GenAI4ELab/papercli-papers huggingface dataset:
Splatter Layout: Geometry-embedded 3D Reconstruction via Surface Unfolding
Authors: Bryan Heryanto; Tackgeun You; Chanwoo Kim; Hwasup Lim
Venue: WACV 2026 (main)

Abstract: We propose Splatter Layout, a single-view feedforward 3D reconstruction method that jointly predicts Gaussian Splats, mesh, and point cloud in unfolded surface layout aligned with the input image. Unlike prior approaches, which suffer from inconsistent surface representations, our unfolded layout extends reconstruction to invisible regions by predicting parameters from visible neighbors and placing them near adjacent counterparts. To achieve this, we supervise the pipeline using data generated from an unfolding network, ensuring bijective mappings and input-view alignment. Since the pipeline is layout-agnostic, it can be readily extended to diverse object categories, including humans and vehicles. Without modifying the underlying architecture, Splatter Layout substantially improves the geometric fidelity of Splatter Images. Its unfolded layout yields a coherent and interpretable feature organization, resolving prior inconsistencies while establishing dense correspondences with a template mesh for tasks such as animation and appearance editing.
Optimal Transport for Rectified Flow Image Editing: Unifying Inversion-Based and Direct Methods
Authors: Marian Lupaşcu; Mihai Sorin Stupariu
Venue: WACV 2026 (main)

Abstract: Image editing in rectified flow models remains challenging due to the fundamental trade-off between reconstruction fidelity and editing flexibility. While inversion-based methods suffer from trajectory deviation, recent inversion-free approaches like FlowEdit offer direct editing pathways but can benefit from additional guidance to improve structure preservation. In this work, we demonstrate that optimal transport theory provides a unified framework for improving both paradigms in rectified flow editing. We introduce a zero-shot transport-guided inversion framework that leverages optimal transport during the reverse diffusion process, and extend optimal transport principles to enhance inversion-free methods through transport-optimized velocity field corrections. Incorporating transport-based guidance can effectively balance reconstruction accuracy and editing controllability across different rectified flow editing approaches. For inversion-based editing, our method achieves high-fidelity reconstruction with LPIPS scores of 0.001 and SSIM of 0.992 on face editing benchmarks, observing 7.8% to 12.9% improvements over RF-Inversion on LSUN datasets. For inversion-free editing with FlowEdit on FLUX and Stable Diffusion 3, we demonstrate consistent improvements in semantic consistency and structure preservation across diverse editing scenarios. Our semantic face editing experiments show an 11.2% improvement in identity preservation and enhanced perceptual quality. The unified optimal transport framework produces visually compelling edits with superior detail preservation across both inversion-based and direct editing paradigms. Code is available for RF-Inversion and FlowEdit at https://github.com/marianlupascu/OT-RF
Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model Adaptation
Authors: Xiwen Chen; Wenhui Zhu; Peijie Qiu; Hao Wang; Huayu Li; Haiyu Wu; Xuanzhao Dong; Aristeidis Sotiras; Yalin Wang; Abolfazl Razi
Venue: WACV 2026 (main)

Abstract: Vision-language models (VLMs) such as CLIP demonstrate strong performance but struggle when adapted to downstream tasks. Prompt learning has emerged as an efficient and effective strategy to adapt VLMs while preserving their pre-trained knowledge. However, existing methods still lead to overfitting and degrade zero-shot generalization. To address this challenge, we propose an optimal transport (OT)-guided prompt learning framework that mitigates forgetting by preserving the structural consistency of feature distributions between pre-trained and fine-tuned models. Unlike conventional point-wise constraints, OT naturally captures cross-instance relationships and expands the feasible parameter space for prompt tuning, allowing a better trade-off between adaptation and generalization. Our approach enforces joint constraints on both vision and text representations, ensuring a holistic feature alignment. Extensive experiments on benchmark datasets demonstrate that our simple yet effective method can outperform existing prompt learning strategies in base-to-novel generalization, cross-dataset evaluation, and domain generalization without additional augmentation or ensemble techniques.
Restora-Flow: Mask-Guided Image Restoration with Flow Matching
Authors: Arnela Hadzic; Franz Thaler; Lea Bogensperger; Simon Johannes Joham; Martin Urschler
Venue: WACV 2026 (main)

