High-resolution sinogram completion is critical for computed tomography reconstruction, as missing projections can introduce severe artifacts. While diffusion models provide strong generative priors for this task, their inference cost grows prohibitively with resolution. We propose HRSino, a training-free and efficient diffusion inference approach for high-resolution sinogram completion. By explicitly accounting for spatial heterogeneity in signal characteristics, such as spectral sparsity and local complexity, HRSino allocates inference effort adaptively across spatial regions and resolutions, rather than applying uniform high-resolution diffusion steps. This enables global consistency to be captured at coarse scales while refining local details only where necessary. Experimental results show that HRSino reduces peak memory usage by up to 30.81% and inference time by up to 17.58% compared to the state-of-the-art framework, and maintains completion accuracy across datasets and resolutions.
论文检索
输入标题、作者或关键词，从 9,256 篇学术成果中精准定位
Most text-to-motion models treat motion duration as a fixed hyperparameter rather than a variable inferred from semantics, inducing two systematic failure modes: duration trailing on short prompts and stage-wise semantic collapse on long, multi-stage prompts. To address this issue, we propose TPPMG (Temporal Planning-driven Progressive Motion Generation), which factorizes generation as semantics -> temporal structure -> motion realization. TPPMG comprises two components: (1) a Temporal Planner that maps text to an explicit stage-duration script via an LLM with a lightweight duration calibrator; and (2) a Progressive Diffusion Generator that bridges fixed-length diffusion training and variable-length inference through heterogeneous noise scheduling and a sliding-window mechanism, enabling budget-driven variable-length motion synthesis. To evaluate both failure modes with verifiable ground truth, we construct three benchmarks: HumanML3D-Short, HumanML3D-Concat, and BABEL-Concat, along with a comprehensive evaluation suite. Experiments show that TPPMG suppresses duration trailing (UR: 0.52 -> 0.85), substantially improves multi-stage structure recovery (PhaseRecall: 0.56 -> 0.90, BoundaryF1: 0.28 -> 0.56), and reduces boundary discontinuity at stage transitions.
Unsupervised 3D semantic segmentation is vital for label-free open-world perception. However, current methods typically struggle with two core limitations: fixed category assumptions that restrict adaptability in complex scenes, and geometric incompleteness caused by irregular point sampling, which degrade the integrity of local geometric descriptors for fine-grained or long-tail objects. To address these, we propose DyG-Seg, a Dynamic Geometric-Semantic Alignment framework. First, targeting the challenge of incomplete geometry, the Geometric Manifold Rectification (GMR) leverages a diffusion model to reconstruct integral object geometry and recover the underlying manifold topology, thereby enhancing feature discriminability. Utilizing this geometric prior, the Dynamic Prototype Contrastive Clustering (DPCC) applies non-parametric Bayesian inference to automatically estimate the optimal number of clusters and generate pseudo-labels. Finally, our Iterative Optimization with Dynamic Constraints integrates Dynamic Class Balancing (DCB) to mitigate long-tail bias. Capitalizing on the recovered manifold topology, we enforce Spatial Geometric Consistency to rectify structural incompleteness and ensure spatially coherent semantic predictions. Extensive experiments on ScanNet and S3DIS demonstrate that DyG-Seg discovers latent semantic structures and significantly outperforms state-of-the-art unsupervised methods.
Unauthorized personalization based on diffusion models pose a severe and growing threat to digital privacy by enabling the unauthorized replication and exploitation of individual identities. Existing disrupting-based defenses primarily add invisible perturbations arbitrarily across the entire image space to disrupt the generation process. However, we reveal that these methods fundamentally overlook the spatio-temporal dynamics of the personalization process, resulting in inefficient optimization that fails to sufficiently disrupt the core identity encoding mechanism. To mitigate these limitations, we propose IdentityMask, a robust protection framework that shifts the paradigm from arbitrary confusion to precise, targeted feature corruption. By anchoring the perturbation on subject-specific semantics and prioritizing the most critical diffusion timesteps, our framework ensures the disruption is maximized precisely where the identity is encoded. Additionally, a novel manifold projection strategy is introduced to embed the adversarial signals into the intrinsic structure of the image, rendering the protection resilient against state-of-the-art purification. Extensive experiments across diverse datasets, personalization techniques, and defense settings demonstrate that IdentityMask consistently outperforms prior state-of-the-art approaches in both protection efficacy and robustness.
