Graph anomaly detection (GAD), which aims to identify abnormal nodes that deviate from the majority, has become increasingly important in high-stakes Web domains. However, existing GAD methods follow a ''one model per dataset'' paradigm, leading to high computational costs, substantial data demands, and poor generalization when transferred to new datasets. This calls for a foundation model that enables a ''one-for-all'' GAD solution capable of detecting anomalies across diverse graphs without retraining. Yet, achieving this is challenging due to the large structural and feature heterogeneity across domains. In this paper, we propose TFM4GAD, a simple yet effective framework that adapts tabular foundation models (TFMs) for graph anomaly detection. Our key insight is that the core challenges of foundation GAD, handling heterogeneous features, generalizing across domains, and operating with scarce labels, are the exact problems that modern TFMs are designed to solve via synthetic pre-training and powerful in-context learning. The primary challenge thus becomes structural: TFMs are agnostic to graph topology. TFM4GAD bridges this gap by ''flattening'' the graph, constructing an augmented feature table that enriches raw node features with Laplacian embeddings, local and global structural characteristics, and anomaly-sensitive neighborhood aggregations. This augmented table is processed by a TFM in a fully in-context regime. Extensive experiments on multiple datasets with various TFM backbones reveal that TFM4GAD surprisingly achieves significant performance gains over specialized GAD models trained from scratch. Our work offers a new perspective and a practical paradigm for leveraging TFMs as powerful, generalist graph anomaly detectors.
论文检索
输入标题、作者或关键词，从 801 篇学术成果中精准定位
Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains to enrich information within the target domain. However, inherent domain gaps can degrade the quality of mixed-domain data, leading to negative transfer and diminished model performance. Existing prevailing model-centric paradigm - which relies on complex, customized architectures - struggles to capture the subtle, non-structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose Taesar, a data-centric framework for target-aligned sequential regeneration, which employs a contrastive decoding mechanism to adaptively encode cross-domain context into target-domain sequences. It employs contrastive decoding to encode cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures. Experiments show Taesar outperforms model-centric solutions and generalizes to various sequential models. By generating enriched datasets, Taesar effectively combines the strengths of data- and model-centric paradigms. The code accompanying this paper is available at~ https://github.com/USTC-StarTeam/Taesar.
Large-scale multimodal contrastive learning has recently achieved impressive success in learning rich and transferable representations, yet it remains fundamentally limited by the uniform treatment of feature dimensions and the neglect of the intrinsic spectral structure of the learned features. Empirical evidence indicates that high-dimensional embeddings tend to collapse into narrow cones, concentrating task-relevant semantics in a small subspace, while the majority of dimensions remain occupied by noise and spurious correlations. Such spectral imbalance and entanglement undermine model generalization. We propose Spectral Disentanglement and Enhancement (SDE), a novel framework that bridges the gap between the geometry of the embedded spaces and their spectral properties. Our approach leverages singular value decomposition to adaptively partition feature dimensions into strong signals that capture task-critical semantics, weak signals that reflect ancillary correlations, and noise representing irrelevant perturbations. A curriculum-based spectral enhancement strategy is then applied, selectively amplifying informative components with theoretical guarantees on training stability. Building upon the enhanced features, we further introduce a dual-domain contrastive loss that jointly optimizes alignment in both the feature and spectral spaces, effectively integrating spectral regularization into the training process and encouraging richer, more robust representations. Extensive experiments on large-scale multimodal benchmarks demonstrate that SDE consistently improves representation robustness and generalization, outperforming state-of-the-art methods. SDE integrates seamlessly with existing contrastive pipelines, offering an effective solution for multimodal representation learning.
