Neural Networks最新文献_第10页

Matrix-Transformation based Low-Rank Adaptation (MTLoRA): A brain-Inspired method for parameter-Efficient fine-Tuning 基于矩阵变换的低秩自适应（MTLoRA）：一种基于大脑的参数高效微调方法

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-25 DOI: 10.1016/j.neunet.2026.108642

Yao Liang , Yuwei Wang , Yang Li , Yi Zeng

Parameter-efficient fine-tuning (PEFT) reduces the compute and memory demands of adapting large language models, yet standard low-rank adapters (e.g., LoRA) can lag full fine-tuning in performance and stability because they restrict updates to a fixed rank-r subspace. We propose Matrix-Transformation based Low-Rank Adaptation (MTLoRA), a brain-inspired extension that inserts a learnable r × r transformation T into the low-rank update (

Δ W = B T A

). By endowing the subspace with data-adapted geometry (e.g., rotations, scalings, and shears), MTLoRA reparameterizes the rank-r hypothesis class, improving its conditioning and inductive bias at negligible O(r²) overhead, and recovers LoRA when

T = I_{r}

. We instantiate four structures for T—SHIM

(T = C)

, ICFM

(T = C C^{⊤})

, CTCM

(T = C D)

, and DTSM

(T = C + D)

—providing complementary inductive biases (change of basis, PSD metric, staged mixing, dual superposition). An optimization analysis shows that T acts as a learned preconditioner within the subspace, yielding spectral-norm step-size bounds and operator-norm variance contraction that stabilize training. Empirically, MTLoRA delivers consistent gains while preserving PEFT efficiency: on GLUE (General Language Understanding Evaluation) with DeBERTaV3-base, MTLoRA improves the average over LoRA by (+2.0) points (86.9 → 88.9) and matches AdaLoRA (88.9) without any pruning schedule; on natural language generation with GPT-2 Medium, it raises BLEU on DART by (+0.95) and on WebNLG by (+0.56); and in multimodal instruction tuning with LLaVA-1.5-7B, DTSM attains the best average (69.91) with  ∼ 4.7% trainable parameters, outperforming full fine-tuning and strong PEFT baselines. These results indicate that learning geometry inside the low-rank subspace improves both effectiveness and stability, making MTLoRA a practical, plug-compatible alternative to LoRA for large-model fine-tuning.

参数有效的微调（PEFT）减少了适应大型语言模型的计算和内存需求，但是标准的低级别适配器（例如，LoRA）可能会在性能和稳定性方面滞后于完全的微调，因为它们将更新限制在固定的rank-r子空间。我们提出了基于矩阵变换的低秩自适应（MTLoRA），这是一种基于大脑的扩展，它将可学习r × r变换T插入到低秩更新（ΔW=BTA）中。通过赋予子空间数据适应几何（例如，旋转、缩放和剪切），MTLoRA重新参数化了rank-r假设类，在可忽略的O（r2）开销下改善了其条件和归纳偏差，并在T=Ir时恢复了LoRA。我们实例化了T - shim （T=C）、ICFM （T=CC）、CTCM （T=CD）和DTSM （T=C+D）的四种结构，提供了互补的归纳偏置（基的变化、PSD度量、阶段混合、对偶叠加）。优化分析表明，T作为子空间内的学习预条件，产生谱范数步长边界和算子范数方差收缩，稳定训练。从经验上看，MTLoRA在保持PEFT效率的同时提供了一致的收益：在带有debertav3碱基的GLUE（通用语言理解评估）上，MTLoRA在没有任何修剪计划的情况下，将LoRA的平均值提高了（+2.0）分（86.9 → 88.9），与AdaLoRA（88.9）相匹配；在使用GPT-2 Medium生成自然语言时，DART和WebNLG的BLEU分别提高了+0.95和+0.56；在使用llva -1.5- 7b进行多模态指令调谐时，DTSM在 ~ 4.7%可训练参数下达到最佳平均值（69.91），优于完全微调和强PEFT基线。这些结果表明，在低秩子空间内学习几何结构提高了有效性和稳定性，使MTLoRA成为一种实用的、可插入兼容的大模型微调替代LoRA。

