GenAI4ELab/papercli-papers huggingface dataset:
ClusterMine: Robust Label-Free Visual Out-Of-Distribution Detection via Concept Mining from Text Corpora
Authors: Nikolas Adaloglou; Diana Petrusheva; Mohamed Asker; Felix Michels; Markus Kollmann
Venue: WACV 2026 (main)

Abstract: Large-scale visual out-of-distribution (OOD) detection has witnessed remarkable progress by leveraging vision-language models such as CLIP. However, a significant limitation of current methods is their reliance on a pre-defined set of in-distribution (ID) ground-truth label names (positives). These fixed label names can be unavailable, unreliable at scale, or become less relevant due to in-distribution shifts after deployment. Towards truly unsupervised OOD detection, we utilize widely available text corpora for positive label mining, bypassing the need for positives. In this paper, we utilize widely available text corpora for positive label mining under a general concept mining paradigm. Within this framework, we propose ClusterMine, a novel method for mining positive labels. ClusterMine is the first method to achieve state-of-the-art OOD detection performance without access to positive labels. It extracts positive concepts from a large text corpus by combining visual-only sample consistency (via clustering) and zero-shot image-text consistency. Our experimental study reveals that ClusterMine is scalable across a plethora of CLIP models and achieves state-of-the-art robustness to covariate in-distribution shifts. The code is available at https://github.com/HHU-MMBS/clustermine_wacv_official.
FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair
Authors: Arya Fayyazi; Mehdi Kamal; Massoud Pedram
Venue: WACV 2026 (main)

Abstract: We introduce FAIR-SIGHT, an innovative post-hoc framework designed to ensure fairness in computer vision systems by combining conformal prediction with a dynamic output repair mechanism. Our approach calculates a fairness-aware non-conformity score that simultaneously assesses prediction errors and fairness violations. Using conformal prediction, we establish an adaptive threshold that provides rigorous finite-sample, distribution-free guarantees. When the non-conformity score for a new image exceeds the calibrated threshold, FAIR-SIGHT implements targeted corrective adjustments, such as logit shifts for classification and confidence recalibration for detection, to reduce both group and individual fairness disparities, all without the need for retraining or having access to internal model parameters. Comprehensive theoretical analysis validates our method's error control and convergence properties. At the same time, extensive empirical evaluations on benchmark datasets show that FAIR-SIGHT significantly reduces fairness disparities while preserving high predictive performance.
T2LF: LLM-Guided Multimodal Diffusion for Text-to-Light Field Synthesis
Authors: Soyoung Yoon; Namhyuk Ahn; In Kyu Park
Venue: WACV 2026 (main)

Abstract: We present a novel text-driven approach for light field (LF) synthesis. Existing methods typically generate LFs from given images, requiring users to find reference images, which makes it difficult to construct the desired scene directly and limits scene diversity. Moreover, existing methods are mainly designed for limited baselines from training datasets, making it difficult to implement various viewpoint changes and consequently limiting the flexibility of motion. In contrast, our method directly synthesizes LFs from user-provided text descriptions by leveraging the scene understanding capabilities of a multi-modal large language model (LLM) and the generative power of a diffusion model. Given a text prompt describing the desired LF, the multimodal LLM extracts relevant information for LF synthesis, which then guides a diffusion model to produce diverse scenes and motions. This approach enables LF synthesis even with a pre-trained model not initially designed for this purpose, requiring only minimal fine-tuning. The proposed framework enables visually diverse LF synthesis with only text input. Experimental results demonstrate that the synthesized LFs exhibit geometric consistency and achieve advanced synthesis quality compared to existing methods.
LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset
Authors: Manjushree Aithal; Rosaura G VidalMata; Manikandtan Kartha; Gong Chen; Eashan Adhikarla; Lucas Nedel Kirsten; Zhicheng Fu; Nikhil Ambha Madhusudhana; Joseph V. Nasti
Venue: WACV 2026 (main)