Abstract: Flow matching has emerged as a promising generative approach that addresses the lengthy sampling times associated with state-of-the-art diffusion models and enables a more flexible trajectory design, while maintaining high-quality image generation. This capability makes it suitable as a generative prior for image restoration tasks. Although current methods leveraging flow models have shown promising results in restoration, some still suffer from long processing times or produce over-smoothed results. To address these challenges, we introduce Restora-Flow, a training-free method that guides flow matching sampling by a degradation mask and incorporates a trajectory correction mechanism to enforce consistency with degraded inputs. We evaluate our approach on both natural and medical datasets across several image restoration tasks involving a mask-based degradation, i.e., inpainting, super-resolution and denoising. We show superior perceptual quality and processing time compared to diffusion and flow matching-based reference methods. Code is available at https://github.com/imigraz/Restora-Flow.
Unsupervised Segmentation by Diffusing, Walking and Cutting
Authors: Daniela Ivanova; Marco Aversa; Paul Henderson; John Williamson
Venue: WACV 2026 (main)

Abstract: We propose a zero-shot unsupervised image segmentation method by utilising self-attention activations extracted from Stable Diffusion. We demonstrate that self-attention can directly be interpreted as transition probabilities in a Markov random walk between image patches. This property enables us to modulate multi-hop relationships through matrix exponentiation, which captures k-step transitions between patches. We then construct a graph representation based on self-attention feature similarity and apply Normalised Cuts to cluster them. We quantitatively analyse the effects of incorporating multi-node paths when constructing the NCuts adjacency matrix, showing that higher-order transitions enhance hierarchical relationships in the proposed segmentations. Finally, we describe an approach to automatically determine the NCut threshold criterion, avoiding the need to manually tune it. Our approach surpasses all existing methods for zero-shot unsupervised segmentation based on pre-trained diffusion models features, achieving state-of-the-art results on COCO-Stuff-27, Cityscapes and ADE20K.
RobuMTL: Enhancing Multi-Task Learning Robustness Against Weather Conditions
Authors: Tasneem Shaffee; Sherief Reda
Venue: WACV 2026 (main)

Abstract: Robust Multi-Task Learning (MTL) is crucial for autonomous systems operating in real-world environments, where adverse weather conditions can severely degrade model performance and reliability. In this paper, we introduce RobuMTL, a novel architecture designed to adaptively address visual degradation by dynamically selecting task-specific hierarchical Low-Rank Adaptation (LoRA) modules and a LoRA expert squad based on input perturbations in a mixture-of-experts fashion. Our framework enables adaptive specialization based on input characteristics, improving robustness across diverse real-world conditions. To validate our approach, we evaluated it on the PASCAL and NYUD-v2 datasets and compared it against single-task models, standard MTL baselines, and state-of-the-art methods. On the PASCAL benchmark, RobuMTL delivers a +2.8% average relative improvement under single perturbations and up to +44.4% under mixed weather conditions compared to the MTL baseline. On NYUD-v2, RobuMTL achieves a +9.7% average relative improvement across tasks. The code will be available at GitHub.
SCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization
Authors: Bowen Yuan; Yuxia Fu; Zijian Wang; Yadan Luo; Zi Huang
Venue: WACV 2026 (main)

Abstract: Dataset Condensation (DC) aims to obtain a condensed dataset that allows models trained on the condensed dataset to achieve performance comparable to those trained on the full dataset. Recent DC approaches increasingly focus on encoding knowledge into realistic images with soft labeling, for their scalability to ImageNet-scale datasets and strong capability of cross-domain generalization. However, this strong performance comes at a substantial storage cost which could significantly exceed the storage cost of the original dataset. We argue that the three key properties to alleviate this performance-storage dilemma are informativeness, discriminativeness, and compressibility of the condensed data. Towards this end, this paper proposes a Soft label compression-centric dataset condensation framework using COding RatE (SCORE). SCORE formulates dataset condensation as a min-max optimization problem, which aims to balance the three key properties from an information-theoretic perspective. In particular, we theoretically demonstrate that our coding rate-inspired objective function is submodular, and its optimization naturally enforces low-rank structure in the soft label set corresponding to each condensed data. Extensive experiments on large-scale datasets, including ImageNet-1K and Tiny-ImageNet, demonstrate that SCORE outperforms existing methods in most cases. Even with 30x compression of soft labels, performance decreases by only 5.5% and 2.7% for ImageNet-1K with IPC 10 and 50, respectively.
ICONIC-444: A 3.1-Million-Image Dataset for OOD Detection Research
Authors: Gerhard Krumpl; Henning Avenhaus; Horst Possegger
Venue: WACV 2026 (main)