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引用次数: 0

Benchmarking autoregressive conditional diffusion models for turbulent flow simulation 湍流模拟的基准自回归条件扩散模型。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-24 DOI: 10.1016/j.neunet.2026.108641

Georg Kohl, Li-Wei Chen, Nils Thuerey

Simulating turbulent flows is crucial for a wide range of applications, and machine learning-based solvers are gaining increasing relevance. However, achieving temporal stability when generalizing to longer rollout horizons remains a persistent challenge for learned PDE solvers. In this work, we analyze if fully data-driven fluid solvers that utilize an autoregressive rollout based on conditional diffusion models are a viable option to address this challenge. We investigate accuracy, posterior sampling, spectral behavior, and temporal stability, while requiring that methods generalize to flow parameters beyond the training regime. To quantitatively and qualitatively benchmark the performance of various flow prediction approaches, three challenging 2D scenarios including incompressible and transonic flows, as well as isotropic turbulence are employed. We find that even simple diffusion-based approaches can outperform multiple established flow prediction methods in terms of accuracy and temporal stability, while being on par with state-of-the-art stabilization techniques like unrolling at training time. Such traditional architectures are superior in terms of inference speed, however, the probabilistic nature of diffusion approaches allows for inferring multiple predictions that align with the statistics of the underlying physics. Overall, our benchmark contains three carefully chosen data sets that are suitable for probabilistic evaluation alongside various established flow prediction architectures.

模拟湍流对于广泛的应用是至关重要的，基于机器学习的求解器正在获得越来越多的相关性。然而，当泛化到更长的推出范围时，实现时间稳定性仍然是学习PDE求解器的一个持续挑战。在这项工作中，我们分析了利用基于条件扩散模型的自回归推出的完全数据驱动的流体求解器是否是解决这一挑战的可行选择。我们研究了准确性、后验采样、光谱行为和时间稳定性，同时要求方法推广到超出训练范围的流量参数。为了定量和定性地对各种流动预测方法的性能进行基准测试，采用了三种具有挑战性的二维场景，包括不可压缩和跨音速流动，以及各向同性湍流。我们发现，即使是简单的基于扩散的方法，在准确性和时间稳定性方面也可以优于多种已建立的流量预测方法，同时与最先进的稳定技术（如在训练时展开）相当。这样的传统架构在推理速度方面是优越的，然而，扩散方法的概率性质允许推断与底层物理统计相一致的多个预测。总的来说，我们的基准包含三个精心挑选的数据集，它们适合于概率评估以及各种已建立的流量预测架构。

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引用次数: 0

UniSymNet: A Unified Symbolic Network with Sparse Encoding and Bi-level Optimization 具有稀疏编码和双级优化的统一符号网络。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-22 DOI: 10.1016/j.neunet.2026.108615

Xinxin Li , Juan Zhang , Da Li , Xingyu Liu , Jin Xu , Junping Yin

Automatically discovering mathematical expressions is a challenging issue to precisely depict natural phenomena, in which Symbolic Regression (SR) is one of the most widely utilized techniques. Mainstream SR algorithms target on searching for the optimal symbolic tree, but the increasing complexity of the tree structure often limits their performance. Inspired by neural networks, symbolic networks have emerged as a promising new paradigm. However, existing symbolic networks still face certain challenges: binary nonlinear operators { × , ÷} cannot be naturally extended to multivariate, training with fixed architecture often leads to higher complexity and overfitting. In this work, we propose a Unified Symbolic Network that unifies nonlinear binary operators into nested unary operators, thereby transforming them into multivariate operators. The capability of the proposed UniSymNet is deduced from rigorous theoretical proof, resulting in lower complexity and stronger expressivity. Unlike the conventional neural network training, we design a bi-level optimization framework: the outer level pre-trains a Transformer with sparse label encoding scheme to guide UniSymNet structure selection, while the inner level employs objective-specific strategies to optimize network parameters. This allows for flexible adaptation of UniSymNet structures to different data, leading to reduced expression complexity. The UniSymNet is evaluated on low-dimensional Standard Benchmarks and high-dimensional SRBench, and shows excellent symbolic solution rate, high fitting accuracy, and relatively low expression complexity.