Abstract: Low-light image enhancement is crucial for a myriad of applications, from night vision and surveillance, to autonomous driving. However, due to the inherent limitations that come in hand with capturing images in low-illumination environments, the task of enhancing such scenes still presents a formidable challenge. To advance research in this field, we introduce our Low Exposure Night Vision (LENVIZ) Dataset, a comprehensive multi-exposure benchmark dataset for low-light image enhancement comprising of over 230K frames showcasing 24K real-world indoor and outdoor, with-and-without human, scenes. Captured using 3 different camera sensors, LENVIZ offers a wide range of lighting conditions, noise levels, and scene complexities, making it the largest publicly available up to 4K resolution benchmark in the field. LENVIZ includes high quality human-generated ground truth, for which each multiexposure low-light scene has been meticulously curated and edited by expert photographers to ensure optimal image quality. Furthermore, we also conduct a comprehensive analysis of current state-of-the-art low-light image enhancement techniques on our dataset and highlight potential areas of improvement.
Fine-grained Defocus Blur Control for Generative Image Models
Authors: Ayush Shrivastava; Connelly Barnes; Xuaner Zhang; Lingzhi Zhang; Andrew Owens; Sohrab Amirghodsi; Eli Shechtman
Venue: WACV 2026 (main)

Abstract: Current text-to-image diffusion models excel at generating diverse, high-quality images, yet they struggle to incorporate fine-grained camera metadata such as precise aperture settings. In this work, we introduce a novel text-to-image diffusion framework that leverages camera metadata, or EXIF data, which is often embedded in image files, with an emphasis on generating controllable lens blur. Our method mimics the physical image formation process by first generating an all-in-focus image, estimating its monocular depth, predicting a plausible focus distance with a novel focus distance transformer, and then forming a defocused image with an existing differentiable lens blur model. Gradients flow backwards through this whole process, allowing us to learn without explicit supervision to generate defocus effects based on content elements and the provided EXIF data. At inference time, this enables precise control over defocus effects while preserving scene contents, which is not achievable with existing diffusion models. Experimental results demonstrate that our model enables superior fine-grained control without altering the depicted scene.
MARS: a Multimodal Alignment and Ranking System for Few-Shot Segmentation
Authors: Nico Catalano; Stefano Samele; Paolo Pertino; Matteo Matteucci
Venue: WACV 2026 (main)

Abstract: Few Shot Segmentation aims to segment novel object classes given only a handful of labeled examples, enabling rapid adaptation with minimal supervision.Current literature crucially lacks a selection method that goes beyond visual similarity between the query and example images, leading to suboptimal predictions. We present MARS, a plug-and-play ranking system that leverages multimodal cues to filter and merge mask proposals robustly. Starting from a set of mask predictions for a single query image, we score, filter, and merge them to improve results. Proposals are evaluated using multimodal scores computed at local and global levels. Extensive experiments on COCO-20i, Pascal-5i, LVIS-92i, and FSS-1000 demonstrate that integrating all four scoring components is crucial for robust ranking, validating our contribution.As MARS can be effortlessly integrated with various mask proposal systems, we deploy it across a wide range of top-performer methods and achieve new state-of-the-art results on multiple existing benchmarks. Code will be available upon acceptance.
Training-free Conditional Image Embedding Framework Leveraging Large Vision Language Models
Authors: Masayuki Kawarada; Kosuke Yamada; Antonio Tejero-de-Pablos; Naoto Inoue
Venue: WACV 2026 (main)

Abstract: Conditional image embeddings are feature representations that focus on specific aspects of an image indicated by a given textual condition (e.g., color, genre), which has been a challenging problem. Although recent vision foundation models, such as CLIP, offer rich representations of images, they are not designed to focus on a specified condition. In this paper, we propose DIOR, a method that leverages a large vision-language model (LVLM) to generate conditional image embeddings. DIOR is a training-free approach that prompts the LVLM to describe an image with a single word related to a given condition. The hidden state vector of the LVLM's last token is then extracted as the conditional image embedding. DIOR provides a versatile solution that can be applied to any image and condition without additional training or task-specific priors. Comprehensive experimental results on conditional image similarity tasks demonstrate that DIOR outperforms existing training-free baselines, including CLIP. Furthermore, DIOR achieves superior performance compared to methods that require additional training across multiple settings.
DreamMakeup: Face Makeup Customization using Latent Diffusion Models
Authors: Geon Yeong Park; Inhwa Han; Serin Yang; Yeobin Hong; Seongmin Jeong; Heechan Jeon; Myeongjin Goh; Sung Won Yi; Jin Nam; Jong Chul Ye
Venue: WACV 2026 (main)