Domain-adaptive hashing enhances discriminative hash representations by transferring knowledge from a label-rich source domain to a label-scarce target domain. It has attracted significant attention due to its ability to enable efficient cross-domain retrieval without requiring target domain labels. However, existing methods generally assume that source domain labels are completely accurate. In practice, labels obtained via web crawling or crowdsourcing often contain varying degrees of noise, which hampers semantic alignment and aggravates domain shift. To tackle these issues, we propose a novel method termed Evidential Learning and Progressive Alignment (ELPA) for domain-adaptive hashing. This method comprises two key modules: the Uncertainty-aware Noise Separation (UNS) and the Progressive Cross-domain Alignment (PCA). In the UNS, we exploit the belief and uncertainty masses obtained from the evidential learning model and utilize the posterior probabilities of a Gaussian Mixture Model to effectively distinguish clean samples from noisy ones. In PCA, we introduce a progressive partial optimal transport mechanism that prioritizes pseudo-label generation for well-aligned target samples, thereby gradually achieving class-level and global-level cross-domain alignment. Extensive experiments across multiple benchmark datasets with various noise ratios demonstrate that ELPA consistently surpasses existing state-of-the-art methods, exhibiting superior robustness and generalization capability.
Cross-domain recommendation (CDR) has emerged as a promising solution by effectively alleviating data sparsity by leveraging information from auxiliary domains. However, a major challenge in CDR is its dependence on predefined alignment rules (e.g., structural or distribution matching) to achieve cross-domain knowledge transfer, which impose fixed transfer patterns and lack the flexibly need for diverse cross-domain scenarios. Furthermore, most existing approaches still rely on coarse-grained representations. Knowledge transfer built upon imprecise representations can, even with improved alignment rules, instead lead to negative transfer in the target domain. To address these challenges and optimize recommendation efficacy, a new framework named meta-learning driven few-shot knowledge transfer with dual-stage adaptive data replay for cross-domain recommendation (MFACDR) is proposed. Specifically, a new meta-learning driven few-shot knowledge transfer method is proposed. This method leverages overlapping parts as anchors to guide the non-overlapping parts in autonomously exploring alignment rules through meta-learning, thus enabling few-shot knowledge transfer and flexible handling of different cross-domain scenarios. In addition, a dual-stage adaptive data replay mechanism is proposed, which enables fine-grained cross-domain adaptability and helps to mitigate negative transfer. Extensive experiments on three real-world datasets consistently demonstrate the superior effectiveness and robustness of the proposed MFACDR.
This paper investigates a novel and critical problem of Graph Cross-Domain Continual Fine-Tuning, which aims to adapt a large pre-trained Graph Foundation Model across diverse domains. Existing continual graph learning methods are mostly limited to incremental settings within only a single domain, and are typically trained from scratch. As a result, they fail to handle cross-domain shifts effectively, suffer from severe forgetting, and lack transferability. To address these challenges, we present G-CORMoL, Graph Continual Fine-tuning with Orthogonal, Router-driven Mixture of LoRA experts. G-CORMoL achieves effective adaptation while preserving prior knowledge by enforcing mathematical orthogonality between expert LoRA adapters, thereby eliminating interference across tasks. It further supports cross-domain knowledge transfer through a symmetric dual-driven routing mechanism that learns a global composition policy over all learned LoRA experts. In addition, it promotes expert specialization via a contrastive objective with theoretical guarantees. Extensive experiments on different cross-domain task orders demonstrate that G-CORMoL achieves robust state-of-the-art performance, not only preventing catastrophic forgetting but also leveraging accumulated knowledge to enable positive transfer.
Large Language Models (LLMs) have shown significant potential in handling text-attributed graphs (TAGs). Nevertheless, existing LLM-enhanced approaches for TAG learning mainly utilize LLMs as text augmenters, which not only incur substantial computational overhead but may also introduce noise. In contrast, their capacity as embedding generators remains underutilized. Furthermore, most current methods are designed for specific TAG instances and lack cross-domain transferability. To overcome these limitations, we propose UTAG, a novel framework that leverages LLMs as unified embedding generators. First, we design a dual-view contrastive fine-tuning approach for cross-domain pretraining. The in-domain view promotes local consistency via adaptive positive sample selection, while the cross-domain view establishes alignment through subspace clustering-based cross-domain prototype aggregation. By jointly pretraining on multiple TAG datasets from diverse domains, we obtain a unified LLM encoder that produces transferable representations. Subsequently, we introduce a graph-specific adaptation training technique that implements two-stage dimensionality reduction with carefully designed semantic and Laplacian reconstruction constraints. This allows effective incorporation of structural signals while preserving semantic information to the greatest extent. Additionally, we employ cross-space alignment to further enhance consistency. Extensive experiments demonstrate that UTAG substantially surpasses existing LLM-enhanced methods, achieving an average improvement of 5.9% on cross-domain transfer tasks, which verifies its effectiveness and superiority.