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.
The increasing availability of trajectory data is often hampered by sparsity and noise. Existing trajectory recovery methods are further limited by either information loss from coordinate discretization into location IDs, or the inefficiency of conventional diffusion models that require a lengthy denoising process from pure noise. To address these challenges, we propose DiffVec, a novel and efficient diffusion framework for free-space trajectory recovery that operates directly on continuous coordinate data. The backbone of our framework is MVformer, a Transformer-based architecture that models motion vectors—the differences between consecutive coordinates—to better capture the local motion patterns of movement. This model is trained within our ResTraj diffusion paradigm, which commences the denoising process from a structured, interpolated prior to significantly reduce sampling steps. Extensive experiments on the Geolife and Porto datasets demonstrate that DiffVec significantly outperforms state-of-the-art baselines across MAE, MSE, and NDTW.
Reconstructing PDE-governed fields from sparse and irregular measurements is challenging due to their ill-posed nature. Deterministic surrogates are trained on dense fields that struggle with limited measurements and uncertainty quantification. Generative models, by learning distributions over spatiotemporal fields, can better handle sparsity and uncertainty. However, existing generative approaches enforce data consistency and PDE constraints simultaneously via sampling-time gradient guidance, resulting in slow and unstable inference. To this end, we propose PerFlow, a Physics-embedded rectified Flow for efficient sparse reconstruction and uncertainty quantification of spatiotemporal dynamics. PerFlow decouples observation conditioning from physics enforcement, performing guidance-free conditioning by feeding observations into rectified-flow dynamics while embedding hard physics via a constraint-preserving projection (e.g., incompressibility or conservation). Theoretically, we establish invariance guarantees to ensure that trajectories remain on the physics-consistent manifold throughout sampling. Experiments on various PDE systems demonstrate competitive reconstruction accuracy with sound physics consistency, while enabling efficient conditional sampling (e.g., 50 steps) and up to 320x faster inference than 2000-step guided diffusion baselines.
Concept erasure aims to suppress sensitive content in diffusion models, but recent studies show that erased concepts can still be reawakened, revealing vulnerabilities in erasure methods. Existing reawakening methods mainly rely on prompt-level optimization to manipulate sampling trajectories, neglecting other generative factors, which limits a comprehensive understanding of the underlying dynamics. In this paper, we model the generation process as an implicit function to enable a comprehensive theoretical analysis of multiple factors, including textual conditions, model parameters, and latent states. We theoretically show that perturbing each factor can reawaken erased concepts. Building on this insight, we propose a novel concept reawakening method: Latent space Unblocking for concept REawakening (LURE), which reawakens erased concepts by reconstructing the latent space and guiding the sampling trajectory. Specifically, our semantic re-binding mechanism reconstructs the latent space by aligning denoising predictions with target distributions to reestablish severed text-visual associations. However, in multi-concept scenarios, naive reconstruction can cause gradient conflicts and feature entanglement. To address this, we introduce Gradient Field Orthogonalization, which enforces feature orthogonality to prevent mutual interference. Additionally, our Latent Semantic Identification-Guided Sampling (LSIS) ensures stability of the reawakening process via posterior density verification. Extensive experiments demonstrate that LURE enables simultaneous, high-fidelity reawakening of multiple erased concepts across diverse erasure tasks and methods.
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.
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.
Cooperative perception allows agents to extend perceptual capabilities through inter-agent communication. However, most existing methods still adopt a frame-wise paradigm, which limits the exploitation of spatio-temporal consistency in dynamic scenes. Therefore, when observations are partially occluded or degraded, these methods are unable to leverage historical context for compensation, leading to unstable perception and reduced detection accuracy. To address these challenges, we propose World4V2X, the first world model framework tailored for V2X cooperative perception. The proposed method first introduces a spatial observability modeling module that defines spatial consistency boundaries to distinguish reliable regions from uncertain ones, thereby enabling spatial consistency modeling over multi-agent heterogeneous observations. Building upon this, we construct a consistency-guided world modeling module. Specifically, a temporal consistency evolution mechanism leverages features from historical and current frames to model the state evolution of the scene, capturing environmental dynamics and producing temporal consistency beliefs. Meanwhile, a consistency-guided deterministic reconstruction mechanism exploits spatial boundaries and temporal beliefs to perform diffusion-based refinement for robust cooperative perception. Extensive experiments on the OPV2V and V2XSet datasets demonstrate that World4V2X achieves state-of-the-art perception performance across diverse V2X scenarios.