自动发现数学表达式是精确描述自然现象的一个具有挑战性的问题，其中符号回归（SR）是应用最广泛的技术之一。主流SR算法的目标是寻找最优的符号树，但树结构的复杂性往往限制了它们的性能。受神经网络的启发，符号网络已经成为一种很有前途的新范式。然而，现有的符号网络仍然面临着一定的挑战：二元非线性算子{ × ，÷}不能自然地扩展到多元，固定架构的训练往往会导致更高的复杂性和过拟合。在这项工作中，我们提出了一个统一的符号网络，将非线性二进制算子统一为嵌套的一元算子，从而将它们转换为多元算子。通过严格的理论论证，推导出了该网络的性能，具有较低的复杂度和较强的表达能力。与传统的神经网络训练不同，我们设计了一个双层优化框架：外层预训练一个具有稀疏标签编码方案的Transformer来指导UniSymNet的结构选择，而内层采用特定目标的策略来优化网络参数。这允许灵活地调整UniSymNet结构以适应不同的数据，从而降低表达式的复杂性。在低维的Standard benchmark和高维的SRBench上对UniSymNet进行了评估，结果表明该算法具有优异的符号解算率、较高的拟合精度和相对较低的表达式复杂度。

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引用次数: 0

Exploring financial sentiment analysis via fine-tuning large language model and attributed graph neural network 通过微调大语言模型和属性图神经网络探索金融情绪分析。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-22 DOI: 10.1016/j.neunet.2026.108620

Zongshen Mu , Yujie Wan , Yueting Zhuang , Jie Tan , Hong Cheng , Yueyang Wang

Financial sentiment analysis (FSA) refers to the task of classifying textual content into predefined sentiment categories to analyze their potential impacts on financial market fluctuations. However, directly applying these pre-trained LLMs to FSA still poses significant challenges. Existing approaches fail to align with domain-specific objectives and struggle to adapt to customized financial data schemas. Moreover, these LLMs predict the stock change primarily depending on its own information, failing to take into account cross-impact among relevant stocks. In this paper, we propose a novel framework that synergizes an LLM with a Graph Neural Network (GNN) to model stock price dynamics, leveraging stock sentiment signals extracted from financial news. Specifically, we employ the open-source Llama-3-8B model as the backbone, then enhance its sensitivity to financial sentiment patterns through supervised fine-tuning (SFT) and direct preference optimization (DPO) techniques. Leveraging the sentiment outputs from the fine-tuned LLM, we design a GNN to enhance stock representations and model cross-asset dependencies via two types of text-attributed graphs, which dynamically encode time-varying price correlations. Experiments on the Chinese A-share market demonstrate that financial sentiment significantly influences stock price variations. Our framework outperforms previous baselines and exhibits an average improvement of 50% in Sharpe ratio.

金融情绪分析（Financial sentiment analysis， FSA）是指将文本内容分类到预定义的情绪类别中，分析其对金融市场波动的潜在影响。然而，将这些预先训练的法学硕士直接应用于金融服务管理局仍然面临着重大挑战。现有的方法不能与特定于领域的目标保持一致，并且难以适应定制的财务数据模式。而且，这些llm预测股票变动主要依靠自身的信息，没有考虑相关股票之间的交叉影响。在本文中，我们提出了一个新的框架，将LLM与图神经网络（GNN）协同作用，利用从金融新闻中提取的股票情绪信号来模拟股票价格动态。具体而言，我们采用开源的lama-3- 8b模型作为主干，然后通过监督微调（SFT）和直接偏好优化（DPO）技术增强其对金融情绪模式的敏感性。利用微调LLM的情感输出，我们设计了一个GNN来增强股票表示，并通过两种类型的文本属性图来建模跨资产依赖关系，这两种类型的文本属性图动态编码时变价格相关性。对中国a股市场的实验表明，金融情绪显著影响股价波动。我们的框架优于以前的基准，夏普比率平均提高了50%。