Graph Foundation Models (GFMs) have emerged as a frontier in graph learning, which are expected to deliver transferable representations across diverse tasks. However, GFMs remain constrained by in-memory bottlenecks: they attempt to encode knowledge into model parameters, which limits semantic capacity, introduces heavy lossy compression with conflicts, and entangles graph representation with the knowledge in ways that hinder efficient adaptation, undermining scalability and interpretability. In this work,we propose RAG-GFM, a Retrieval-Augmented Generation aided Graph Foundation Model that offloads knowledge from parameters and complements parameterized learning. To externalize graph knowledge, we build a dual-modal unified retrieval module, where a semantic store from prefix-structured text and a structural store from centrality-based motif. To preserve heterogeneous information, we design a dual-view alignment objective that contrasts both modalities to capture both content and relational patterns. To enable efficient downstream adaptation, we perform in-context augmentation to enrich supporting instances with retrieved texts and motifs as contextual evidence. Extensive experiments on five benchmark graph datasets demonstrate that RAG-GFM consistently outperforms 13 state-of-the-art baselines in both cross-domain node and graph classification, achieving superior effectiveness and efficiency.
Graph anomaly detection (GAD) identifies nodes, edges, and subgraphs that deviate from normal patterns, playing a key role in cybersecurity, finance, and web technologies. However, existing deep learning paradigms trained on fixed datasets struggle to generalize to unseen graphs, while current graph foundation models (GFMs) face inherent challenges in anomaly detection. Their generalization objectives conflict with the rarity and divergence of anomalies, and existing attempts to develop GFMs for GAD mainly fall into two directions: single-task cross-domain models and domain-specific multi-task models. The former transfers knowledge across domains but fails to capture cross-level dependencies, whereas the latter handles multiple tasks jointly but lacks cross-domain adaptability. Both rely heavily on labeled data, limiting scalability. These limitations highlight the need for a unified unsupervised graph foundation model capable of generalizing across domains and detecting anomalies at multiple levels. We propose GFM-UAD, a Graph Foundation Model for Unified Anomaly Detection. It is pretrained on a single dataset and performs multi-level anomaly detection across unlabeled domains. To address anomaly scarcity, GFM-UAD extracts high-confidence normal samples and synthesizes diverse anomalies through adversarial generation. By freezing the backbone and fine-tuning the detection head with distribution alignment and message passing, the model adapts flexibly to unseen environments without supervision. Experiments across multiple datasets demonstrate that GFM-UAD achieves superior accuracy and generalization, establishing a foundation for future unsupervised graph foundation models in anomaly detection.
Graph incremental learning aims to sequentially adapt models to evolving graphs while mitigating catastrophic forgetting. This problem becomes particularly challenging due to the simultaneous occurrence of covariate and label distribution shifts, introduced by newly emerging node classes, changes in node feature styles, and additional edges. To address these challenges, we propose DINGLE, a novel framework for both class and domain (class-domain) incremental learning on graphs. DINGLE consists of two key modules: a representation decoupler, which disentangles node representations into domain-invariant semantic factors for classification and domain-specific variation factors, and a teacher-student knowledge distillation module, which facilitates knowledge transfer across tasks while mitigating catastrophic forgetting through memory replay. By leveraging a Representative Node Feature (RNF) bank and an Encoder Parameters (EP) bank, DINGLE ensures effective knowledge retention and adaptation. Extensive experiments on 5 real-world datasets demonstrate that DINGLE outperforms 11 state-of-the-art baselines in class-domain incremental learning, improving classification accuracy while effectively preventing forgetting across tasks.
