WACV 2026 论文列表

Currently, prominent Transformer architectures applied to graphs and meshes for shape analysis tasks employ traditional attention layers that heavily utilize spectral features requiring costly eigenvalue decomposition-based methods. To encode the mesh structure, these methods derive positional embeddings that heavily rely on eigenvalue decomposition-based operations, e.g. on the Laplacian matrix, or on heat-kernel signatures, which are then concatenated to the input features. This paper proposes a novel approach inspired by the explicit construction of the Hodge Laplacian operator in Discrete Exterior Calculus as a product of discrete Hodge operators and exterior derivatives. We adjust the Transformer architecture in a novel deep learning layer that utilizes the multi-head attention mechanism to approximate Hodge matrices and learn families of discrete operators that act on mesh vertices, edges and faces. Our approach results in a computationally-efficient architecture that achieves comparable performance in mesh segmentation and classification tasks, through a direct learning framework, while eliminating the need for costly eigenvalue decomposition operations or complex preprocessing operations.

We present GRAPE--Gaussian Rendering for Accelerated Pixel Enhancement, a fast, lightweight method for arbitrary-scale super-resolution (ASSR) based on 2D Gaussian splatting. Lookup-table (LUT) schemes are limited to preset scale factors and struggle with varied textures, while implicit neural representations (INRs) slow down because they require per-coordinate queries; moreover, prior Gaussian-splatting approaches rely on heavy networks or complex processing. GRAPE overcomes these limitations with a compact design in which a single point-wise layer predicts anisotropic Gaussian parameters--RGB value, rotation, scale, and offset--and a differentiable rasterizer then renders the high-resolution image in one pass. The entire model, including both encoder and decoder, contains just 1.56 M parameters and requires only 1.10 GB of GPU memory, yet achieves 69.33 FPS on Urban100 at x4 whose average image size is 985 x 798. This is more than 315x faster than GSASR, a 20.45 M-parameter model that runs at 0.22 FPS. Although GRAPE does not further improve perceptual fidelity over heavier networks, it remains competitively close, providing an attractive quality-efficiency trade-off across Set5, Set14, BSD100, DIV2K and Urban100. Consequently, GRAPE is ideal for resource-limited deployments or interactive applications that require rapid screen updates. The source code will be made publicly available at github.com/mulkkog/GRAPE.

The reliability of computer vision systems in the construction industry is critically undermined by motion blur, a complex and non-uniform degradation that conventional deblurring models with static architectures fail to address effectively. To overcome this challenge, we introduce the Spatial-Adaptive Channel Network (SAC-Net), a dynamic architecture designed specifically for this demanding environment. SAC-Net features three synergistic innovations. First, a Spatial-Adaptive Channel Module (SACM) generates content-aware spatial filters, allowing the network to adaptively focus on the most salient features for restoration. Second, our Hierarchical Feature Transfer with Wavelet (HFTW) method ensures robust propagation of core structural information by refining features in the wavelet domain, effectively suppressing noise. Finally, a Selective Feature Integration (SFI) module intelligently merges multi-scale features, combining semantic context with fine-grained detail. Evaluated on a large-scale, domain-specific construction dataset, SAC-Net significantly outperforms state-of-the-art methods, setting a new benchmark in both quantitative metrics and visual quality.

Multi-rater medical image segmentation addresses annotation ambiguities but typically requires costly multiple expert annotations per scan. We propose P-Diverse, a novel two-stage framework that minimizes the annotation needs while achieving state-of-the-art performance. Stage-I trains a modified nnU-Net with expert-specific embeddings throughout the network stages, generating personalized segmentations using as low as one annotation per scan. Stage-II freezes that network to synthesize the missing annotations and trains a diversification model that captures multi-rater variability using the available and synthesized annotations. We evaluated on the public NPC dataset and QUBIQ2021 dataset (where the current SOTA method fails), P-Diverse establishes new SOTA performance using synthetic annotations on the diversification stage, significantly reducing clinical annotation burdens. Code: https://github.com/SajedHassan/P-Diverse.

