WACV 2026 论文列表

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

We introduce Universal Neural Architecture Space (UniNAS), a generic search space for neural architecture search (NAS) which unifies convolutional networks, transformers, and their hybrid architectures under a single, flexible framework. Our approach enables discovery of novel architectures as well as analyzing existing architectures in a common framework. We also propose a new search algorithm that allows traversing the proposed search space, and demonstrate that the space contains interesting architectures, which, when using identical training setup, outperform state-of-the-art hand-crafted architectures. Finally, a unified toolkit including a standardized training and evaluation protocol is introduced to foster reproducibility and enable fair comparison in NAS research. Overall, this work opens a pathway towards systematically exploring the full spectrum of neural architectures with a unified graph-based NAS perspective. The source code is publicly available at \smallhttps://www.github.com/ondratybl/UniNAS .

Online handwriting generation (OHG) enhances handwriting recognition models by synthesizing diverse, human-like samples. However, existing OHG methods struggle to generate unseen characters, particularly in glyph-based languages like Chinese, limiting their real-world applicability. In this paper, we introduce our method for OHG, where the writer's style and the characters generated during testing are unseen during training. To tackle this challenge, we propose a Dual-branch Network with Adaptation (DNA), which comprises an adaptive style branch and an adaptive content branch. The style branch learns stroke attributes such as writing direction, spacing, placement, and flow to generate realistic handwriting. Meanwhile, the content branch is designed to generalize effectively to unseen characters by decomposing character content into structural information and texture details, extracted via local and global encoders, respectively. Extensive experiments demonstrate that our DNA model is well-suited for the unseen OHG setting, achieving state-of-the-art performance.

Out-of-distribution (OOD) detection is crucial for deploying robust and reliable machine-learning systems in open-world settings. Despite steady advances in OOD detectors, their interplay with modern training pipelines that maximize in-distribution (ID) accuracy and generalization remains under-explored.We investigate this link through a comprehensive empirical study.Fixing the architecture to the widely adopted ResNet-50, we benchmark 21 post-hoc, state-of-the-art OOD detection methods across 54 ImageNet-trained models obtained via diverse training strategies and evaluate them on eight OOD test sets.Contrary to the common assumption that higher ID accuracy implies better OOD detection performance, we uncover a non-monotonic relationship: OOD performance initially improves with accuracy but declines once advanced training recipes push accuracy beyond the baseline.Moreover, we observe a strong interdependence between training strategy, detector choice, and resulting OOD performance, indicating that no single method is universally optimal.

Despite decades of research on data collection and model architectures, current gaze estimation models encounter significant challenges in generalizing across diverse data domains. Recent advances in self-supervised pre-training have demonstrated remarkable generalization across various vision tasks. However, their effectiveness in gaze estimation remains unexplored. We propose UniGaze, which for the first time leverages large-scale in-the-wild facial datasets for gaze estimation through self-supervised pre-training. Through systematic investigation, we clarify critical factors essential for effective pre-training in gaze estimation. Our experiments reveal that self-supervised approaches designed for semantic tasks fail when applied to gaze estimation, while our carefully designed pre-training pipeline consistently improves cross-domain performance. Through comprehensive experiments of the challenging cross-dataset evaluations and novel protocols, including leave-one-dataset-out and joint-dataset settings, we demonstrate that UniGaze significantly improves generalization across multiple data domains while minimizing reliance on costly labeled data. Source code and model are available at https://github.com/ut-vision/UniGaze.

We propose PoseGaussian, a pose-guided Gaussian Splatting framework for high-fidelity human novel view synthesis. Human body pose serves a dual purpose in our design: as a structural prior, it is fused with a color encoder to refine depth estimation; as a temporal cue, it is processed by a dedicated pose encoder to enhance temporal consistency across frames. These components are integrated into a fully differentiable, end-to-end trainable pipeline. Unlike prior works that use pose only as a condition or for warping, PoseGaussian embeds pose signals into both geometric and temporal stages to improve robustness and generalization. It is specifically designed to address challenges inherent in dynamic human scenes, such as articulated motion and severe self-occlusion. Notably, our framework achieves real-time rendering at 100 FPS, maintaining the efficiency of standard Gaussian Splatting pipelines. We validate our approach on ZJU-MoCap, THuman2.0, and in-house datasets, demonstrating state-of-the-art performance in perceptual quality and structural accuracy (PSNR 30.86, SSIM 0.979, LPIPS 0.028).

Image sharing on social media has become routine but poses serious privacy risks, as users may unknowingly expose sensitive information. This necessitates an image privacy advisor that assigns personalized privacy risk scores, helping users decide whether to share images publicly or not. However, centralized training of such models risks user data exposure and loss of ownership, as data must be uploaded to a central server. To safeguard user privacy, we adopt Federated Learning (FL), which enables collaborative model training without sharing raw data. Despite its advantages, FL faces challenges such as data heterogeneity from diverse user privacy preferences, limited annotations per user, and communication overhead. To address these issues, we propose CFedDC, a personalized FL algorithm combined with PIONet, a mobile friendly model with 14.5x fewer trainable parameters and 93.09% lower memory footprint than centralized baselines. CFedDC mitigates data heterogeneity through clustering and cluster aware regularization with stability, and tackles data scarcity using a daisy-chaining knowledge transfer mechanism. Comprehensive experimental evaluations demonstrate that our proposed method achieves well-aligned personalized user privacy scores, outperforming existing centralized and FL-based image privacy models.