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引用次数: 0

Mitigating sensitive information leakage in LLMs4Code through machine unlearning 通过机器学习减少LLMs4Code中的敏感信息泄漏

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-22 DOI: 10.1016/j.neunet.2026.108606

Shanzhi Gu , Zhaoyang Qu , Ruotong Geng , Mingyang Geng , Shangwen Wang , Chuanfu Xu , Haotian Wang , Zhipeng Lin , Dezun Dong

Large Language Models for Code (LLMs4Code) have achieved strong performance in code generation, but recent studies reveal that they may memorize and leak sensitive information contained in training data, posing serious privacy risks. To address this gap, this work presents the first comprehensive empirical study on applying machine unlearning to mitigate sensitive information leakage in LLMs4Code. We first construct a dedicated benchmark that includes: (i) a synthetic forget set containing diverse forms of personal information, and (ii) a retain set designed to evaluate whether code-generation capability is preserved after unlearning. Using this benchmark, we systematically assess three representative unlearning algorithms (GA, GA+GD, GA+KL) across three widely used open-source LLMs4Code models (AIXCoder-7B, CodeLlama-7B, CodeQwen-7B). Experimental results demonstrate that machine unlearning can substantially reduce direct memorization-based leakage: on average, the direct leak rate drops by more than 50% while retaining about over 91% of the original code-generation performance. Moreover, by analyzing post-unlearning outputs, we uncover a consistent shift from direct to indirect leakage, revealing an underexplored vulnerability that persists even when the target data has been successfully forgotten. Our findings show that machine unlearning is a feasible and effective solution for enhancing privacy protection in LLMs4Code, while also highlighting the need for future techniques capable of mitigating both direct and indirect leakage simultaneously.

大型代码语言模型（Large Language Models for Code, LLMs4Code）在代码生成方面取得了较强的性能，但最近的研究表明，它们可能会记忆和泄露训练数据中包含的敏感信息，带来严重的隐私风险。为了解决这一差距，这项工作提出了第一个应用机器学习来减轻LLMs4Code中敏感信息泄漏的综合实证研究。我们首先构建了一个专用基准，其中包括：(i)包含多种形式个人信息的合成遗忘集，以及（ii）用于评估遗忘后是否保留代码生成能力的保留集。利用这一基准，我们系统地评估了三种具有代表性的学习算法（GA, GA+GD, GA+KL），涵盖了三种广泛使用的开源LLMs4Code模型（aixcode - 7b, CodeLlama-7B, CodeQwen-7B）。实验结果表明，机器学习可以大大减少基于直接记忆的泄漏：平均而言，直接泄漏率下降了50%以上，同时保留了91%以上的原始代码生成性能。此外，通过分析学习后的输出，我们发现了从直接泄漏到间接泄漏的一致转变，揭示了一个未被充分探索的漏洞，即使目标数据已经被成功遗忘，这个漏洞仍然存在。我们的研究结果表明，机器学习是增强LLMs4Code中隐私保护的可行且有效的解决方案，同时也强调了对未来能够同时减轻直接和间接泄漏的技术的需求。

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引用次数: 0

Lifelong knowledge graph embedding via diffusion model 基于扩散模型的终身知识图谱嵌入

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-21 DOI: 10.1016/j.neunet.2026.108630

