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Xurui Hu, Xingqun Qi, Bingkun Yang, Chen Su, Yiwei Ru, Junhui Yin, Muyi Sun, Man Zhang

Hand Object Interaction (HOI) generation provides an efficient solution for virtual reality simulation and embodied AI deployment.Recent studies have explored instruction-driven HOI synthesis, yet they overlooked the fine-grained interactive contact and struggled with robust generalization in data-scarce scenarios. In addition, existing datasets are frequently constrained with limited object categories and monotonous motion patterns. To address these limitations, we first integrate a large-scale HOI dataset, namely HOI-X, which contains diverse objects, rich motion patterns, and part-level contact annotations. Along with HOI-X, we propose A3fford-HOI, a fine-grained and generalizable Hand Object Interaction framework with Anatomy-Aligned Affordance Disentanglement. Specifically, we define a Disentangle-2-Interact paradigm in A3fford-HOI. Considering the various interactions of distinct hand anatomical structures, in the Disentangle stage, we devise an Anatomy-Aligned Affordance Predictor, which leverages the external plausible physical priors in 3D multimodal large language models for affordance disentanglement prediction. Then, the disentangled affordance is employed into the Interact stage, i.e. Affordance Driven Interactor. The interactor integrates textual instructions, object geometric features, and the external knowledge enhanced (MLLMs) affordance via a Diffusion Transformer (DiT) for generalizable HOI generation. Extensive experiments demonstrate that A3fford-HOI maintains superior generation quality and contact plausibility for fine-grained and generalizable hand object interaction.

Zetao Li, Silin Zhou, Zheng Hu, Shimin Cai, Tao Zhou

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITS). However, traffic dynamics exhibit inherent multi-scale heterogeneity, where stable global trends are often masked by stochastic local fluctuations. Existing methods struggle to reconcile these conflicting resolutions, leading to sub-optimal forecasting. To address this, we propose the Multi-Scale Spatial-Temporal Diffusion Transformer (MS-STDT). Deviating from previous diffusion approaches that treat generation as a monolithic task, we reframe the forward diffusion process as an intrinsic temporal coarse-graining operation. By leveraging the data's multi-scale hierarchy as a structural anchor, we introduce a coarse-to-fine latent guidance strategy that enables the model to reconstruct stable global trends before refining fine-grained details. This ensures physical consistency and generative stability without requiring external labels. Extensive experiments across six real-world datasets confirm that MS-STDT performs the best, demonstrating significant improvements in predictive accuracy and zero-shot robustness against sensor failures. We provide code and data at https://github.com/ZetaoLiPhD/MS-STDT.

Yuanfei Bao, Dong Li, Jie Huang, Xingbo Wang, Xueyang Fu

Diffusion models show significant potential for low-light image enhancement. However, this task requires satisfying human perceptual preferences and content fidelity transcending simple brightness and color improvement. Existing methods rely on heuristic physical priors or incorporate perceptual metrics directly into timestep-wise training objectives. Such proxy constraints fail to provide reliable trajectory-level guidance for perceptual alignment. Furthermore, they often lead to artifacts or unnatural visual effects in this ill-posed inverse problem. To address these issues, we propose an unsupervised low-light enhancement framework based on Group Relative Policy Optimization (GRPO), which utilizes perceptual preferences to directly optimize the diffusion policy. We introduce a Sliding Window Hybrid ODE-SDE Sampling strategy that confines stochasticity to dynamic sub-intervals, thereby achieving efficient coarse-to-fine exploration and precise advantage attribution. To meet strict fidelity constraints, we construct a Synergistic Perception-Fidelity Reward and introduce an Independent Advantage Estimation strategy to mitigate signal collapse and gradient suppression in multi-objective optimization. Furthermore, we design a Temporal-Aware Dynamic Weighting Mechanism that adaptively adjusts perceptual weights across denoising stages to balance visual enhancement with structural preservation. Extensive experiments on multiple real-world benchmarks demonstrate that our method effectively improves the perceptual performance of existing models, yielding results that better align with human aesthetics.