Abstract: Current progress in out-of-distribution (OOD) detection is limited by the lack of large, high-quality datasets with clearly defined OOD categories across varying difficulty levels (near- to far-OOD) that support both fine- and coarse-grained computer vision tasks.To address this limitation, we introduce ICONIC-444 (Image Classification and OOD Detection with Numerous Intricate Complexities), a large-scale image dataset containing over 3.1 million RGB images spanning 444 classes tailored for OOD detection research.Captured with a prototype industrial sorting machine, ICONIC-444 closely mimics real-world tasks.It complements existing datasets by offering structured, diverse data suited for rigorous OOD evaluation across a spectrum of task complexities. We define four reference tasks within ICONIC-444 to benchmark and advance OOD detection research and provide baseline results for 22 state-of-the-art post-hoc OOD detection methods.
BAFIS: Dataset + Framework to Assess Occupational Bias and Human Preference in Modern Text-to-image Models
Authors: Thomas Klassert; Adrian Ulges; Biying Fu
Venue: WACV 2026 (main)

Abstract: Generative artificial intelligence has the potential to improve productivity and transform the production of creative content. However, existing research indicates that image generation models are significantly influenced by biases. This work investigates the inherent biases and language-induced biases present in text-to-image models within the context of occupation-related image generation, complementing established metrics with human preference feedback. We present a comprehensive evaluation of five current text-to-image models: Midjourney v6.1, Stable Diffusion 3 Medium, DALL-E 3, Playground v2.5, and FLUX.1-dev , focusing on gender and ethnicity bias, image quality, and prompt alignment. To facilitate this evaluation, we developed the "Battle-Arena for Fair Image Synthesis" (BAFIS), a platform designed to collect human feedback on bias in generated images. Furthermore, we created a dataset comprising 21,140 synthetic images generated using multilingual prompts, which serves as a basis for our analysis. We further place our results within a broader social context by comparing them to official statistics from the German Federal Employment Agency. Our findings reveal systematic biases in text-to-image models, with established evaluation metrics in partial correlation with subjective user ratings. Thus, our research emphasizes the need for including human preferences to develop fairer and more inclusive text-to-image models. Code and dataset are public here.
Cosine Similarity is Almost All You Need (for Prototypical-Part Models)
Authors: Luke Moffett; Frank Willard; Maximillian Machado; Emmanuel Mokel; Jon Donnelly; Zhicheng Guo; Adam Costarino; Julia Yang; Giyoung Kim; Alina Jade Barnett; Cynthia Rudin
Venue: WACV 2026 (main)

Abstract: Prototypical-part networks are a popular interpretable alternative to black-box deep learning models for computer vision because of their faithful, prototype-based self-explanations.However, in practice, they have proven difficult to train because they are highly sensitive to hyperparameter tuning and difficult to comprehend because they contain a large number of prototypes.We show that replacing l_2 distance with an angular prototype similarity in the original ProtoPNet greatly improves robustness to hyperparameter selection and is sufficient to produce accuracy and sparsity competitive with state-of-the-art on many backbones and datasets.We also show cosine similarity leads to superior accuracy for five different ProtoPNet architectures (ProtoPNet, TesNet, Deformable ProtoPNet, ProtoTree, and ST-ProtoPNet).Finally, we demonstrate ProtoPNet with cosine similarity produces better semantics than l_2: prototypes from cosine models score better on prototype quality metrics and are perceived as more similar 3:2 in a user study.
Towards Photorealistic Style Transfer with Multimodal Guidance and Robustness to Content Images in Arbitrary Styles
Authors: Ruikai Zhou; Yating Liu; Yi Xu
Venue: WACV 2026 (main)

