Vision Transformers (ViTs) exhibit notable susceptibility to adversarial attacks, presenting a significant challenge for their deployment in security-sensitive applications. Despite their considerable empirical successes, a rigorous theoretical foundation for ViT's adversarial generalization behavior has not been adequately established. To address this limitation, we leverage empirical Rademacher complexity to analyze the mechanism of perturbation accumulation through deep ViTs layers. We establish a high-probability generalization bound for ViTs in classification tasks under adversarial settings. Our theoretical framework elucidates the roles of several factors in mitigating perturbation effects, norm regularization of weight matrices (in both MLP and attention modules) and depth-wise propagation constraints on layer-wise norms. Extensive experiments on benchmark datasets corroborate our theoretical insights, bridging the gap between ViTs architecture design and adversarial robustness.
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Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characteristics. Intermediate phases—key microstructural constituents—are pivotal in regulating mechanical and functional properties. However, intermediate phase segmentation in zinc alloy microstructures faces formidable challenges: scarce annotated datasets, low contrast, difficulty detecting small targets, and heterogeneous morphologies. To this end, we construct IPSM-Bench, the largest high-quality dataset for zinc-alloy intermediate phase segmentation. Furthermore, we propose SCoP-SAM, a new Spatial Context Prior-guided SAM method that leverages the gradient structure and grayscale properties of intermediate phases to capture spatial context priors and incorporates them into the entire SAM encoding-decoding process, improving segmentation performance. Based on the proposed IPSM-Bench, we establish a new benchmark for intermediate phase segmentation to systematically evaluate state-of-the-art (SOTA) methods and advance research on zinc alloy microstructure analysis. Extensive experiments on IPSM-Bench and additional public alloy benchmarks demonstrate that our SCoP-SAM not only achieves SOTA performance for zinc-alloy intermediate phase segmentation but also generalizes remarkably well to other alloy scenarios.
Spatiotemporal fusion (STF) bridges the gap between temporal and spatial resolutions in satellite imagery, enabling effective monitoring of Earth's surface dynamics. However, existing methods rely on cloud-free reference images, a constraint that fails in realistic, cloud-prone scenarios. To overcome this, we propose the Cloud-Aware Wavelet Generative Adversarial Network (CLAW-GAN), a novel framework for high-fidelity reconstruction under cloud-contaminated conditions. CLAW-GAN introduces Regression Wavelet Analysis (RWA) to decouple spectral backgrounds from structural details. While a change-aware gated mechanism accounts for land-cover changes, the Frequency-Separated Fusion (FSF) module then independently integrates these components. To ensure visual realism, a multi-scale discriminator operates in the wavelet domain, enforcing consistency across high-frequency subbands to minimize artifacts. Evaluated on the newly introduced Global Cloud-shrouded Agricultural Regions (GCAR) benchmark and the simulated Daxing dataset, CLAW-GAN achieves state-of-the-art performance and demonstrates superior robustness across varying cloud coverage.
Reasoning chains in Large Language Models (LLMs) often exhibit heavy-tailed length distributions, yet existing efficiency methods rely on suboptimal linear penalties that suppress complex reasoning, limiting both accuracy and generalization. To address this, we first empirically observe that reasoning lengths are well approximated by a log-normal distribution, and provide an intuitive explanation for this phenomenon. Based on this insight, we propose the Powered Length Penalty (PLP), an adaptive regularizer that penalizes redundancy in short sequences while gradually reducing penalties for longer sequences, preserving deep reasoning. Trained solely on the elementary GSM8K dataset, PLP significantly improves reasoning efficiency by reducing inference costs on GSM8K and MATH500, while simultaneously enhancing accuracy on the challenging AIME2024 benchmark. Furthermore, PLP transfers effectively to diverse domains, including MMLU and GPQA. These results suggest that modeling reasoning length distributions and adapting penalties accordingly can mitigate the typical trade-off between efficiency and performance, enabling more reliable and cost-effective reasoning across tasks.