Unsupervised graph domain adaptation (UGDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph, addressing the performance degradation caused by distributional shifts in node attributes and graph structures across domains. Despite recent progress, existing UGDA approaches still face two key challenges: (C1) Data-level: Most methods rely on a single source domain, overlooking the complementary knowledge that could be leveraged from multiple sources. (C2) Model-level: Many UGDA models emphasize complex, handcrafted Graph neural network (GNN) architectures, while simpler yet effective designs with propagation (P) & transformation (T) pipeline remain underexplored. To address these challenges, in this paper, we propose a novel approach, which leverages Concise Propagation–Transformation pipeline for multi-source unsupervised Graph Domain Adaptation, dubbed as CPT-GDA, to better capture complementary knowledge from multiple sources in an efficient manner. Specifically, the proposed CPT-GDA adopts a dual-branch GNN architecture with different depths of propagation but the same P-T patterns, which enables the model to efficiently learn node representations to mitigate domain discrepancy. Meanwhile, to facilitate effective knowledge transfer across graphs, we derive three optimization objectives: (1) the classifier loss to learn discriminative representations; (2) the alignment loss weighted by the graph Wasserstein distance to align the structure and feature distribution; and (3) the pseudo-label loss to refine target node representations. Extensive experiments on real-world datasets confirm that the proposed method outperforms recent state-of-the-art baselines, demonstrating its effectiveness.
Automatically mapping and segmenting global mining footprints using remote sensing and deep learning is critical for monitoring the socio-environmental risks and impacts of mining, yet its progress is hindered by the scarcity of fine-grained annotated data. Although large-scale datasets with coarse boundaries are widely available, leveraging them to improve fine-grained segmentation is challenging due to significant domain shift. To address this, we propose MineC2FNet, a coarse-to-fine domain incremental learning framework that exploits abundant coarse data to enhance fine-grained mining footprint segmentation. MineC2FNet adopts a teacher–student architecture with attentive distillation at both the feature and prediction levels, selectively transferring generalized knowledge from the coarse domain while enabling boundary refinement using limited fine-grained data (fine domain). We further introduce an expertly validated dataset of 219 images with precise boundary annotations across diverse geographies and commodities. Extensive experiments against state-of-the-art approaches, including domain adaptation and domain incremental learning methods, demonstrate that MineC2FNet achieves superior performance while effectively handling domain shift. The dataset and code are publicly available at https://github.com/risqiutama/MineC2FNet.
Robotic perception in unstructured environments remains challenging despite the zero-shot capabilities of foundation models such as SAM. This work attributes performance degradation to non-uniform representation shifts across transformer layers: shallow layers exhibit substantial domain gaps (CKA < 0.5), whereas deep layers transfer effectively (CKA > 0.7). Based on this observation, we propose RepSAM, a representation-guided parameter-efficient fine-tuning (PEFT) framework for adapting foundation models to robotic vision. RepSAM employs a theoretically grounded CKA-guided rank allocation strategy combined with a multi-modal fusion module for robust handling of challenging robotic scenarios, including transparent objects and cluttered scenes. Experimental evaluation across six benchmarks and robotic manipulation tasks demonstrates that RepSAM achieves 97.9% of full fine-tuning performance (89.0% vs. 90.9% mIoU) while reducing trainable parameters by 158× (from 632M to 4.0M). RepSAM outperforms DoRA by 7.9% mIoU with just 4 hours of training on a single A100 GPU (a 96× reduction from full fine-tuning, which takes 384 GPU-hours). These improvements are statistically significant (p<0.01) and translate to a 12.0% absolute improvement in robotic manipulation success rates over the LoRA (RGB) baseline.