Abstract: The exponential growth of the global makeup market has paralleled advancements in virtual makeup simulation technology. Despite the progress led by GANs, their application still encounters significant challenges, including training instability and limited customization capabilities. Addressing these challenges, we introduce DreamMakup: a novel training-free Diffusion model based Makeup Customization method, leveraging the inherent advantages of diffusion models for superior controllability and precise real-image editing. DreamMakeup employs early-stopped DDIM inversion to preserve the facial structure and identity while enabling extensive customization through various conditioning inputs such as reference images, specific RGB colors, and textual descriptions. Our model demonstrates notable improvements over existing GAN-based and recent diffusion-based frameworks: improved customization, color-matching capabilities, identity preservation and compatibility with textual descriptions or LLMs with affordable computational costs.
AirLock+: Scaling UAV-to-Satellite Image Registration for Target Geolocalization and Geospatial Augmented Reality
Authors: Zhiyun Deng; Austin Case; Luis Sentis
Venue: WACV 2026 (main)

Abstract: This paper introduces AirLock+, an end-to-end vision system for scalable UAV-to-satellite image registration, enabling two key downstream tasks: (i) precise target geolocalization in geodetic coordinates and (ii) geospatial augmented reality to elevate situation awareness. AirLock+ comprises three modules: A predictive tracker first localizes targets in UAV image frames, while a cross-view image matcher generates robust UAV-to-satellite homographies that withstand severe domain gaps, outdated satellite imagery, and generalize to unseen environments without finetuning. The resulting pixel-to-world correspondences enable target pixel coordinates to be mapped into geodetic space, yielding continuous trajectory estimates and supporting geospatial augmentation of UAV video feeds. Our system achieves an average target localization error of 20.23 m across 7.8 km real-world trajectories, demonstrating robustness in high-altitude, oblique-view conditions where existing methods typically fail.
Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection
Authors: Seyedehanita Madani; Vishal M. Patel
Venue: WACV 2026 (main)

Abstract: Remote sensing change detection is often complicated by spatial misalignment between image pairs, especially when observations are separated by long temporal gaps such as seasonal or multi-year intervals. Conventional CNN- and transformer-based methods perform well on aligned data, but their reliance on perfect co-registration limits their applicability in practice. Existing approaches that integrate registration and change detection generally demand task-specific training and transfer poorly across domains. We present a lightweight, modular pipeline that strengthens robustness without retraining the underlying change detection models. The framework combines rapid per-image LoRA adaptation with a compact flow refinement module trained under supervision. To mitigate large appearance differences, we generate intermediate morphing frames via a diffusion-based semantic interpolator. Consecutive frames are aligned using a registration backbone (e.g., RoMa), and the composed flows are further corrected through a residual refinement network. The refined flow is then applied to co-register the original image pairs, enabling more reliable downstream change detection. Extensive experiments on LEVIR-CD, DSIFN-CD, and WHU-CD demonstrate that the proposed pipeline significantly improves both registration accuracy and change detection performance, especially in scenarios with substantial spatial and temporal variations.
General and Domain-Specific Zero-shot Detection of Generated Images via Conditional Likelihood
Authors: Roy Betser; Omer Hofman; Roman Vainshtein; Guy Gilboa
Venue: WACV 2026 (main)

