviating sample scarcity. Building on this, a frequency-domain adaptive fusion and Mamba-based enhanced encoder network (FAME-Net) is proposed to address the spectral variation and improve the discriminability of targets and backgrounds. FAME-Net comprises two key modules: a frequency-domain feature adaptive fusion (FDFAF) module that adaptively amplifies information-rich bands and integrates complementary frequency components while preserving the overall reflectance trend; and an efficient Mamba block that captures long-range spectral dependencies, avoids class confusion caused by similar local features, and converts the frequency-enhanced spectra into scalable, robust features. Extensive experiments on six benchmark datasets demonstrate that the proposed method outperforms state-of-the-art approaches under limited supervision, achieving superior detection robustness. The code will be available at https://github.com/Zhiru-Yang/AdvGMM-FAME-Net.
高光谱目标检测(HTD)涉及利用已知或推断的光谱特征从复杂背景中识别目标像素。随着高光谱成像技术的进步,高光谱成像技术在军事和民用领域都得到了广泛的应用。然而,它仍然面临着样品不平衡和光谱变异性等挑战。为了应对这些挑战,我们提出了一个将数据、表示和建模耦合在一起的连贯管道。首先,我们开发了AdvGMM,它将高斯混合模型(GMM)拟合到高置信度的目标光谱中,并在硬背景下应用对抗性重加权来合成多种结构约束的假目标,从而缓解样本稀缺性。在此基础上,提出了一种基于频域自适应融合和mamba的增强编码器网络(FAME-Net),以解决频谱变化问题,提高目标和背景的可分辨性。FAME-Net包括两个关键模块:频域特征自适应融合(FDFAF)模块,该模块自适应放大信息丰富的频段并集成互补频率分量,同时保持整体反射率趋势;一个有效的曼巴块捕获远程频谱依赖关系,避免由相似的局部特征引起的类混淆,并将频率增强的频谱转换为可扩展的,健壮的特征。在六个基准数据集上进行的大量实验表明,该方法在有限的监督下优于最先进的方法,实现了卓越的检测鲁棒性。代码可在https://github.com/Zhiru-Yang/AdvGMM-FAME-Net上获得。
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引用次数: 0
Disentangled Generative Graph Representation Learning 解纠缠生成图表示学习
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-16 DOI: 10.1109/tnnls.2026.3679557
Xinyue Hu, Zhibin Duan, Xinyang Liu, Yuxin Li, Bo Chen, Chaojie Wang, Yilin He, Hongwei Liu, Xuefei Cao, Mingyuan Zhou
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引用次数: 0
Adaptive Prototype-Guided Personalized Propagation for Heterophilic Graphs With Missing Data. 缺失数据的异亲图的自适应原型引导个性化传播。
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-14 DOI: 10.1109/tnnls.2026.3676197
Mengran Li,Wenbin Xing,Zelin Zang,Bo Li,Chengyang Zhang,Yong Zhang,Junzhou Chen,Ronghui Zhang,Yongfu Li,Chuan Hu,Xiaolei Ma,Zibin Zheng
Graph neural networks (GNNs) have achieved strong results on homophilic graphs with complete node attributes, yet their performance significantly deteriorates when faced with the combined challenges of heterophily and feature missingness. Heterophily introduces semantic inconsistency in neighborhoods, while feature missingness obscures node identity, which together constitute a complex problem we define as the heterophily-missing coupling (HMC). Under HMC, information exchanged between nodes becomes less reliable, and the usual assumptions that support message propagation no longer hold. To address this, we propose a novel adaptive prototype-guided personalized propagation (APP) framework. Specifically, it first leverages semantic rectification via prototypes (SRPs) to align neighborhood information with prototype semantics, reducing noise from inconsistent neighbors. Subsequently, personalized virtual propagation (PVP) builds upon this by clustering to construct prototype-aligned virtual edges, enabling effective feature imputation through minimizing Dirichlet energy across both real and virtual graphs. Finally, adaptive representation synergy (ARS) consolidates the propagated and imputed features by employing prototype-guided confidence weighting and enhancing representation quality via a contrastive training objective. Extensive experiments on multiple benchmark datasets demonstrate that APP consistently improves node classification performance on heterophilic graphs with missing features, achieving up to 11.22% improvement over state-of-the-art baselines while significantly reducing imputation error. The implementation is publicly available at https://github.com/limengran98/APP.