Image quality is critical for accurate medical diagnosis. However, MRI, CT, and ultrasound images are often of low resolution and quality due to cost constraints, complicating the visualization of key anatomical structures and lesions. While such limitations are common in practice, traditional methods treat image enhancement as a separate preprocessing step, failing to fully leverage its potential synergy with image segmentation. To address this, we propose DiSIINet (Diffusion-based Symbiotic Information Interaction Network), which is built on the principle that enhancement and segmentation should mutually reinforce each other in a unified model. Based on Denoising Diffusion Implicit Models (DDIM), DiSIINet integrates an enhancement branch and a segmentation branch. These branches interact through a novel Symbiotic Information Interaction (SII) module, which facilitates dynamic, feature-level information exchange via cross-attention during the reverse diffusion process. This design enables both tasks to iteratively improve each other. The DDIM backbone ensures high-quality output and efficient inference through deterministic sampling. Experiments on multi-modal medical datasets (MRI, CT, ultrasound) show that DiSIINet achieves significant performance improvements compared to sequential or independent enhancement and segmentation approaches. The code is available at: https://github.com/Reconsider80/DiSIINet.
Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, where models behave normally on clean data but exhibit targeted misclassifications once specific triggers are activated. Existing backdoor attacks on GNNs mainly focus on enhancing trigger stealthiness or diversifying attack paradigms. However, these methods overlook a fundamental property of GNNs: trigger-induced malicious signals inevitably propagate through graph neighborhoods, causing unintended mispredictions on clean nodes, i.e., Collateral Damage. To address this issue, we propose the Collateral Damage Constrained Graph Backdoor Attack (CDCA), a novel framework that explicitly controls malicious diffusion. Specifically, the proposed method combines neighborhood-aware target node selection with a self-constrained trigger generation strategy to suppress trigger-induced propagation by enforcing prediction consistency on clean K-hop neighboring nodes. Extensive experiments on real-world datasets demonstrate that the proposed method remains effective while significantly reducing collateral damage.
The diffusion auction is an emerging auction model which leverages social networks to recruit more buyers, thereby improving auction revenue and allocation efficiency. Previous studies on diffusion auctions have primarily focused on the single-parameter domain, where each buyer's valuation function is represented by a single private value. However, due to the complex interdependencies between allocations and network structures, few studies have examined the combinatorial auction domain, in which buyers' valuation functions exhibit multidimensionality. In this paper, we fully characterize implementable diffusion mechanisms within the combinatorial auction domain for the first time. Based on the characterization, we identify a class of allocation policies for each of which the optimal payment, namely the one maximizing the seller's revenue, can be derived in closed form. Furthermore, we apply the established theory to design a practical diffusion combinatorial auction, named the Exhausted-Reference Pricing Mechanism, which is incentive-compatible, individually rational, and weakly budget balanced.
Due to proliferation of vehicle trajectory data from advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches work to some extent, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts cross-scenario generalization capabilities. To address these limitations, we present DGCPath – an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This architecture establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diversified trajectory views from Gaussian noise distributions; (2) a variational contrastive mechanism enforcing latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate DGCPath's superior performance over state-of-the-art baselines in two distinct downstream tasks, validating its enhanced generalization capacity and representation effectiveness.
Multi-view learning aims to enhance performance by integrating information from multiple sources. While different views offer complementary perspectives, extracting consistent and discriminative representations remains a significant challenge due to discrepancies in representation and presence of noise. Existing methods typically separate the denoising and alignment processes, mapping the denoised heterogeneous views to a shared subspace for alignment. This means that noise may propagate or even amplify during the alignment stage, ultimately leading to suboptimal solutions. To address this issue, we propose center-guided spectral diffusion, which replaces traditional alignment with generative modeling. This method avoids noise propagation in multi-view alignment process and prevents multi-view features be aligned to the noise subspace during fusion process. Specifically, we first performs unconditional diffusion in both the feature space and a low-rank spectral space to learn a stable consensus centre anchor. This anchor is then used to condition a guided generative diffusion process, enabling the model to generate more consistent and realistic sample representations. By combining conditional and unconditional diffusion, the proposed method alleviates the noise amplification problem commonly found in traditional alignment methods. Experimental results show that the proposed method outperforms state-of-the-art methods on several datasets.