Deyu Chen , Caicai Guo , Qiyuan Li , Jinguang Gu , Meiyi Xie , Hong Zhu

Lifelong knowledge graph embedding (KGE) methods aim to learn new knowledge continuously while retaining old knowledge. This line of work has received much attention for its potential to enable knowledge retention and transfer and to reduce training costs under knowledge graphs’ growing scale and flexibility. However, embedding space drift under different contexts is a crucial reason for catastrophic forgetting and inefficient learning of new facts, and existing work ignores this perspective. In order to address the above issues, we proposed a novel lifelong KGE framework that considers learning new facts and preserving old facts in a unified perspective. We propose a diffusion-based embedding method that captures the contextual variation of entity representations and obtains transferable embeddings. In order to handle the drift of the embedding space and balance the learning efficiency, we adopt a reconstruction and generation strategy based on contrastive learning. To avoid catastrophic forgetting and maintain the stability of the embedding distribution, we proposed an effective distribution regularization method. We conduct extensive experiments on seven benchmark datasets with different construction strategies and incremental speed. Experimental results show that our proposed framework outperforms existing lifelong KGE methods.

终身知识图嵌入（KGE）方法的目的是在保留旧知识的同时不断学习新知识。在知识图的规模和灵活性不断增长的情况下，这方面的工作因其实现知识保留和转移以及降低培训成本的潜力而受到广泛关注。然而，在不同情境下嵌入空间漂移是灾难性遗忘和新事实学习效率低下的重要原因，现有的研究忽视了这一观点。为了解决上述问题，我们提出了一种新的终身KGE框架，该框架从统一的角度考虑了学习新事实和保留旧事实。我们提出了一种基于扩散的嵌入方法，该方法捕获实体表示的上下文变化并获得可转移的嵌入。为了处理嵌入空间的漂移和平衡学习效率，我们采用了一种基于对比学习的重构生成策略。为了避免灾难性遗忘和保持嵌入分布的稳定性，提出了一种有效的分布正则化方法。我们在7个基准数据集上采用不同的构建策略和增量速度进行了广泛的实验。实验结果表明，我们提出的框架优于现有的终身KGE方法。

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引用次数: 0

FluidFormer : Transformer with continuous convolution for particle-based fluid simulation FluidFormer：具有连续卷积的变压器，用于基于颗粒的流体模拟

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-21 DOI: 10.1016/j.neunet.2026.108631

Nianyi Wang, Shuai Zheng, Yu Chen, Hai Zhao, Zhou Fang

Learning-based fluid simulation has emerged as an efficient alternative to traditional Navier-Stokes solvers. However, existing neural methods that build upon Smoothed Particle Hydrodynamics (SPH) predominantly rely on local particle interactions, which induces instability in complex scenarios due to error accumulation. To address this, we introduce FluidFormer, a novel architecture that establishes a hierarchical local-global modeling paradigm. The core of our model is the Fluid Attention Block (FAB), a co-design that orchestrates continuous convolution for locality with self-attention for global corrective long-range hydrodynamic phenomena. Embedded in a dual-pipeline network, our approach seamlessly fuses inductive physical biases with structured global reasoning. Extensive experiments show that FluidFormer achieves state-of-the-art performance, with significantly improved stability and generalization in challenging fluid scenes, demonstrating its potential as a robust simulator for complex physical systems.

基于学习的流体模拟已经成为传统Navier-Stokes解算器的有效替代方案。然而，现有的基于光滑粒子流体动力学（SPH）的神经方法主要依赖于局部粒子相互作用，这在复杂的情况下由于误差积累而导致不稳定。为了解决这个问题，我们介绍了FluidFormer，这是一种新的架构，可以建立分层的局部全局建模范式。我们模型的核心是流体注意块（FAB），这是一种协同设计，它协调了局部的连续卷积和全局校正远程流体动力学现象的自关注。我们的方法嵌入在双管道网络中，将归纳物理偏差与结构化全局推理无缝融合。大量实验表明，FluidFormer实现了最先进的性能，在具有挑战性的流体场景中具有显着提高的稳定性和通用性，证明了其作为复杂物理系统鲁棒模拟器的潜力。