YuBing Luo, Zekai Ji, Jia Qin, Zhihang Chen, Tengyue Guo, Pinle Qin, Rui Chai, Jianchao Zeng

Diffusion-based image compression has exhibited robust performance. However, most existing methods primarily emphasize spatial domain, causing frequency dependencies to be learned only implicitly. We revisit this problem from a frequency perspective, and observe pronounced heterogeneity between global and local frequency dependencies. Global frequency patterns describe the energy distribution over the entire image, whereas patch level frequency relations govern the coupling of local details. These two forms of dependency differ substantially in both scale and semantic level, yet most methods overlook this distinction. To address this issue, we propose a learning heterogeneous global local frequency dependencies in diffusion-based image compression which uses Fourier and Mamba jointly models both global and local frequency correlations(FMDiff). The core of FMDiff is the dual branch FMBlock. In the frequency branch, features are decomposed into magnitude and phase, then fed into Mamba separately. Its scanning mechanism captures cross frequency coupling within each patch while aggregating long range frequency context along the sequence. Magnitude phase interactive modulation is then used to explicitly restore spectral information corrupted by compression. The spatial branch provides high level semantic constraints and is fused with the frequency branch during diffusion reconstruction. Extensive experiments show that FMDiff consistently improves performance across multiple datasets.

Yunfeng Ge, Ming Jin, Yiji Zhao, Hongyan Li, Bo Du, Chang Xu, Shirui Pan

Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined with external events in other modalities such as texts. However, incorporating natural language-based external events to improve non-stationary forecasting remains largely unexplored, as most approaches still rely on a single modality, resulting in limited contextual knowledge and model underperformance. Enabling fine-grained multimodal interactions between temporal and textual data is challenged by two fundamental issues: (1) the gap in modeling interactions among discrete external events and continuous time series in a unified framework; (2) classical uniform diffusion timestep ignores event-induced non-stationary variability, leading to imbalanced denoising difficulty across diffusion stages. In this work, we propose event-aware non-stationary time series forecasting EventTSF, an autoregressive diffusion framework that integrates historical time series and textual events via step-wise diffusion. To mitigate the imbalanced denoising difficulty of uniform timestep sampling, EventTSF uses an event-aware flow-matching timestep conditioned on event semantics. Extensive experiments on 7 synthetic and real-world datasets show that EventTSF outperforms 12 non-stationary time series forecasting baselines, achieving average gains of 41.3% in probabilistic forecasting and 27.5% in deterministic forecasting across all evaluation metrics.

Kang Zhao, Ye Zhang, Bin Wang, Jianchao Zeng

Remote sensing change detection (RSCD) aims to identify changed regions in bitemporal images. However, conventional one-step modeling suffers from performance degradation caused by imaging temporal differences (e.g., illumination disturbances, seasonal variations). To address this issue, we formulate the problem as an “adversarial attack and defense” paradigm. Then, instead of traditional adversarial training, we propose a brain-inspired deep network based on the drift diffusion model (DDM) decision-making mechanism for RSCD, which is rooted in cognitive neuroscience. By simulating brain decision processes, the network enhances model robustness significantly. Specifically, we design the multi-stage reasoning unit to simulate DDM’s iterative evidence generation, stochastic representation unit with reparameterization sampling to model stochastic diffusion in decision-making, decision aggregation unit to replicate DDM’s evidence accumulation, and loss-guided unit with multi-loss joint constraints to simulate directional drift. Experimental results verify the model’s superior performance on multiple datasets and their perturbed versions.

Haokai Gao, Menghua Jiang, Xuantao Yang, Jiale Liu, Yubin Li, Yuncheng Jiang

Edge addition is commonly considered risky in Graph Anomaly Detection (GAD), as random edge addition may induce anomaly–normal connectivity. Consequently, most existing augmentation strategies focus on feature perturbation, edge removal, or subgraph sampling, leaving edge addition largely unexplored. In contrast, we empirically find that moderate and targeted structural completion among normal nodes consistently improves GAD performance, revealing guided edge addition as an overlooked yet effective augmentation dimension. Motivated by this observation, we introduce two adjacency-centered edge generation strategies with complementary mechanisms. One performs a training-free structural completion scheme via spectrum-aware Langevin dynamics, enriching graph connectivity while preserving node features. The other models the joint evolution of node features and graph structure through a stochastic differential equation–based diffusion process, producing structurally coherent and anomaly-aware complementary graphs. Extensive experiments on 12 benchmark datasets with 7 state-of-the-art GAD models demonstrate consistent and substantial improvements in both AUROC and AUPRC. Code and appendices are available at https://github.com/GaoHaokai222/LangGen-JointSDE

