Suche: diffusion models

Mehr als 1.000 Paper passen: hier die 1.000 neuesten, nach Relevanz sortiert.

• WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models

  Jiamin Xu, Cong Wang, Zheng Dong, Chi Wang, Renshu Gu, Weiwei Xu, Gang Xu · 30. Juli 2026 · Image Enhancement Techniques

  Video shadow removal in the wild remains challenging due to complex illumination, diverse shadow appearances, and limited training data. Despite its importance to numerous vision and graphics applications, it remains largely unexplored in unconstrained real-world scenarios. To address this gap, we p…

• ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions

  Yule Li, Yifeng Lu, Zhen Wang, Zhewei Wei, Yaliang Li, Bolin Ding · 30. Juli 2026 · Advanced Graph Neural Networks

  In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks. Although GNNs inherently encode specific inductive biases (e.g., acting as low-pass or high-pass filters), most existing methods implicitly assume conditional independence among node labe…

• Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality

  Xiaoyin Pan, Christian R. Shelton, Rakshith Mahishi, Chengkuan Hong · 30. Juli 2026 · Point processes and geometric inequalities

  We study generative modeling of spatial point processes (SPP), where both the number of points and their spatial configuration are governed by a joint distribution. While diffusion models have achieved strong performance in modeling complex distributions, extending them to variable-cardinality SPP r…

• Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

  Xiaolong Liu, Junjian Li, Yuan Xiao, Jiaqi Deng, Dayong Ye, Tianqing Zhu, Huan Huo · 30. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineering. However, existing prompt inversion methods suffer from significant limitations: (1) gradient-based methods are unstab…

• FARI: Robust One-Step Inversion for Watermarking in Diffusion Models

  Jindong Yang, Han Fang, Weiming Zhang, Nenghai Yu, Kejiang Chen · 30. Juli 2026 · Advanced Steganography and Watermarking Techniques

  Inversion-based watermarking is a promising approach to authenticate diffusion-generated images, yet practical use is bottlenecked by inversion that is both slow and error-prone. While the primary challenge in the watermarking setting is robustness against external distortions, existing approaches o…

• Diff-ID: Identity Consistent Facial Image Generation and Morphing via Diffusion Models

  Taimoor Rizwan, Sara Atito, Muhammad Awais, Zhenhua Feng, Josef Kittler · 29. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Generative diffusion models have revolutionized facial image synthesis, yet robust identity preservation in high resolution outputs remains a critical challenge. This issue is especially vital for security systems, biometric authentication, and privacy sensitive applications, where any drift in iden…

• Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation

  Farzana Yasmin Ahmad, Vanamala Venkataswamy, Geoffrey Fox · 29. Juli 2026 · Particle physics theoretical and experimental studies

  Monte Carlo simulation of calorimeter showers is a principal bottleneck for the High-Luminosity LHC, and diffusion models have emerged as fast, high-fidelity surrogates. Their denoising objective is purely statistical, however: a model can minimize it while placing the physics wrong. Existing physic…

• OmniCache: Multidimensional Hierarchical Feature Caching For Diffusion Models

  Zhaoyuan He, Muhammad Muaz, Lili Qiu · 28. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  High-resolution image and video diffusion models, including SD3, FLUX, and recent video diffusion transformers, have substantially improved generative quality but remain expensive at inference time because they repeatedly evaluate attention-heavy denoisers over many sampling steps. We address this i…

• Exact Evaluation of the Accuracy of Diffusion Models for Inverse Problems with Gaussian Data Distributions

  Emile Pierret, Bruno Galerne · 28. Juli 2026 · Statistical and numerical algorithms

  Used as priors for Bayesian inverse problems, diffusion models have recently attracted considerable attention in the literature. Their flexibility and high variance enable them to generate multiple solutions for a given task, such as inpainting, super-resolution, and deblurring. However, there is st…

• Learning Sampling Parameters for Diffusion Models

  Arisrei Lim, Yossi Gandelsman · 28. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Text-to-image diffusion models expose many inference-time sampling parameters, including prompts, negative prompts, classifier-free guidance scales, and noise schedules. These parameters are typically manually chosen once and then held fixed across prompts and denoising timesteps, even though differ…

• From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models

  Jinshu Huang, Yiming Jiang, Chunlin Wu · 28. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Despite the empirical success of score-based diffusion models, a complete theoretical understanding of how finite-sample learning, network parameterization, and numerical discretization jointly dictate generative quality remains underdeveloped. Existing sampling analyses often evaluate the generativ…

• Correlation-Aware and Gaussianity-Preserving Robust Latent Angular Watermarking for Diffusion Models

  Yebin Zheng, Haonan An, Guang Hua, Zhiping Lin, Yuguang Fang · 27. Juli 2026 · Advanced Steganography and Watermarking Techniques

  Latent domain watermarking for diffusion models embeds watermarks directly into the latent prior, enjoying non-intrusiveness to model parameters and seamless integration with the generation process. However, due to the violation of latent Gaussianity or sensitivity to normal and malicious perturbati…