图神经网络(gnn)在具有完整节点属性的同亲图上取得了较好的结果,但在面对异构性和特征缺失的双重挑战时,其性能显著下降。异构性引入了邻域的语义不一致,而特征缺失又模糊了节点的身份,这些共同构成了一个复杂的问题,我们将其定义为异构-缺失耦合(HMC)。在HMC下,节点之间交换的信息变得不那么可靠,支持消息传播的通常假设不再成立。为了解决这个问题,我们提出了一种新的自适应原型引导的个性化传播(APP)框架。具体来说,它首先通过原型(srp)利用语义纠正来对齐邻居信息和原型语义,减少不一致邻居的噪声。随后,个性化虚拟传播(PVP)在此基础上通过聚类构建原型对齐的虚拟边缘,通过最小化真实和虚拟图中的狄利克雷能量来实现有效的特征输入。最后,自适应表示协同(ARS)通过使用原型引导的置信度加权和通过对比训练目标提高表示质量来巩固传播和输入的特征。在多个基准数据集上进行的大量实验表明,APP在缺失特征的异亲图上持续提高了节点分类性能,在显著降低输入误差的同时,比最先进的基线提高了11.22%。该实现可在https://github.com/limengran98/APP上公开获得。
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引用次数: 0
Causal Counterfactual Inference Network for Video Object State Changes in Open-World Scenarios. 开放世界场景下视频对象状态变化的因果反事实推理网络。
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-14 DOI: 10.1109/tnnls.2026.3678945
Zhichao Wang,Shucheng Huang,Mingxing Li,Yifan Jiao
Object state changes (OSCs) play a critical role in video understanding, as they focus on localizing the stages of state transitions within temporal sequences. However, existing methods face two key challenges in open-world scenarios. First, there is a significant background-causal scene imbalance due to dataset bias. This leads to reliance on irrelevant features and degrades prediction capability. Second, existing methods have poor generalization performance on unseen objects. They typically focus on a single state change of a specific object, which limits them to understand the state change of an unseen object in a generalized way as humans do. To address these challenges, we first introduce a structural causal model (SCM) to formally structure the OSC task, which explicitly defines the confounding effect of dataset bias and the lack of generalization. Guided by this SCM, we propose CCI-Net, a causal counterfactual inference-based video OSC neural network. CCI-Net employs a causal inference network for backdoor adjustment to effectively eliminate confounders. In addition, it integrates counterfactual inference to enhance understanding in open-world scenarios. Specifically, CCI-Net comprises two key components: the backdoor scene classifier (BSC) and the counterfactual module (CM). The BSC controls potential confounders and mitigates spurious correlations. The CM enhances generalization to unseen objects and their state changes by constructing counterfactual scenes during training. Furthermore, we design two loss functions for causal and counterfactual scenes to optimize the learning process. Experimental results on three benchmark datasets demonstrate that, compared with existing methods, CCI-Net significantly improves both precision and generalization in open-world scenarios.
对象状态变化(OSCs)在视频理解中起着至关重要的作用,因为它们专注于在时间序列中定位状态转换的阶段。然而,现有方法在开放世界场景中面临两个关键挑战。首先,由于数据集偏差,存在显著的背景因果场景不平衡。这将导致对不相关特征的依赖,并降低预测能力。其次,现有方法对不可见对象的泛化性能较差。它们通常关注特定对象的单个状态变化,这限制了它们像人类一样以广义的方式理解不可见对象的状态变化。为了解决这些挑战,我们首先引入了一个结构因果模型(SCM)来正式构建OSC任务,该模型明确定义了数据集偏差和缺乏泛化的混淆效应。在此SCM的指导下,我们提出了基于因果反事实推理的视频OSC神经网络CCI-Net。CCI-Net采用因果推理网络进行后门调整,有效消除混杂因素。此外,它还集成了反事实推理,以增强对开放世界场景的理解。具体来说,CCI-Net包括两个关键组件:后门场景分类器(BSC)和反事实模块(CM)。平衡计分卡控制潜在的混杂因素并减轻虚假相关性。该算法通过在训练过程中构造反事实场景来增强对未见对象及其状态变化的泛化。此外,我们设计了因果和反事实场景的两个损失函数来优化学习过程。在三个基准数据集上的实验结果表明,与现有方法相比,CCI-Net在开放世界场景下的精度和泛化都有显著提高。
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引用次数: 0
Attribute-Topology Cross-Frequency Aligned Graph Neural Networks for Homophilic and Heterophilic Graphs in Node Classification. 同亲和异亲图节点分类的属性-拓扑交叉频率对齐图神经网络。
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-13 DOI: 10.1109/tnnls.2026.3678135
Yachao Yang,Yanfeng Sun,Jipeng Guo,Jinlu Wang,Shaofan Wang,Junbin Gao,Fujiao Ju,Baocai Yin
Graph neural networks (GNNs) have excelled in handling graph-structured data, attracting significant research interest. However, two primary challenges have emerged: interference between topology and attributes distorting node representations, and the low-pass filtering nature of most GNNs leading to the oversight of valuable high-frequency information in graph signals. These issues are particularly pronounced in heterophilic graphs. To address these challenges, we propose attribute-topology cross-frequency aligned (ATCFA) GNNs. ATCFA combines low- and high-pass filters to capture both smooth and detailed representations from topological and attribute perspectives. It also enforces frequency-specific constraints to reduce noise and redundancy in each frequency band. The model can dynamically adjust the filtering ratios for both homophilic and heterophilic graphs. Crucially, ATCFA establishes dynamic associations between corresponding frequency components of topology and attribute, achieving systematic alignment and interactive fusion that explicitly mitigates interference and promotes complementary information utilization across domains. Extensive experiments on standard datasets show that ATCFA delivers higher classification accuracy than state-of-the-art methods, proving its capability to handle both homophilic and heterophilic graphs in node classification.