Unified segmentation aims to consolidate multiple vision tasks into a single model, yet faces two core challenges: learning robust homogeneous features (e.g., shared low- and mid-level cues) to enable cross-domain knowledge transfer, while disentangling heterogeneous features (e.g., task-specific semantic objectives) to avoid negative transfer and preserve task independence. To address these challenges, we propose the Homo- and Heterogeneous Network (H2Net), a unified framework that jointly models shared homogeneous representations and task-specific heterogeneous features. Specifically, H2Net incorporates a Cross-Modal Structure Enhancement Module (CSEM), which integrates auxiliary depth priors via joint frequency–spatial cross-modal attention to strengthen task-agnostic structural representations. In addition, a Task Adapter Pool (TAP) is introduced to model task-specific heterogeneous features by assigning dedicated adapters to individual tasks, enabling task-aware feature modulation and semantic disentanglement within a shared backbone. Extensive experiments on benchmarks spanning eight tasks demonstrate the effectiveness of the proposed approach and its superior performance. Code and results will be available at https://h2net-ijcai26.github.io.
Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods largely overlook the issue of Catastrophic Forgetting (CF): Models often sacrifice their ability to retain source-domain knowledge while pursuing cross-domain robustness. This can directly threaten diagnostic safety in already-deployed clinical scenarios. To address this, we investigate data augmentation strategies and catastrophic forgetting for medical image DG segmentation. First, we propose a structure-guided style diffusion augmentation method. Constrained by anatomical structure consistency in the frequency domain, this method performs cross-domain diffusion on the amplitude spectrum, generating samples with more diverse and broader style coverage to better support domain generalization. Then, we design a collaborative learning network with a dual-branch interactive architecture (CoDG-Net), together with a novel learning bias-guided strategy that adaptively regulates knowledge transfer at both the layer level and the task level, thereby effectively mitigating catastrophic forgetting on the source domain. Experiments and ablation studies on single-source and multi-source medical DG benchmark datasets demonstrate that CoDG-Net not only outperforms existing state-of-the-art methods in target-domain segmentation performance, but also achieves a lower forgetting rate on the source-domain data. The code is available at: https://github.com/wangprocess/CoDG-Net.
Zero-shot multimodal anomaly detection is critical for identifying structural defects that are often invisible to traditional RGB images, particularly in scenarios lacking target domain samples. However, existing methodologies face two significant impediments: the prohibitive computational overhead caused by relying on multi-view rendering for depth processing, and the inability of static text prompts to adapt to fine-grained local anomalies. To address these challenges, a unified zero-shot multimodal anomaly detection framework GRASP is proposed. Firstly, a Frequency Domain Enhancement module is introduced to replace costly rendering with spectral transformations, directly synthesizing high-fidelity depth images for efficient utilization. Secondly, a Prompt-Conditioned Variational module is designed to bridge the semantic gap by grounding global textual descriptions into local visual nuances. Finally, a Dual Cross-Injection Alignment module is proposed to enable robust feature fusion for enhancing anomaly classification performance, while a Pyramid Anomaly Map Recalibration module further refines anomaly localization across multiple scales. Extensive experiments on MVTec 3D-AD and Eyecandies demonstrate that GRASP establishes a new state-of-the-art, yielding substantial improvements of 3.0 points in I-AUROC and 2.7 points in AUPRO compared to the SOTA method.
Open cross-domain federated graph learning facilitates collaborative learning among clients from distinct graph domains while preserving privacy. However, severe structure and feature heterogeneity in open scenarios exacerbates the multiplicative amplification of structural and feature noises within graph neural network encoders, leading to significant negative transfer. Existing methods addressing partial heterogeneity are insufficient. We theoretically reveal this amplification mechanism and propose a novel learning method FedFINFO, which leverages full-informativeness shared knowledge. Specifically, we construct an aligned and general feature space integrating node semantic and structural roles. We proposed a counterfactual multi-view framework to explicitly learn a structure masker for extracting consensus subgraphs. These jointly suppress the mutual amplification of noises at the source. We also propose a history-aware adaptive multi-channel aggregation mechanism to enable dynamic and personalized sharing driven by channel similarity while preventing collaborative oscillation. FedFINFO effectively reconciles common knowledge sharing with personalized knowledge retention, significantly mitigating negative transfer. Extensive experiments under cross-dataset and cross-domain settings demonstrate the superiority of our method.