• Spectral Prior for Reducing Exposure Bias in Diffusion Models

  Yuya Kobayashi, Masato Ishii, Yuhta Takida, Takashi Shibuya, Yuki Mitsufuji · 27. Juli 2026 · Advanced Neuroimaging Techniques and Applications

  Diffusion models typically suffer from error accumulation during iterative sampling, commonly referred to as exposure bias. We reveal systematic frequency-dependent discrepancies between training and inference, which can be interpreted as frequency-dependent SNR error. Crucially, the direction of th…

• Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions

  Arthur Dantas Mangussi, Joana Cristo Santos, Ricardo Cardoso Pereira, Ana Carolina Lorena, Mário A. T. Figueiredo, Pedro Henriques Abreu · 27. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. In medical imaging, this task is particularly important because artifacts, missing information, and pathological alterations can compromise diagnostic …

• From Score Approximation to Distribution Approximation in Score-Based Diffusion Models

  Lan V. Truong · 27. Juli 2026 · Markov Chains and Monte Carlo Methods

  Score-based diffusion models have achieved remarkable empirical success in generative modeling, yet their approximation-theoretic foundations remain incomplete. In particular, although classical universal approximation theorems guarantee that neural networks can approximate score functions, it remai…

• Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability

  Ahmed M. Abuzuraiq, Philippe Pasquier · 27. Juli 2026 · Artistic and Creative Research

  Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug models as part of making Yet largescale texttoimage diffusion systems are typically presented as opaque endtoend tools limiting this kind of material enga…

• TILT: Improving Compositional Generation in Diffusion Models with a Model-Intrinsic Reward

  Debottam Dutta, Jaehoon Hahm, Jianchong Chen, Romit Roy Choudhury · 27. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Recent advances in powerful text-to-image generation models have made it increasingly important to develop test-time methods that modify the sampling trajectory to produce images more faithful to complex compositional prompts. We present TILT, a training-free framework for compositional text-to-imag…

• Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers

  Sicheng Mo, Yuheng Li, Ziyang Leng, Krishna Kumar Singh, Bolei Zhou · 24. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared st…

• Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning

  Rogerio Guimaraes, Pietro Perona · 24. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion and flow-matching models dominate conditional image generation, yet inference-time scaling for these models is far less developed than for autoregressive language models. Because final quality is highly sensitive to the initial noise seed, many approaches spend extra compute on seed search…

• Texture++: Elevating 3D Asset Texture Resolution with a Region-Aware Diffusion Model

  Shuaiwei Wang, Shi Li, Jieting Xu, Yuchi Huo, Qi Wang, Wenting Zheng, Rengan Xie · 24. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Numerous 3D assets are discarded due to low texture resolution, while current super-resolution models ignore texture maps and focus on natural images. An efficient and generalizable texture super-resolution model can revitalize a large corpus of aging yet valuable assets across industries such as fi…

• Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting

  Ferdinand Bhavsar (INRAE), Lionel Benoit (INRAE), Maxime Savatier (ANDRA), Edith Gabriel (INRAE) · 24. Juli 2026 · Hydrology and Watershed Management Studies

  The modeling of hydrometeorological time series with limited observations is a key challenge in the monitoring of hydro-systems and water resources, as well as for flood or drought risk assessment. Due to the high variability of the underlying processes and the sparsity of available measurements, tr…

• A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention

  Ziping Xu, Yuyi Chang, Chenshun Ni, Nithin Sugavanam, Asim H. Gazi, Pedja Klasnja, Emre Ertin, Susan A. Murphy · 24. Juli 2026 · Digital Mental Health Interventions

  Mobile-health interventions increasingly use online learning and decision making algorithms to personalize when to nudge users toward healthier behavior, but a poorly designed algorithm can burden and disengage participants. New algorithm design decisions should therefore be vetted against realistic…

• Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT

  Alzahra Altalib, Chunhui Li, Alessandro Perelli · 24. Juli 2026 · Medical Imaging Techniques and Applications

  Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy. It offers real time anatomical visualization with low acquisition cost and dose. Nevertheless, photon scattering and beam hindrance lead CBCT images to suffer from several artifacts. These invol…

• Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning

  Yi Xiong, Yuan-Yuan Cheng, Xiao-Ming Fu · 24. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Fine-tuning large diffusion models for new domains or styles involves a trade-off: improving target-specific generation often degrades the pretrained model's broad generative capability. Existing full and parameter-efficient fine-tuning methods typically handle this trade-off only implicitly. In thi…

• StrideDiffusion: Accelerating Diffusion Models for Time-series Generation

  Du Yin, Estrid He, Juli\'an Jer\'onimo Ba\~nuelos, Yang Yang, Feng Hu, Yuchen Luo, Hao Xue, Stephan Sigg, Flora Salim · 24. Juli 2026 · Time Series Analysis and Forecasting

  Diffusion models have become competitive generators for time series, but their practical use is limited by the large number of sequential denoising steps required at inference time. Existing fast samplers typically use fixed or generic timestep schedules, overlooking a distinctive property of time-s…