Semi-supervised clustering (SSC) enables personalized clustering under limited user supervision. Given the sparsity of initial user intents, constraint propagation has been proposed as a powerful approach to explore and generate new performance-enhancing constraints. However, existing methods struggle to handle a large number of ``gray samples'' that deviate from initial supervision signals and exhibit ambiguous semantics with indistinct boundaries. Compared with ``distinct samples'' that closely match the supervision, gray samples typically contain richer latent semantics, and accurately identifying their relational types can significantly improve clustering performance. To address this challenge, we propose Adversarially Enhanced Propagation-driven Intent-aware Clustering (AEPIC). Specifically, we design an Adversarially Enhanced Constraint Propagation (AECP) mechanism that leverages global adversarial learning over dual relational links to identify gray samples and expand them into meaningful pseudo-user intents. In addition, an intent-aware regularization strategy integrates these pseudo-user intents into representation learning and clustering optimization, further improving clustering performance. Experiments on 5 benchmark datasets demonstrate that, under sparse supervision, AEPIC consistently outperforms state-of-the-art semi-supervised clustering methods.
Point cloud completion seeks to recover geometrically consistent shapes from partial or sparse 3D observations. Although recent methods have achieved reasonable global shape reconstruction, they often rely on Euclidean proximity and overlook the intrinsic nonlinear geometric structure of point clouds, resulting in suboptimal geometric consistency and semantic ambiguity. In this paper, we present a manifold-aware point cloud completion framework that explicitly incorporates nonlinear geometry information throughout the feature learning pipeline. Our approach introduces two key modules: a Geodesic Distance Approximator (GDA), which estimates geodesic distances between points to capture the latent manifold topology, and a Manifold-Aware Feature Extractor (MAFE), which utilizes geodesic-based k-NN groupings and a geodesic-relational attention mechanism to guide the hierarchical feature extraction process. By integrating geodesic-aware relational attention, our method promotes semantic coherence and structural fidelity in the reconstructed point clouds. Extensive experiments on benchmark datasets demonstrate that our approach consistently outperforms state-of-the-art methods in reconstruction quality. The code has been available online https://anonymous.4open.science/r/Manifold-Aware-Point-Cloud-Completion--F380/README.md.
Backdoor attacks pose a serious threat to deep neural networks, especially when training relies on third-party data, allowing adversaries to inject malicious behaviors through data poisoning. In this work, we reveal that backdoor behaviors tend to be absorbed by a simpler parallel branch when jointly trained with the main network. Motivated by this insight, we propose Trapping and Removing (TR), a simple yet effective training-time defense that introduces a lightweight shortcut branch as a “honeypot” to trap backdoor knowledge. After training, backdoors can be removed by discarding the shortcut, without requiring any additional data. To further enhance backdoor isolation while maintaining benign performance, we design a knowledge decoupling strategy with entropy-based weight assignment, encouraging poisoned samples to flow through the honeypot while guiding the main network to focus on benign learning. In addition, we introduce an automatic shortcut generation strategy to improve generalization across model architectures. Extensive experiments on four benchmark datasets and five model architectures demonstrate that our approach effectively mitigates a wide range of backdoor attacks while preserving performance on benign data. Code: github.com/Zixuan-Zhu/TR.
Label Distribution Learning (LDL) represents each instance with a label distribution, but these distributions are often incomplete in practice due to high annotation costs and annotators’ cognitive burden. Recent Incomplete Label Distribution Learning (InLDL) methods leverage global and local correlations to impute missing values, imputation errors remain systematic and non-uniform across labels. However, in the more challenging scenario of imbalanced label distributions, such non-uniform errors disproportionately harm low-degree labels, skewing learning toward dominant labels. To address the more complex challenge, we propose Label Distribution Imputation and Bias-Corrective Representations (LDIBR) for incomplete and imbalanced label distribution learning. LDIBR performs instance-adaptive imputation conditioned on instance features and the binary observation mask. It learns a prior, a reliability-gated correction, and entry-wise fusion weights to produce a normalized imputed distribution. Additionally, LDIBR decomposes the imputed distributions into a shared low-rank core and an instance-specific residual. The core captures stable global degree patterns, while the residual isolates sample-specific deviations and imputation noise. Comprehensive experiments across multiple benchmark datasets with a 50% missing ratio validate both the effectiveness and robustness of the LDIBR approach. The code is available at https://github.com/wenhuihji/LDCBR.
Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxies as deterministic points in the embedding space. This rigid assumption causes severe gradient conflicts in multi-label scenarios, where gradient conflicts arising from label co-occurrence lead to severe gradient contention and optimization collapse.To resolve this, we propose Kent-based Distributional Proxy Hashing (KDPH), a novel framework that shifts proxy representation from static points to flexible anisotropic Kent distributions on the hypersphere. Unlike point proxies that must shift their positions to accommodate conflicting gradients, KDPH absorbs these conflicts by dynamically adjusting its directional variance. This allows the proxy to maintain a stable semantic mean direction while stretching to cover diverse label correlations. Furthermore, to ensure stable training of these geometric parameters, we derive a tailored loss function incorporating the Cayley transform to enforce strict orthogonality. To the best of our knowledge, KDPH is the first framework to successfully introduce the Kent distributions into cross-modal hashing.Experiments on three benchmark datasets demonstrate that KDPH mitigates proxy collapse and chaotic oscillation, significantly outperforms state-of-the-art methods. Code is available at https://github.com/Senmo996/KDPH-official-code.
Multi-view clustering has garnered significant attention for its ability to integrate heterogeneous data and uncover underlying categorical structures. However, prevailing autoencoder-based methods often yield indiscriminative embeddings, rendering them prone to trivial solutions. While diffusion models have shown promise in modeling complex data distributions, their generative processes remain inherently unstable. More critically, the resulting latent representations often lack sufficient intra-class compactness and inter-class separability, leading to suboptimal clustering performance. In this paper, we propose SCD-MVC, a Stable Conditional Diffusion-driven framework for Multi-View Clustering. Specifically, it integrates conditional diffusion generation into the latent encoder to generate a target view conditioned on the remaining views, thereby facilitating robust joint distribution modeling. To align the generative process with clustering objectives, we introduce a clustering-guided semantic alignment module. This module regulates the diffusion path to enforce the learning of representations that are both view-consistent and discriminative. Furthermore, we introduce a temporal consistency regularization that enforces alignment between representations of adjacent diffusion steps. This stabilizes training and guides the optimization toward clustering-friendly representations. Extensive experiments on nine benchmark datasets demonstrate that SCD-MVC consistently outperforms eight state-of-the-art methods, achieving performance gains ranging from 6.68% to 13.09%. The source code is available at https://github.com/Knighttt0011/SCD-MVC.
Artificial intelligence models typically perform well on large-scale datasets, yet their effectiveness tends to degrade in real-world scenarios with scarce data, such as medical diagnostics. In contrast, humans can learn and reason effectively from few examples. Even when novel objects differ significantly from prior observations, humans can quickly infer category distributions accurately. Inspired by this, we explore how to leverage human-inspired cognitive mechanisms to improve the distribution calibration in few-shot learning models. Based on the ``fast-slow thinking'' dual-process theory, we propose a novel cognition-inspired few-shot learning framework. It mimics the human cognition when handling novel information: it first rapidly screens relevant knowledge through ``fast thinking'' and then infers inter-class relationships and calibrates distributions via ``slow thinking''. Specifically, the fast-thinking stage employs a gating mechanism to quickly match input samples with known base-class prototypes, activating relevant candidate knowledge. In the slow-thinking stage, the model aggregates inter-class edge embeddings into a summary relation graph and then applies divergent Gaussian sampling to generate multiple relation graphs representing different association strengths, thereby achieving distribution calibration for few-shot classes. Experiments on public few-shot benchmarks and medical image datasets show competitive performance. Visualization analyses further reveal that our framework exhibits meaningful interpretability.
Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient. However, existing approaches generally struggle to reconcile fine-grained representation learning, especially under class imbalance and real-world constraints. In this paper, we present FreSH, a Frequency-Segmented Hierarchical Multi-Expert Framework designed to address these challenges. FreSH introduces a new perspective for MTSC by enabling adaptive, multi-scale analysis of temporal signals, allowing different aspects of the data to be modeled in a complementary and coordinated manner. By combining localized specialization with holistic context modeling, FreSH achieves strong representational capacity without incurring excessive computational overhead. An adaptive fusion strategy further enhances flexibility, enabling the model to dynamically emphasize the most informative components of the input. In addition, we incorporate a more robust optimization objective that improves learning stability across varying sample difficulties and class distributions. Extensive evaluations on 30 UEA benchmark datasets and real-world vibration data demonstrate that FreSH consistently outperforms state-of-the-art methods in classification accuracy, while substantially reducing model size and efficiency. The implementation code is publicly available at https://github.com/Wangmy2120/FreSH00.