Junhao Zhang, Ming Kong, Zhanbin Hu, Hao Qin, Zhijie Xu, Xiaojun Zhu, Qiang Zhu

In this work, we propose a diffusion-based method for 3D-aware image composition. Previous approaches have focused on 2D-view image composition, which limits their handling of complex 3D spatial relationships. Consequently, they are not well-suited for applications requiring precise 3D object control and iterative refinement, including interior design visualization, visual effects prototyping, and virtual reality scene construction. In contrast, our method extracts 3D bounding boxes for all objects in the scene image. Users can then specify a new 3D bounding box based on existing spatial context and provide an image of the target object. Leveraging a fine-tuned diffusion model, our approach enables high-fidelity image composition while preserving the underlying 3D structure of the scene.

Abhiram Srivatsa Kadaba

Modern generative models often violate basic physical principles. Shadows drift, geometry becomes inconsistent across views, and measurement models are ignored, which limits trust in both video synthesis and computational imaging. We propose a finite time Schrödinger Bridge (SB) world model that formulates generation as entropy regularized optimal transport from a simple prior to a distribution that is consistent with both data and physics. Instead of applying consistency corrections only at the final output, the framework introduces geometric and physical structure directly along the generative path. For video, the model enforces multiview geometric constraints through reprojection and epipolar agreement, homographies, and depth guided warping. For imaging, it incorporates differentiable optical operators, including point spread function based defocus models and lightweight Fourier propagation for coherent and partially coherent settings. When camera poses are known, the model penalizes reprojection error and warp aligned photometric or feature inconsistencies. When poses are unknown, a compact motion or flow estimator encourages cycle consistent trajectories. A lightweight UNet or Vision Transformer backbone, together with a short SB horizon, maintains computational efficiency. Evaluation will measure three dimensional and temporal consistency, physics fidelity through forward simulation residuals, and overall generative quality and efficiency using FID, KID, and FVD. Comparisons will include modern video diffusion models, plug and play data consistency methods, and unconstrained SB variants. The central hypothesis is that constraining the entire generative trajectory, rather than only the final frame, can shorten sampling while improving cross view coherence and physical plausibility across diverse sensing modalities, including cameras, microscopes, and medical imaging systems.

Aditya Mehta, Swarnim Chaudhary, Pratik Narang, Jagat Sesh Challa

Modern generative and diffusion models produce highly realistic images that can mislead human perception and even sophisticated automated detection systems. Most detection methods operate in RGB space and thus analyze only three spectral channels. We propose HSI-Detect, a two-stage pipeline that reconstructs a 31-channel hyperspectral image from a standard RGB input and performs detection in the hyperspectral domain. Expanding the input representation into denser spectral bands amplifies manipulation artifacts that are often weak or invisible in the RGB domain, particularly in specific frequency bands. We evaluate HSI-Detect across FaceForensics++ dataset and show the consistent improvements over RGB-only baselines, illustrating the promise of spectral-domain mapping for Deepfake detection.

Suin Kim, Jihoon Lee, Moonsung Kang, Doheun Cha, Sangtae Ahn

Recent advances in diffusion-based models have significantly broadened their scope, extending well beyond image generation to encompass zero-shot segmentation tasks. In this work, we introduce a novel, training-free approach that harnesses both self- and cross-attention maps to achieve highly detailed hair segmentation. Our method demonstrates remarkable efficacy in producing fine-grained results without the need for additional training.