Source-Fully-Free Domain Adaptation (SFF-DA) has emerged as a strategic paradigm to adapt Vision-Language Models (VLMs) without any access to source data or task-specific source models. However, we identify a critical "Dual Semantic Drift" that hinders this process: static drift caused by the stagnation of frozen hand-crafted templates, and dynamic drift resulting from noisy, instance-level generated captions. These issues lead to a severe stability-plasticity dilemma, manifesting as semantic misalignment and class collapse. To address this, we propose DSSG (Dual-Stream Semantic Guidance), an end-to-end framework that reconciles fine-grained plasticity with global stability. Our core contribution is the Dual Semantic Guidance (DSG) module, which integrates a captioning stream for detailed domain nuances with a template stream to anchor global categorical consistency. Furthermore, a Dynamic Cross-Modal Knowledge Distillation (CMKD) module is introduced to adaptively optimize teacher-student alignment across modalities. Extensive experiments demonstrate that DSSG significantly outperforms current state-of-the-art methods across multiple benchmarks, providing a robust solution for the unsupervised transfer of foundation models. The code is available on https://github.com/mrmenand/DSSG.
Adapting large, adversarially pre-trained models to specialized domains via transfer learning is a promising path toward building secure AI systems. However, a critical challenge arises when fine-tuning on limited downstream data: models often suffer from catastrophic forgetting of robustness, where the generalizable robust features from pre-training are erased, leading to severe robust overfitting. To address this, we first establish the underlying principle that adversarial vulnerability is not diffuse, but is concentrated in a low-dimensional subspace defined by the principal components of the model's weight matrices. We then introduce Principal component-based Implicit regularization with Low-rank Optimization (PILO), a novel parameter-efficient framework that operationalizes this insight. PILO employs a principled decoupling of learning objectives: a spectrally-guided adversarial branch, initialized via Singular Value Decomposition (SVD), performs a surgical update on the identified vulnerable subspace to enhance robustness. This is complemented by a transient, standard low-rank branch that gently adapts the model to natural data, preserving clean accuracy. This dual-branch design acts as a form of implicit spectral regularization, anchoring the model to its robust pre-trained state. Extensive experiments show that PILO significantly outperforms state-of-the-art full-parameter and parameter-efficient methods in robust accuracy across multiple benchmarks, all while reducing trainable parameters by up to 71%. Our work thus establishes a new, more effective paradigm for robust transfer learning through principled and targeted parameter optimization.
Code-switching (CS) speech translation (ST) aims to translate speech that alternates between multiple languages into a target language text, posing significant challenges due to the complexity of semantic modeling and the scarcity of CS data. Previous studies mainly rely on the models themselves to implicitly learn semantic representations and resort to costly manual annotations. To mitigate these limitations, we propose enhancing Large Language Models (LLMs) with a Mixture-of-Experts (MoE) speech projector composed of language expert groups, where each group specializes in the semantic space of a specific language for fine-grained speech feature modeling. A language-specific loss and an intra-group load balancing loss are jointly introduced to guide efficient token routing across and within expert groups. Furthermore, we introduce a multi-stage training paradigm that utilizes readily available automatic speech recognition (ASR) and monolingual ST data, facilitating speech-text alignment and improving translation performance. To bridge the data gap for smooth domain transfer, a transition loss is employed to improve adaptation to CS scenarios. Extensive experiments on widely used datasets demonstrate the effectiveness and generality of our approach, achieving average improvements of 0.86 BLEU and 0.93 COMET over SeamlessM4T, with maximum improvements of 1.49 BLEU and 1.41 COMET across different test sets. Our code and supplementary appendices are available at https://github.com/XMUDeepLIT/CSST-SSA.