Medical scans often include patient identifiers and clinical annotations that must be removed prior to data sharing or use in downstream model training. With machine learning now central to clinical imaging analysis, reliable removal of such non-imaging artifacts is essential for preserving patient privacy, reducing bias, and improving data quality. However, this crucial curation step is frequently overlooked or addressed heuristically.We present a deep learning framework that automatically detects and removes overlaid text, markers, and other non-imaging elements from clinical scans while restoring the underlying image content. The model comprises two components: a detection module that localizes non-imaging regions, and a dual-generator architecture for unsupervised image decomposition, where one generator reconstructs the imaging content and the other produces the non-imaging components. Unlike conventional inpainting, our method bypasses explicit segmentation by leveraging explainable AI (XAI) maps from the detection module to guide artifact masking and restoration.We demonstrate robust curation performance on three datasets, one MRI and two ultrasound, for both public and private sources.Results show high visual quality (Turing-test validated) and strong quantitative scores. Importantly, training downstream classification and segmentation models with scans curated by our method substantially improves results compared to models trained on data containing overlaid annotations. In fact, our performance on various metrics (e.g., accuracy, F1 score, IoU, and Dice) is comparable to those obtained with clean, marker-free training data. Code is included with the submission.

Accurately modeling the eye is a challenging task as it exhibits refraction and reflection at the cornea, complex iris texture, and self-occlusion and shadowing due to eyelids and eyelashes. To address these challenges, we present a system for learning a hybrid relightable eye model which can be relit under near-field point lights. Our hybrid model leverages an eyeball mesh for explicitly representing the cornea surface and the reflections on it while learning the geometry and light transport of the periocular region and eye interior implicitly. To account for refraction, we explicitly handle the refraction of camera rays using Snell's law and predict the refraction of incident light rays using a neural network. Furthermore, we propose an extension of our method which enables us to relight the eye using a fringe projector to simulate structured light. Through experiments, we demonstrate that our method results in higher fidelity rendering under novel viewpoint and lighting conditions, improves learned iris geometry, and more accurately simulates structured light fringe patterns on the eye.

Despite the privacy-oriented design, federated learning (FL) remains vulnerable to privacy breaches due to the exposure of model update snapshots throughout training. To achieve robust privacy-preserving FL that protects both data and model privacy, Trusted Execution Environments (TEEs) offer a promising solution by isolating code and data within a secure memory enclave. However, the memory capacity of commonly used TEEs still constrains the training of large-scale neural networks, such as GPT, creating significant challenges within these secure enclaves and thereby limiting the full potential of TEEs in federated learning. To address this limitation, we propose Marshaled Learning, a solution to protect FL privacy for both data and model owners while enabling large neural network training within memory-constrained TEEs. To overcome memory constraints, we first partition the neural network and distribute subnetworks to clients in alignment with their TEE memory capacities. We also facilitate end-to-end training for global optimization by enabling knowledge propagation across client-side TEEs. Given the distributed nature of subnetworks, we introduce a dynamic knowledge propagation mechanism to enhance knowledge transfer in FL. This diversified propagation accelerates FL on heterogeneous data and mitigates critical straggler effects common in distributed training. We also analyze the convergence of Marshaled Learning under conditions of data heterogeneity. Our theoretical and empirical results demonstrate the effectiveness and efficiency of Marshaled Learning over existing FL algorithms in the constrained memory scenarios. Marshaled Learning outperforms the baseline methods by around 2% to 5% accuracy with much faster convergence. We also implement \proposed on commercial Azure confidential VMs to prove its feasibility and show that it incurs only a 1~3xcomputational overhead compared to non-TEE settings, validating its practicality in real-world deployments.