Ritika Jain, Atul Kumar, Akshay Agarwal

Although deep networks excel on RGB images, their performance degrades sharply under severe domain shifts—such as sketch recognition, where color and texture cues are missing. In this work, we propose a novel pipeline that leverages semantic cues extracted from sketches to guide the synthesis of photorealistic RGB images using diffusion-based generative models. Our framework operates by extracting two crucial cues from the input sketch: semantic captions via the BLIP model and structural outlines via Canny edge detection. These cues are then integrated using ControlNet to guide a Stable Diffusion model, ensuring the synthesized RGB image is both semantically consistent with the content and structurally faithful to the original sketch. We evaluated our synthesized images by benchmarking classification performance. We trained standard architectures (from convolutional to transformer-based) on Tiny-ImageNet subsets and tested them on sketches, their synthesized counterparts, and the original RGB images. Experimental results demonstrate that our approach produces realistic, identity-preserving images, which significantly improve classification accuracy and effectively bridge the semantic gap. While BLIP-based captioning and ControlNet-guided diffusion are established methods, our contribution lies in their integration into a unified, caption-guided pipeline that enhances sketch-to-RGB translation with improved semantic consistency. The proposed method generalizes well across architectures, providing a scalable and cost-efficient solution for sketch-based image synthesis.

Giovanni B. Esposito, Daniele Cesarini, Andrea Bartolini

We present a context-aware diffusion model for multivariate time series generation in dynamic and partially observed environments, with applications to data-center computing node's telemetry and beyond. The model integrates pretrained textual embeddings to represent feature semantics, enabling flexible, context-guided generation and improved adaptability to unseen or re-ordered input features. Built on a transformer architecture, it employs both time-wise and feature-wise masking to support missing data during training and inference. We show that the model is robust to permutations with respect to the feature dimension, mantaining stable performance in settings where input configurations vary. Empirical evaluations on HPC sensor data illustrate the model’s versatility across generation and imputation tasks. This work introduces a modular and generalizable framework for time series modeling in complex, high-dimensional systems which can serve as a digital-twin for data-center's compute node telemetry.

Ankan Deria, Dwarikanath Mahapatra, Murari Mondal, Sudipta Roy

We present a language-based noise modulation module for diffusion models that improves image color generation under textual guidance. Unlike standard approaches that inject noise uniformly, our method leverages semantic cues from text to selectively control the noise injection process, preserving local details and enhancing color accuracy even when descriptions are ambiguous or incomplete. Applied to language guided image colorization, this targeted modulation leads to more faithful and visually consistent results. The proposed module is lightweight, generalizable, and can be integrated into existing diffusion pipelines, offering a simple yet effective step toward more controllable text-to-image generation.

Eduarda Caldeira, Tahar Chettaoui, Naser Damer, Fadi Boutros

Diffusion models conditioned on identity embeddings enable the generation of synthetic face images that consistently preserve identity across multiple samples. Recent work has shown that introducing an additional negative condition through classifier-free guidance during sampling provides a mechanism to suppress undesired attributes, thus improving inter-class separability. Building on this insight, we propose a dynamic weighting scheme for the negative condition that adapts throughout the sampling trajectory. This strategy leverages the complementary strengths of positive and negative conditions at different stages of generation, leading to more diverse yet identity-consistent synthetic data.

Hyojun Ahn, Joongheon Kim

Large language models (LLMs) often generate hallucinations—fluent yet factually incorrect responses—that undermine reliability in knowledge-intensive tasks. Existing approaches for hallucination mitigation typically rely on external retrieval modules or probability heuristics, which either require additional resources or lack interpretability. In this work, we propose a diffusion-based hallucination detection framework (DHDF) that leverages U-Net denoising to reconstruct consensus answers from multiple LLM outputs. If the diffusion process exhibits spurious convergence away from factual ground truth, it provides a clear signal of hallucination. To quantify factual correctness, we incorporate TruthfulQA scores as a fact-grounded evaluation metric, distinguishing well-aligned models (high scores) from hallucination-prone models (low scores). Experimental results demonstrate that convergence dynamics under diffusion, combined with fact-grounded QA evaluation, offer an effective and interpretable pathway for hallucination detection without relying on external knowledge bases.

Mrigendra Agrawal, Yunze Xiao

We present AniTales, a system designed to generate multimodal visual novels from natural language prompts. Our system integrates large language models for story generation, diffusion models for character art, and text-to-speech for voice acting. This paper describes the system's architecture and presents findings from a pilot user study. We evaluated the system with general users (n=10) and domain experts (n=5), focusing on usability, coherence, and visual consistency. General users reported high usability (SUS: 84/100) and strong character-dialogue consistency (4.2/5), along with an average score of 82/100 for their intention to continue using the platform. These initial results suggest AniTales is a promising approach for bridging the gap between text-based AI storytelling and end-to-end multimedia content creation.