Generative models continue to advance rapidly in both fidelity and diversity, posing increasing challenges for reliably distinguishing generated images from real ones. Existing reconstruction-based detection methods often rely on assumptions tied to specific generative models, which leads to limited cross-model generalization and substantial computational overhead. In this paper, we propose General Feature Reconstruction Error (GFRE), a fast and generalizable detection paradigm that leverages reconstruction behavior in a general-purpose representation space. Our key insight is that real and generated images exhibit consistently different reconstruction stability when projected into universal visual representations. Instead of tracing generator-specific artifacts, GFRE employs a lightweight autoencoder to model the reconstructability of image representations, producing a reconstruction signal that is inherently generator-agnostic and transferable across diverse generative processes. Extensive experiments on images synthesized by 18 different generative models demonstrate that GFRE consistently outperforms existing state-of-the-art methods, achieving a 4.70% improvement in detection accuracy and an 8.20% gain in cross-model generalization. Moreover, by avoiding costly generator inversion and diffusion-based reconstruction, GFRE reduces reconstruction error extraction time by up to 150x, enabling efficient and scalable deployment.
Designing functional protein sequences that satisfy multiple desired properties is a core research focus of protein engineering. Prior methods struggle with inability or inefficiency when dealing with numerous, often conflicting, properties. We propose Multi-Property Protein Diffusion, (MP2D), a unified framework for multi-objective protein sequence optimization that integrates conditional discrete diffusion with constrained MCTS and global iterative refinement. MP2D formulates diffusion denoising as a constrained sequential decision-making process and employs MCTS to explore diverse denoising trajectories guided by Pareto-based rewards. A global iterative refinement strategy further enables repeated remasking and re-optimization of candidate sequences, while a dynamic Pareto constraint prevents candidate bloat and maintains balanced trade-offs across objectives. We evaluate MP2D on two challenging multi-objective protein design tasks: antimicrobial peptide and protein binder optimization, involving four to five conflicting properties. Experimental results demonstrate that MP2D consistently outperforms existing multi-objective baselines, achieving robust and balanced improvements across all objectives without retraining generative models. These results highlight MP2D as a practical and scalable solution for multi-objective functional protein design.
Combinatorial optimization (CO) problems have widespread applications in science and engineering, but they present significant computational challenges. Recent advancements in generative models, particularly diffusion models, have shown promise in bypassing traditional optimization solvers by directly generating near-optimal solutions. However, existing diffusion solvers are highly sensitive to the quality of the training dataset, particularly the number of problem instances for training and the optimality of their labels. Notably, we observe an exponential scaling law between the performance of diffusion-based solvers and the number of near-optimally labeled instances needed. When training instances are scarce or sub-optimally labeled, diffusion-based solvers suffer significant performance degradation. To enhance the robustness of diffusion solvers to dataset quality, we propose a robust diffusion solver for combinatorial optimization capable of learning from sub-optimally labeled instances follows a two-stage generate-then-decode framework, integrating an objective-guided diffusion model, further reinforced by classifier-free guidance, to produce solutions that surpass the optimality of the training dataset. Experiments demonstrate the improved robustness in \myalg compared to the diffusion-based solver baseline, in a range of combinatorial optimization benchmark tasks such as TSP (Traveling Salesman Problem) and MIS (Maximum Independent Set).
The rapid progress of identity-feature-based face-swapping technology has raised concerns about impersonation and privacy violations. Although proactive defenses aim to block identity extraction at the source, existing methods suffer from perceptible visual artifacts, poor generalization across diverse deepfake models, and vulnerability to post-processing techniques (e.g., diffusion purification, image compression, and transformations). This work proposes a robust, generalizable proactive face-swapping defense via semantic gradient divergence (SGD-Guard) to address these challenges. It introduces an integrated feature gallery that uses CLIP features and a generalized identity feature, obtained by iteratively refining heterogeneous identity features into a homogeneous representation. This framework facilitates our semantic distortion attack by leveraging consensus weighting to target specific facial attributes within a CLIP-identity joint embedding space, disrupting deepfake generation while preserving visual fidelity. Furthermore, to ensure robustness against purification and post-processing, this method incorporates a module that prioritizes critical transformations by exploiting directional discrepancies. Comprehensive experiments demonstrate that the method effectively defends against diverse face-swapping models with high cross-model transferability.