Generating visual instructions in a given context is essential for developing interactive world simulators. While prior works address this problem through either text-guided image manipulation or video prediction, these tasks are typically treated in isolation. This separation reveals a fundamental issue: image manipulation methods overlook how actions unfold over time, while video prediction models often ignore the intended outcomes. To this end, we propose ShowMe, a unified framework that enables both tasks by selectively activating the spatial and temporal components of video diffusion models. In addition, we introduce structure and motion consistency rewards to improve structural fidelity and temporal coherence. Notably, this unification brings dual benefits: the spatial knowledge gained through video pretraining enhances contextual consistency and realism in non-rigid image edits, while the instruction-guided manipulation stage equips the model with stronger goal-oriented reasoning for video prediction. Experiments on diverse benchmarks demonstrate that our method outperforms expert models in both instructional image and video generation, highlighting the strength of video diffusion models as a unified action-object state transformer.

Sparse annotations fundamentally constrain multimodal remote sensing: even recent state-of-the-art supervised methods such as MSFMamba are limited by the availability of labeled data, restricting their practical deployment despite architectural advances. ImageNet-pretrained models provide rich visual representations, but adapting them to heterogeneous modalities such as hyperspectral imaging (HSI) and synthetic aperture radar (SAR) without large labeled datasets remains challenging. We propose UniDiff, a parameter-efficient framework that adapts a single ImageNet-pretrained diffusion model to multiple sensing modalities using only target-domain data. UniDiff combines FiLM-based timestep-modality conditioning, parameter-efficient adaptation of approximately 5 percent of parameters, and pseudo-RGB anchoring to preserve pretrained representations and prevent catastrophic forgetting. This design enables effective feature extraction from remote sensing data under sparse annotations. Our results with two established multimodal benchmarking datasets demonstrate that unsupervised adaptation of a pretrained diffusion model effectively mitigates annotation constraints and achieves effective fusion of multimodal remotely sensed data.

Spatial transcriptomics is an emerging field that enables the identification of functional regions based on the spatial distribution of gene expression. Integrating this functional information present in transcriptomic data with structural data from histopathology images is an active research area with applications in identifying tumor substructures associated with cancer drug resistance. Current histopathology-spatial-transcriptomic region segmentation methods suffer due to either making spatial transcriptomics prominent by using histopathology features just to assist processing spatial transcriptomics data or using vanilla contrastive learning that make histopathology images prominent due to only promoting common features losing functional information. In both extremes, the model gets either lost in the noise of spatial transcriptomics or overly smoothed, losing essential information. Thus, we propose our novel architecture SENCA-st (Shared Encoder with Neighborhood Cross Attention) that preserves the features of both modalities. More importantly, it emphasizes regions that are structurally similar in histopathology but functionally different on spatial transcriptomics using cross-attention. We demonstrate the superior performance of our model that surpasses state-of-the-art methods in detecting tumor heterogeneity and tumor micro-environment regions, a clinically crucial aspect.

Specialized datasets that capture the fashion industry's rich language and styling elements can boost progress in AI-driven fashion design. We present FLORA (Fashion Language Outfit Representation for Apparel Generation), the first comprehensive dataset containing 4,330 curated pairs of fashion outfits and corresponding textual descriptions. Each description utilizes industry-specific terminology and jargon commonly used by professional fashion designers, providing precise and detailed insights into the outfits. Hence, the dataset captures the delicate features and subtle stylistic elements necessary to create high-fidelity fashion designs. We demonstrate that fine-tuning generative models on the FLORA dataset significantly enhances their capability to generate accurate and stylistically rich images from textual descriptions of fashion sketches. FLORA will catalyze the creation of advanced AI models capable of comprehending and producing subtle, stylistically rich fashion designs. It will also help fashion designers and end-users to bring their ideas to life. As a second orthogonal contribution, we introduce NeRA (Nonlinear low-rank Expressive Representation Adapter), a novel adapter architecture based on Kolmogorov-Arnold Networks (KAN). NeRA replaces traditional MLP-based LoRA adapters with learnable spline-based nonlinear transformations, enabling superior modeling of complex semantic relationships, achieving strong fidelity, faster convergence and semantic alignment. Extensive experiments and ablation studies on our proposed FLORA and LAION-5B datasets validate the superiority of NeRA over LoRA adapters. We will open-source both the FLORA dataset and our implementation code.