图神经网络(gnn)在处理图结构数据方面表现出色,引起了人们的极大兴趣。然而,出现了两个主要挑战:拓扑和属性之间的干扰扭曲节点表示,以及大多数gnn的低通滤波特性导致图信号中有价值的高频信息被忽视。这些问题在异性恋图中尤为明显。为了解决这些挑战,我们提出了属性拓扑交叉频率对齐(ATCFA) gnn。ATCFA结合了低通和高通滤波器,从拓扑和属性的角度捕获平滑和详细的表示。它还强制执行特定频率的约束,以减少每个频带中的噪声和冗余。该模型可以动态调整同亲图和异亲图的滤波比例。至关重要的是,ATCFA在拓扑和属性的相应频率分量之间建立了动态关联,实现了系统的对齐和交互融合,从而明确地减轻了干扰,促进了跨域的互补信息利用。在标准数据集上进行的大量实验表明,ATCFA比最先进的方法提供了更高的分类精度,证明了其在节点分类中处理同亲和异亲图的能力。
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引用次数: 0
When Optimal Transport Meets Photo-Realistic Image Dehazing With Unpaired Training. 当最优传输满足非配对训练的逼真图像去雾。
IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-04-09 DOI: 10.1109/tnnls.2026.3673760
Yuanbo Wen,Tao Gao,Shan Liang,Dena Zhang,Ziqi Li,Jing Qin,Ting Chen
Image hazing is crucial for enhancing the image visibility and mitigating the weather degradations. However, most existing approaches rely on the paired hazy and clean images, which are challenging to obtain in real-world scenarios. To this end, we propose an oriented Bayesian-regularized consistent optimal transport (OBCOT) framework, which formulates the unpaired image dehazing task as an optimal transport (OT) problem. Specifically, we introduce a structure-preserving transport cost, incorporating the structural similarity (SSIM) constraint to minimize the duality gap between the primal and dual formulations, while preserving the structural details of reconstructed images. Furthermore, we derive the Bayesian frequency-domain regularization (BFR) to balance the spectral consistency with clean References and repulsion from hazy patterns. In addition, we employ a pretrained one-step stable diffusion model as the restoration network, which is fine-tuned using the low-rank adaptation (LoRA) adapters and zero convolutional layers, while integrating the domain-specific text prompts for both degraded and clean images to guide the generation process. Extensive experiments demonstrate that our method surpasses the existing well-performing unpaired learning approaches, achieving notable improvements in both the fidelity and photo-realism.
图像雾化是提高图像能见度和减轻天气退化的关键。然而,现有的方法大多依赖于模糊和清晰图像的配对,这在现实场景中很难获得。为此,我们提出了一个定向贝叶斯正则化一致最优传输(OBCOT)框架,该框架将未配对图像去雾任务表述为最优传输(OT)问题。具体来说,我们引入了一个保持结构的传输成本,结合结构相似性(SSIM)约束来最小化原始和对偶公式之间的对偶差距,同时保留重构图像的结构细节。此外,我们推导了贝叶斯频域正则化(BFR)来平衡干净参考谱的一致性和模糊模式的斥力。此外,我们采用预训练的一步稳定扩散模型作为恢复网络,该网络使用低秩自适应(LoRA)适配器和零卷积层进行微调,同时集成针对退化和干净图像的特定领域文本提示来指导生成过程。大量的实验表明,我们的方法超越了现有的表现良好的非配对学习方法,在保真度和照片真实感方面都取得了显著的进步。
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引用次数: 0
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