引用次数: 0

Relation-aware pre-trained network with hierarchical aggregation mechanism for cold-start drug recommendation 基于层次聚合机制的关系感知预训练网络冷启动药物推荐

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-21 DOI: 10.1016/j.neunet.2026.108618

Xiaobo Li , Xiaodi Hou , Shilong Wang , Hongfei Lin , Yijia Zhang

Drug recommendation systems have garnered considerable interest in the healthcare, striving to offer precise and customized drug prescriptions that align with patients’ specific health needs. However, existing methods primarily focus on modeling temporal dependencies between visits for patients with multiple encounters, often neglecting the challenge of data sparsity in single-visit patients. To address above limitation, we propose a novel Relation-aware Pre-trained Network with hierarchical aggregation mechanism for drug recommendation (RPNet), which employs a pre-training and fine-tuning framework to enhance drug recommendation in cold-start scenario. Specifically, we introduce: 1) A code matching discrimination task during pre-training, designed to model the complex relationships between diagnosis and procedure entities. This task employs a mask-replace contrastive learning strategy, which pulls similar samples closer while pushing dissimilar ones apart, thereby capturing robust feature representations; 2) A hierarchical aggregation mechanism that enhances drug information integration by first selecting relevant visits based on rarity discrimination and then retrieving similar patients’ drug insights via similarity matching during fine-tuning. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed RPNet, notably improving the F1 metric by 1.32% and 1.19%. The code of our model is available at https://github.com/Lxb0102/RPNet.

药物推荐系统已经在医疗保健领域获得了相当大的兴趣，努力提供精确和定制的药物处方，与患者的特定健康需求保持一致。然而，现有的方法主要侧重于对多次就诊患者就诊之间的时间依赖性建模，往往忽略了单次就诊患者的数据稀疏性的挑战。为了解决上述问题，我们提出了一种新的具有层次聚合机制的关系感知预训练药物推荐网络（RPNet），该网络采用预训练和微调框架来增强冷启动场景下的药物推荐。具体来说，我们介绍了：1)在预训练过程中，一个代码匹配判别任务，旨在对诊断和过程实体之间的复杂关系进行建模。该任务采用掩模替换对比学习策略，将相似的样本拉得更近，同时将不相似的样本分开，从而捕获鲁棒特征表示；2)层次聚合机制，首先基于稀缺性判别选择相关就诊，然后在微调过程中通过相似性匹配检索相似患者的药物见解，增强药物信息整合。在两个真实数据集上的大量实验证明了RPNet的优越性，显著提高了F1指标1.32%和1.19%。我们模型的代码可以在https://github.com/Lxb0102/RPNet上找到。

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引用次数: 0

MMFormer: Multi-Modality semi-Supervised vision transformer in remote sensing imagery classification MMFormer：遥感图像分类中的多模态半监督视觉转换器

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-21 DOI: 10.1016/j.neunet.2026.108628

Daixun Li , Weiying Xie , Leyuan Fang , Yunke Wang , Zirui Li , Mingxiang Cao , Jitao Ma , Yunsong Li , Chang Xu

Significant progress has been made in the application of transformer architectures for multimodal tasks. However, current methods such as the self-attention mechanism rarely consider the benefits that feature complementarity and consistency between different modalities bring to fusion, leading to obstacles such as redundant fusion or incomplete representation. Inspired by topological homology groups, we introduce MMFormer, a novel semi-supervised algorithm for high-dimensional multimodal fusion. This method is engineered to capture comprehensive representations by enhancing the interactivity between modal mappings. Specifically, we advocate for the representational consistency between these heterogeneous representations through a complete dictionary lookup and homology space in the encoder, and establish an exclusivity-aware mapping of the two modalities to emphasize their complementary information, serving as a powerful supplement for multimodal feature interpretation. Moreover, the model attempts to alleviate the challenge of sparse annotations in high-dimensional multimodal data by introducing a consistency joint regularization term. We have formulated these focuses into a unified end-to-end optimization framework and are the first to explore and derive the application of semi-supervised visual transformers in high-dimensional multimodal data fusion. Extensive experiments across three benchmarks demonstrate the superiority of MMFormer. Specifically, the model improves overall accuracy by 3.12% on Houston2013, 1.86% on Augsburg, and 1.66% on MUUFL compared with the strongest existing methods, confirming its robustness and effectiveness under sparse annotation conditions. The code is available at https://github.com/LDXDU/MMFormer.