The appearance of ultrasound images varies across acquisition devices, causing domain shifts that degrade the performance of fixed black-box downstream inference models when reused. To mitigate this issue, it is practical to develop unpaired image translation (UIT) methods that effectively align the statistical distributions between source and target domains, particularly under the constraint of a reused inference-blackbox setting. However, existing UIT approaches often overlook class-specific semantic alignment during domain adaptation, resulting in misaligned content-class mappings that can impair diagnostic accuracy. To address this limitation, we propose UI-Styler, a novel ultrasound-specific, class-aware image style transfer framework. UI-Styler leverages a pattern-matching mechanism to transfer texture patterns embedded in the target images onto source images while preserving the source structural content. In addition, we introduce a class-aware prompting strategy guided by pseudo labels of the target domain, which enforces accurate semantic alignment with diagnostic categories. Extensive experiments on ultrasound cross-device tasks demonstrate that UI-Styler consistently outperforms existing UIT methods, achieving state-of-the-art performance in distribution distance and downstream tasks, such as classification and segmentation.

3D Gaussian Splatting (3DGS) has advanced novel view synthesis, but its densification process leads to an excessive number of Gaussian primitives, which negatively impact rendering speed and memory usage. Although many 3DGS pruning techniques have been proposed to address this issue, a comprehensive analysis is still lacking. In this paper, we are the first to categorize 3DGS pruning techniques into two approaches: scene-level importance-threshold-based pruning and pixel-level importance-rank-based pruning, defined by their scope of importance calculation (scene-level vs. pixel-level) and their pruning criteria (threshold vs. rank). Our studies reveal that while the former leads to disastrous quality drops under extreme Gaussian primitive decimation, the latter not only sustains high rendering quality but also provides a natural pruning boundary, i.e., a safeguard for Gaussian pruning. We further propose multiple pruning score functions. From our extensive studies on various pruning score functions, we discover that color similarity with blending weight is the most effective factor for identifying insignificant primitives. In our experiments, our proposed method, SafeguardGS, with the selected score function achieves the highest PSNR-per-primitive performance under extreme pruning setting, retaining only about 10% of the primitives from the original 3DGS scene, i.e., 10x compression ratio.

Fitting 3D morphable models to video is a key technique in content creation.In particular, regression-based approaches have produced fast and accurate results by matching the rendered output of the morphable model to the target image. These methods typically achieve stable performance by using orthographic projection, which removes ambiguity related to focal length and object distance. However, this simplification makes them unsuitable for close-up footage, such as that captured with head-mounted cameras.To address this limitation, we introduce a novel shrinkage parameter to the orthographic projection, enabling the incorporation of a pseudo-perspective effect while preserving the stability of the original projection. We present several techniques that allow this parameter to be integrated into existing orthographic methods with minimal changes through fine-tuning. We demonstrate the effectiveness of our modification through both quantitative and qualitative comparisons using a custom dataset recorded with head-mounted cameras.

The human face plays a central role in social communication, necessitating the use of performant computer vision tools for human-centered applications. We propose Face-LLaVA, a multimodal large language model for face-centered, in-context learning, including facial expression and attribute recognition. Additionally, Face-LLaVA is able to generate natural language descriptions that can be used for reasoning. Leveraging existing visual databases, we first developed FaceInstruct-1M, a face-centered database for instruction tuning MLLMs for face processing. We then developed a novel face-specific visual encoder powered by Face-Region Guided Cross-Attention that integrates face geometry with local visual features. We evaluated the proposed method across nine different datasets and five different face processing tasks, including facial expression recognition, action unit detection, facial attribute detection, age estimation and deepfake detection. Face-LLaVA achieves superior results compared to existing open-source MLLMs and competitive performance compared to commercial solutions. Our model output also receives a higher reasoning rating by GPT under a zero-shot setting across all the tasks. Both our dataset and model will be made public to support future advancements in social AI and foundational vision-language research.