Abstract: Existing photorealistic style transfer methods are broadly categorized into two groups: image-guided and text-guided approaches. The image-guided paradigm requires a reference style image, which is superior when the target style is difficult to define precisely with text. Unfortunately, such references are not always available in practical scenarios. In contrast, the text-guided paradigm offers greater flexibility. However, existing text-guided methods often fail to preserve the details of content images or perform poorly when content images deviate from normal style. In this paper, we present a novel multimodal-guided photorealistic style transfer framework, supporting flexible switching and fusion of both modalities while ensuring robust performance across content images in arbitrary styles. Specifically, we adopt a two-stage pipeline. First, the Style Removal Module removes the original style from the content image. Then, the Style Injection Module applies stylization based on the style guidance (image, text, or their fusion). To make the text-guided branch compatible with this pipeline, we propose the Image-Assisted Textual Style Injection (IATSI) strategy. Additionally, we design a Dual-Residual Adaptive MLP (DRA-MLP), which exhibits strong color mapping capability and avoids spatial distortions. Extensive experiments show that our method achieves state-of-the-art (SOTA) performance in both image-guided and text-guided settings. Moreover, we innovatively implement multimodal fusion-guided photorealistic style transfer, achieving promising results.
Decomposition Sampling for Efficient Region Annotations in Active Learning
Authors: Jingna Qiu; Frauke Wilm; Mathias Öttl; Jonas Utz; Maja Schlereth; Moritz Schillinger; Marc Aubreville; Katharina Breininger
Venue: WACV 2026 (main)

Abstract: Active learning improves annotation efficiency by selecting the most informative samples for annotation and model training. While most prior work has focused on selecting informative images for classification tasks, we investigate the more challenging setting of dense prediction, where annotations are more costly and time-intensive, especially in medical imaging. Region-level annotation has been shown to be more efficient than image-level annotation for these tasks. However, existing methods for representative annotation region selection suffer from high computational and memory costs, irrelevant region choices, and heavy reliance on uncertainty sampling. We propose decomposition sampling (DECOMP), a new active learning sampling strategy that addresses these limitations. It enhances annotation diversity by decomposing images into class-specific components using pseudo-labels and sampling regions from each class. Class-wise predictive confidence further guides the sampling process, ensuring that difficult classes receive additional annotations. Across ROI classification, 2-D segmentation, and 3-D segmentation, DECOMP consistently surpasses baseline methods by better sampling minority-class regions and boosting performance on these challenging classes. Code is in https://github.com/JingnaQiu/DECOMP.git.
Codebook Knowledge with Mamba-Transformer For Low-Light Image Enhancement
Authors: Runhua Deng; Aiwen Jiang; Long Peng; Qiuhai Yan
Venue: WACV 2026 (main)

Abstract: Low-light image enhancement is a critical task which aims to improve the quality of images captured in bad lighting conditions, and contribute to more robust and reliable computer vision systems. Existing methods failed to account for multifaceted and intertwined degradations typically encountered in low-light scenarios. In this paper, we have reconsider the application of vector-quantized codebook in low-light image enhancement task as a domain adaptation paradigm and proposed an effective method called CodeMTNet to solve aforementioned issues. Specifically, we leverage codebook learning from collections of norm-light images to provide unified high-quality knowledge guidance.We have further developed two learning schemes, namely Domain Adaptation Encoder with implicit neural representation regularization across multiple scales, and hybrid Mamba-Transformer blocks for nearest neighbor matching, to tackle distribution mismatch between features of low-quality low-light images and high-quality normal-light images. Additionally, to solve structural information loss during codebook retrieval, we have introduced a controllable feature fusion modules for well texture detail preservation. Experiments conducted on public datasets have demonstrated that CodeMTNet consistently outperforms many state-of-the-art methods and restore images better in line with human perception. Related source codes and pretrained parameters are in https://github.com/YunHDan/CodeMTNet.git.
Illuminating Darkness: Learning to Enhance Low-light Images In-the-Wild
Authors: S. M. A. Sharif; Abdur Rehman; Zain Ul Abidin; Fayaz Ali Dharejo; Radu Timofte; Rizwan Ali Naqvi
Venue: WACV 2026 (main)