Aniket Roy

The rapid advancement of generative models has created new opportunities for addressing core challenges in computer vision, including data scarcity, image quality, and efficient personalization. My research develops principled, resource- aware methods that enable models to generalize effectively from limited supervision, adapt efficiently to new concepts, and generate high-fidelity visual content. I first address few-shot learning through augmentation-driven uncertainty- guided mixup, improving robustness in data-constrained regimes. Building on this, I propose caption-guided multi-modal augmentation techniques that enrich visual diversity while mitigating real-to-synthetic domain gaps. To enhance the quality and realism of generated images, I introduce diffusion models grounded in natural image statistics, yielding perceptually aligned outputs suitable for downstream tasks. To advance personalization, I develop parameter-efficient mechanisms for combining low-rank adapters, enabling fine-grained control over content and style without retraining. I further extend personalization to a zero-shot setting through a training-free textual-inversion-based method that customizes arbitrary objects directly within the diffusion process. Finally, I present a frequency-guided multi-LoRA fusion framework that leverages wavelet-domain cues and timestep-aware weighting for accurate, training-free concept composition. Collectively, these contributions move toward a unified vision of generative models that are efficient, adaptive, and capable of high-quality, customizable image synthesis.

Thi Kieu Khanh Ho

Time-series data, which represent the evolution of one or more variables over time, are ubiquitous across domains such as finance, medicine, industry, and security. Time-Series Anomaly Detection (TSAD) is essential for identifying irregular events such as equipment failures, fraudulent activities, and neurological disorders. Despite significant progress, TSAD remains challenging due to the complexity of time-series signals, the diversity of anomaly types, and the scarcity of high-quality labeled data. This thesis contributes: (i) the first comprehensive surveys of Graph-based TSAD (G-TSAD) and Self-Supervised Learning for Anomaly Detection (SSL-AD), showing how graph modeling and SSL proxy tasks yield robust representations for TSAD while mapping limits and future directions; (ii) EEG-CGS, a contrastive–generative SSL framework that encodes fine-grained subgraph structure without anomaly labels, improving multivariate TSAD and localizing anomalous sensors and regions; (iii) TSAD-C, which integrates graph representations with diffusion models to capture long-range temporal and spatial dependencies while explicitly handling contaminated training data; and (iv) extending TSAD beyond benchmark datasets into other impactful domains, and developing foundation models specialized for biosignals to detect novel anomalies in drug-resistant epilepsy patients.

Uttam Rao, Madhav Marathe

Bibliometric and science-of-science studies have yielded valuable insights into co-authorship and citation networks, yet most analyses rely on static datasets and limited relation types. We introduce a multi-agent AI architecture that orchestrates specialized large language model (LLM) agents (ingestion, extraction, disambiguation, integration, and analysis) to build and query a comprehensive knowledge graph. Ingestion agents unify data from diverse sources such as OpenAlex, ORCID, ROR, USPTO, and custom web scrapers. Extraction agents harness LLMs to parse unstructured text. Disambiguation agents combine rule-based heuristics with LLM reasoning to resolve ambiguous authors and institutions. Integration agents assemble and cache a provenance-rich graph. An analysis agent translates natural language questions into graph queries and interprets results. This end-to-end pipeline produces a rich graph schema spanning authors, institutions, publications, patents, grants, topics, and temporal relations. Researcher mobility and knowledge diffusion are then modeled as timed automata, where each researcher node’s institutional transitions and accumulated attributes (such as publications, collaborators, and topic expertise) enable dynamic temporal reasoning. Results show that our multi-agent, graph-based system consistently outperforms standalone LLMs and research agents on complex temporal queries, entity disambiguation accuracy, and cross-entity reasoning while maintaining competitive efficiency. These capabilities position the system as a foundation for real-time, LLM-assisted knowledge analysis platforms that can support science policy, research evaluation, and meta-scientific inquiry.