变压器架构在多模态任务中的应用取得了重大进展。然而，现有的自注意机制等方法很少考虑到不同模式之间的互补性和一致性给融合带来的好处，导致融合冗余或表征不完整等障碍。受拓扑同调群的启发，提出了一种新的半监督算法MMFormer，用于高维多模态融合。该方法旨在通过增强模态映射之间的交互性来捕获全面的表示。具体而言，我们主张通过完整的字典查找和编码器中的同源空间来实现这些异构表示之间的表征一致性，并建立两种模态的排他性感知映射，以强调它们的互补信息，作为多模态特征解释的有力补充。此外，该模型试图通过引入一致性联合正则化项来缓解高维多模态数据中稀疏注释的挑战。我们已经将这些重点制定为统一的端到端优化框架，并率先探索和推导了半监督视觉变形在高维多模态数据融合中的应用。在三个基准测试中进行的大量实验证明了MMFormer的优越性。具体而言，与现有最强的方法相比，该模型在Houston2013上的总体准确率提高了3.12%，在Augsburg上提高了1.86%，在MUUFL上提高了1.66%，证实了其在稀疏标注条件下的鲁棒性和有效性。代码可在https://github.com/LDXDU/MMFormer上获得。

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引用次数: 0

PHoM: Effective pan-sharpening via higher-order state-space model 基于高阶状态空间模型的有效泛锐化

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-01-21 DOI: 10.1016/j.neunet.2026.108616

Penglian Gao , Hongwei Ge , Shuzhi Su

Pan-sharpening is intended to generate high-resolution multi-spectral images, utilizing pairs of low-resolution multi-spectral and high-resolution panchromatic images. Recently, the Mamba-based pan-sharpening models achieve state-of-the-art performance due to their efficient long-range relational modeling. However, Mamba inherently obeys a first-order state-space high-dimensional nonlinear mapping, which fails to efficiently encode higher-order expressive interactions of spectral features. In this study, we propose a novel higher-order state-space model for pan-sharpening (PHoM). Our PHoM follows the concept of splitting, interaction, and aggregation for higher-order spatial adaptive interaction and discriminative learning without introducing excessive computational overhead. To model the fusion process between multi-spectral and panchromatic images, we further extend the PHoM into a cross-modal PHoM, which further improves the representation capability by exploiting higher-order cross-modal correlations. We conduct extensive experiments on different datasets. Experimental results show that our method achieves significant performance improvements, outperforming previous state-of-the-art methods on public datasets.

泛锐化的目的是产生高分辨率的多光谱图像，利用对低分辨率多光谱和高分辨率全色图像。最近，基于曼巴的泛锐化模型由于其高效的远程关系建模而实现了最先进的性能。然而，曼巴固有地服从一阶状态空间高维非线性映射，无法有效地编码光谱特征的高阶表达相互作用。在这项研究中，我们提出了一种新的高阶状态空间泛锐化模型。我们的PHoM遵循分裂、交互和聚合的概念，用于高阶空间自适应交互和判别学习，而不会引入过多的计算开销。为了模拟多光谱和全色图像之间的融合过程，我们进一步将PHoM扩展为跨模态PHoM，通过利用高阶跨模态相关性进一步提高了表征能力。我们在不同的数据集上进行大量的实验。实验结果表明，我们的方法在公共数据集上取得了显着的性能改进，优于以前最先进的方法。

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引用次数: 0