As a prominent paradigm for large-scale unsupervised learning, anchor-based multi-view clustering aims to reveal the latent structures across heterogeneous data representations with high efficiency. Despite achieving some progress, existing methods typically suffer from the following two limitations. Firstly, they rely on pre-constructed anchors or rigid constraints (e.g., orthogonality), whereas the intrinsic topological correlations among anchors are completely discarded. Secondly, most existing methods either perform clustering directly on the bipartite graph or treat different views equally, thereby failing to capture the global structural information. To this end, the Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship (i.e., bipartite graph) but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold. By integrating an adaptive view-weighting strategy to balance view contributions, DOTA derives discriminative global sample embeddings by propagating spectral information through the bipartite graph. Extensive experiments on benchmark datasets demonstrate the superiority of DOTA.
Short video harmful content detection aims to automatically identify diverse anomalies from user-generated media. This task presents unique challenges due to frequent editing cuts and highly variable anomaly densities, limiting the effectiveness of traditional surveillance-based approaches. Moreover, existing Vision-Language Model-based approaches typically rely on rigid instance selection mechanisms that fail to adapt to the unpredictable duration of anomalies in such unconstrained videos. To address these issues, we propose SVLA, a Shot-conditioned Vision-Language Adaptation framework, for effectively detecting harmful contents from online short videos. Our approach introduces a novel π-adaptive strategy to dynamically estimate shot-level anomaly density, replacing rigid selection with calibrated supervision. Furthermore, we employ a shot-conditioned temporal encoder to respect video hierarchy and adopt a dual-path contextual adapter to resolve semantic ambiguity. To benchmark this task, we construct a new dataset (SVA) covering more genuine online short videos that involve seven anomaly categories. Experiments on the SVA dataset demonstrate that SVLA can achieve the state-of-the-art performance and outperform its competitors across diverse scenarios. Codes and datasets are available at: https://github.com/xushuai7/IJCAI-SVLA.
Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the understanding of cellular heterogeneity. Despite the significant progress in scRNA-seq data clustering, we argue that current methods always ignore the sparsity and noise, as well as the complex intercellular structural information inherent in scRNA-seq data. Toward this end, in this paper, we propose a novel single-cell RNA-seq clustering framework via deep Siamese Graph Transformer Network (termed scGTN), which explicitly integrates gene expression profile and intercellular structural dependencies for cell clustering. In particular, we formulate scRNA-seq data as a graph and construct two augmented graph views that serve as dual views to capture complementary intercellular information. Then, a Siamese graph transformer network is employed to explicitly incorporate shortest-path information and node-wise distances for capturing richer structural relationships between cells. Finally, we employ an optimal transport strategy to guide the cell clustering in a self-supervised manner. Extensive experiments on multiple benchmark scRNA-seq datasets demonstrate that our scGTN consistently outperforms existing methods. Our code is available at https://github.com/W-RMSL/scGTN.
Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency. Similarity-based methods often suffer from quadratic complexity caused by pairwise distance computations, while deep learning–based approaches typically rely on costly iterative training and a large number of trainable parameters. In this paper, we propose MSRGC-Net, an efficient time-series clustering framework that integrates multiscale reservoir computing, granular-ball-based anchoring graph construction, and consensus learning. MSRGC-Net adopts a training-free reservoir computing paradigm to extract multiscale temporal representations from raw time series without backpropagation, significantly reducing computational overhead. To capture the intrinsic structure of the resulting representations, granular-ball computing is employed to adaptively model data distributions via density-consistent regions, yielding compact and robust anchor graph representations. Furthermore, a consensus-based anchoring graph optimization strategy is introduced to effectively align multiscale reservoir representations and integrate complementary information across temporal scales. Extensive experiments on widely used univariate and multivariate benchmark datasets demonstrate that MSRGC-Net consistently outperforms state-of-the-art methods in clustering performance while maintaining superior computational efficiency.
Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to enable LLMs to select appropriate tools and correctly invoke them to meet user requirements. However, it is observed in previous works that the performance of tool learning varies from tasks, datasets, training settings, and algorithms. Without understanding the impact of these factors, it can lead to inconsistent results, inefficient model deployment, and suboptimal tool utilization, ultimately hindering the practical integration and scalability of LLMs in real-world scenarios. Therefore, in this paper, we explore the impact of both internal and external factors on the performance of tool learning frameworks. Through extensive experiments on two benchmark datasets, we find several insightful conclusions for future work, including the observation that LLMs can benefit significantly from increased trial and exploration. We believe our empirical study provides a new perspective for future tool learning research.
Spatial transcriptomics has significantly advanced tissue biology and makes it possible to study the spatial interactions of cells in the microenvironment of complex tissues. However, accurately inferring intercellular communication from these data remains challenging due to the need to effectively integrate spatial topology with high-dimensional gene expression information. To address this challenge, we propose DAHGT-CCI, a novel method for inferring intercellular communication from spatial transcriptomics data. By fusing multimodal heterogeneous features to construct a unified node representation and introducing a meta-relation-aware dynamic attention mechanism, DAHGT-CCI can adaptively learn the weights of edges in the graph, thereby achieving superior precision and fine-grained inference of intercellular interactions. We evaluated the performance of DAHGT-CCI on six different spatial transcriptomics datasets. In these benchmarks, DAHGT-CCI outperformed existing graph-based learning methods and cell-cell communication inference models in terms of accuracy, AUROC, recall, and F1 score. These results demonstrate that DAHGT-CCI can more accurately reconstruct cell communication networks in complex tissue microenvironments, offering an indispensable computational tool for studying developmental processes, disease mechanisms, and potential therapeutic targets from a spatially resolved perspective.
Multi-view clustering aims to utilize information from multiple feature representations to uncover underlying data structures. Most existing methods emphasize learning a consensus representation by enforcing consistency across views. However, those structures that cannot be directly incorporated into the clustering space receive little attention, and are often discarded as noise in practice. In this paper, we argue that such information can be informative signals for the clustering process and should be explicitly modeled rather than suppressed or ignored. Specifically, we propose salient-residual decoupled multi-view learning for clustering, SRDMVC, introducing a novel decomposition-fusion iterative optimization, which separates the feature space into a salient space and a residual subspace effectively and fuses them using a novel attention mechanism. The residual subspace can capture the deep-level structure of view information, making positive contributions to the clustering process, under proper constraints. Without assuming stronger view alignment or complementarity, SRDMVC enhances cluster discrimination, avoids spurious consensus, and alleviates representation degradation. Extensive experiments on benchmark datasets demonstrate the superior performance of the method we propose.
Cross-Domain Graph Anomaly Detection supports the transfer of knowledge to unknown targets. Nevertheless, current approaches frequently struggle in the Cross-Broad-Domain paradigm which is characterized by two significant discrepancies: feature heterogeneity and structural disparity. Such diverse geometric topologies result in substantial manifold mismatch, as the traditional dependence on linear residuals in flat Euclidean space induces considerable geometric distortion when accommodating various topological patterns. We propose a novel framework termed Geometry-aware Riemannian Anomaly DEtector (GRADE) to tackle these difficulties. To address the semantic gap, we develop a domain-based contrastive learning technique combined with target-adaptive prompt tuning, which dynamically adjusts source representations to align with the target distribution while maintaining local contexts. To tackle the manifold mismatch, we present the Riemannian Residual Displacement (RRD), a curvature-adaptive metric that maps node representations onto multi-curvature Riemannian manifolds. By quantifying geodesic deviations in curved spaces, RRD corrects geometric distortions and identifies anomaly patterns that remain invariant to topological alterations. Additionally, a geometry-aware prototype alignment approach secures these residuals to data-independent priors for resilient inference. Comprehensive experiments on 11 benchmark datasets across 4 distinct domains demonstrate that GRADE significantly outperforms state-of-the-art baselines in both zero-shot and few-shot settings.