Abstract: Single-shot low-light image enhancement (SLLIE) remains challenging due to the limited availability of diverse, real-world paired datasets. To bridge this gap, we introduce the Low-Light Smartphone Dataset (LSD), a large-scale, high-resolution (4K+) dataset collected in the wild across a wide range of challenging lighting conditions (0.1-200 lux). LSD contains 6,425 precisely aligned low and normal-light image pairs, selected from over 8,000 dynamic indoor and outdoor scenes through multi-frame acquisition and expert evaluation. To evaluate generalization and aesthetic quality, we collect 2,117 unpaired low-light images from previously unseen devices. To fully exploit LSD, we propose TFFormer, a hybrid model that encodes luminance and chrominance (LC) separately to reduce color-structure entanglement. We further propose a cross-attention-driven joint decoder for context-aware fusion of LC representations, along with LC refinement and LC-guided supervision to significantly enhance perceptual fidelity and structural consistency. TFFormer achieves state-of-the-art results on LSD (+2.45 dB PSNR) and substantially improves downstream vision tasks, such as low-light object detection (+6.80 mAP on ExDark).
Food Image Generation on Multi-Noun Categories
Authors: Xinyue Pan; Yuhao Chen; Jiangpeng He; Fengqing Zhu
Venue: WACV 2026 (main)

Abstract: Generating realistic food images for categories with multiple nouns is surprisingly challenging. For instance, the prompt "egg noodle" may result in images that incorrectly contain both eggs and noodles as separate entities. Multi-noun food categories are common in real-world datasets and account for a large portion of entries in benchmarks such as UEC-256. These compound names often cause generative models to misinterpret the semantics, producing unintended ingredients or objects. This is due to insufficient multi-noun category related knowledge in the text encoder and misinterpretation of multi-noun relationships, leading to incorrect spatial layouts. To overcome these challenges, we propose FoCULR (Food Category Understanding and Layout Refinement) which incorporates food domain knowledge and introduces core concepts early in the generation process. Experimental results demonstrate that the integration of these techniques improves image generation performance in the food domain.
FuLLaMa: Training-free Diffusion-based Object Removal with Context Preservation
Authors: Ilke Demir; Umur Aybars Ciftci
Venue: WACV 2026 (main)

Abstract: Diffusion models have demonstrated remarkable capabilities in image inpainting tasks, yet they often struggle to maintain semantic consistency and fine-grained details when filling large masked regions. Existing approaches typically require extensive fine-tuning or are trained from scratch, still losing the context, patterns, or realism. We introduce FuLLaMa, a novel training-free framework for diffusion-based removal that preserves semantic embeddings as well as the image quality throughout the infill process. FuLLaMa enhances traditional removal algorithms with the information manifold of LVLMs and generation capability of DMs. Through Adaptive Parameter Manifold Navigation (APMN), DM is guided to generate content that harmonizes with the existing context and structure of the image, without introducing new elements. FuLLaMa achieves a high and balanced performance compared to 6 SOTA object removal algorithms, on 2 datasets, for various tasks such as large area, small object, multi-instance, and patterned removal; using 9 visual and 5 contextual evaluation metrics. We conduct several ablation studies for the system and objective design, also showcasing mask-based, language-based, and point-and-click removal applications. Our work establishes context and quality co-preservation as a fundamental principle for diffusion-based removal.
Augmenting with NeRFs: Fast Relocalization on Densified Datasets
Authors: Michael Tomadakis; Rebecca Borissova; Yuxuan Zhang; Sanjeev Koppal
Venue: WACV 2026 (main)

Abstract: We reinterpret NeRFs as a resource for extreme data augmentation to advance the field of camera relocalization. Our approach lets us automatically render a massive, densified dataset of novel views, given only sparse ground-truth viewpoints. We introduce a filtering strategy that, compared to existing novel-view-synthesis-focused relocalizers, does not rely on custom or specific NeRF backbones. This filtering strategy allows for significant spatial extrapolation within the scene, without compromising novel view quality. As a result, training a lightweight off-the-shelf vision backbone as a pose regressor on our expanded datasets significantly improves accuracy, uniquely enables relocalization of very spatially-novel views, and performs well on portable-scale hardware.