Diffusion models have emerged as the backbone of several modern generative AI models for effective visual content generation. However, their opaque nature raises fundamental questions about which training samples are responsible for specific generations, especially in applications involving bias detection, model auditing, and dataset curation. Data attribution seeks to identify the training samples that highly influence the output of generative models, a task that becomes especially challenging when targeting fine-scale attributes for attribution. Prior work has focused on broad concepts such as global features or entire images, often overlooking the nuances of fine-grained attributes and relying on group-based strategies that dilute individual influence. We propose a novel latent distribution conditioned method DoTA for data attribution. DoTA presents an effective search space pruning technique based on the latent distribution matching between the generated and training data for effective and controlled attribution. We demonstrate the attribution effectiveness through extensive quantitative and qualitative evaluations across challenging settings such as counterfactual evaluation and robustness to adversarial attack.

Periodic human activities with implicit workflows are common in manufacturing, sports, and daily life. While short-term periodic activities--characterized by simple structures and high-contrast patterns--have been widely studied, long-term periodic workflows with low-contrast patterns remain largely underexplored. To bridge this gap, we introduce the first benchmark comprising 580 multimodal human activity sequences featuring long-term periodic workflows. The benchmark supports three evaluation tasks aligned with real-world applications: unsupervised periodic workflow detection, task completion tracking, and procedural anomaly detection. We also propose a lightweight, training-free baseline for modeling diverse periodic workflow patterns. Experiments show that: (i) our benchmark presents significant challenges to both unsupervised periodic detection methods and zero-shot approaches based on powerful large language models (LLMs); (ii) our baseline outperforms competing methods by a substantial margin in all evaluation tasks; and (iii) in real-world applications, our baseline demonstrates deployment advantages on par with traditional supervised workflow detection approaches, eliminating the need for annotation and retraining. Our project page is https://sites.google.com/view/periodicworkflow.

Effective data augmentation for domain-specific image classification must balance three competing objectives: diversity, faithfulness, and label clarity. However, current methods, including state-of-the-art diffusion models, struggle to achieve this balance and are further limited by issues such as stochastic outputs under strong transformations. We propose SGD-Mix, a novel framework that systematically reconciles these objectives. Our approach employs saliency-guided mixing to preserve foreground semantics while introducing diverse backgrounds, followed by a domain-specific fine-tuned diffusion model that refines the output to ensure high fidelity and strict label consistency. Extensive experiments across fine-grained, long-tail, fewshot, and background robustness tasks demonstrate that SGD-Mix achieves state-of-the-art performance, surpassing existing diffusion-based and non-generative methods by notable margins.

Test-time adaptation allows pretrained models to adjust to incoming data streams, addressing distribution shifts between source and target domains. However, standard methods rely on single-dimensional linear classification layers, which often fail to handle diverse and complex shifts. We propose Hierarchical Adaptive Networks with Task Vectors (Hi-Vec), which leverages multiple layers of increasing size for dynamic test-time adaptation. By decomposing the encoder's representation space into such hierarchically organized layers, Hi-Vec, in a plug-and-play manner, allows existing methods to adapt to shifts of varying complexity. Our contributions are threefold: First, we propose dynamic layer selection for automatic identification of the optimal layer for adaptation to each test batch. Second, we propose a mechanism that merges weights from the dynamic layer to other layers, ensuring all layers receive target information. Third, we propose linear layer agreement that acts as a gating function, preventing erroneous fine-tuning by adaptation on noisy batches. We rigorously evaluate the performance of Hi-Vec in challenging scenarios and on multiple target datasets, proving its strong capability to advance state-of-the-art methods. Our results show that Hi-Vec improves robustness, addresses uncertainty, and handles limited batch sizes and increased outlier rates.