Abstract: The rapid advancement of generative models, particularly diffusion-based methods, has significantly improved the realism of synthetic images. As new generative models continuously emerge, detecting generated images remains a critical challenge. While fully supervised, and few-shot methods have been proposed, maintaining an updated dataset is time-consuming and challenging. Consequently, zero-shot methods have gained increasing attention in recent years. We find that existing zero-shot methods often struggle to adapt to specific image domains, such as artistic images, limiting their real-world applicability. In this work, we introduce CLIDE, a novel zero-shot detection method based on conditional likelihood approximation. Our approach computes likelihoods conditioned on real images, enabling adaptation across diverse image domains. We extensively evaluate CLIDE, demonstrating SOTA performance on a large-scale general dataset and significantly outperform existing methods in domain-specific cases. These results demonstrate the robustness of our method and underscore the need of broad, domain-aware generalization for the AI-generated image detection task. Code is available at https://tinyurl.com/clide-detector.
Multi-Modal Soccer Scene Analysis with Masked Pre-Training
Authors: Marc Peral; Guillem Capellera; Luis Ferraz; Antonio Rubio; Antonio Agudo
Venue: WACV 2026 (main)

Abstract: In this work we propose a multi-modal architecture for analyzing soccer scenes from tactical camera footage, with a focus on three core tasks: ball trajectory inference, ball state classification, and ball possessor identification. To this end, our solution integrates three distinct input modalities (player trajectories, player types and image crops of individual players) into a unified framework that processes spatial and temporal dynamics using a cascade of sociotemporal transformer blocks. Unlike prior methods, which rely heavily on accurate ball tracking or handcrafted heuristics, our approach infers the ball trajectory without direct access to its past or future positions, and robustly identifies the ball state and ball possessor under noisy or occluded conditions from real top league matches. We also introduce CropDrop, a modality-specific masking pre-training strategy that prevents over-reliance on image features and encourages the model to rely on cross-modal patterns during pre-training. We show the effectiveness of our approach on a large-scale dataset providing substantial improvements over state-of-the-art baselines in all tasks. Our results highlight the benefits of combining structured and visual cues in a transformer-based architecture, and the importance of realistic masking strategies in multi-modal learning.
Learning Group Actions In Disentangled Latent Image Representations
Authors: Farhana Hossain Swarnali; Miaomiao Zhang; Tonmoy Hossain
Venue: WACV 2026 (main)

Abstract: Modeling group actions on latent representations enables controllable transformations of high-dimensional image data. Prior works applying group-theoretic priors or modeling transformations typically operate in the high-dimensional data space, where group actions apply uniformly across the entire input, making it difficult to disentangle the subspace that varies under transformations. While latent-space methods offer greater flexibility, they still require manual partitioning of latent variables into equivariant and invariant subspaces, limiting the ability to robustly learn and operate group actions within the representation space. To address this, we introduce a novel end-to-end framework that for the first time learns group actions on latent image manifolds, automatically discovering transformation-relevant structures without manual intervention. Our method uses learnable binary masks with straight-through estimation to dynamically partition latent representations into transformation-sensitive and invariant components. We formulate this within a unified optimization framework that jointly learns latent disentanglement and group transformation mappings. The framework can be seamlessly integrated with any standard encoder-decoder architecture. We validate our approach on five 2D/3D image datasets, demonstrating its ability to automatically learn disentangled latent factors for group actions in diverse data, while downstream classification tasks confirm the effectiveness of the learned representations.
Evaluating the Capability of Video Question Generation for Expert Knowledge Elicitation
Authors: Huaying Zhang; Atsushi Hashimoto; Tosho Hirasawa
Venue: WACV 2026 (main)

Abstract: Skilled human interviewers can extract valuable information from experts. This raises a fundamental question: what makes some questions more effective than others? To address this, a quantitative evaluation of question-generation models is essential. Video question generation (VQG) is a topic for video question answering (VideoQA), where questions are generated for given answers. Their evaluation typically focuses on the ability to answer questions, rather than the quality of generated questions. In contrast, we focus on the question quality in eliciting unseen knowledge from human experts. For a continuous improvement of VQG models, we propose a protocol that evaluates the ability by simulating question-answering communication with experts using a question-to-answer retrieval. We obtain the retriever by constructing a novel dataset, EgoExoAsk, which comprises 27,666 QA pairs generated from Ego-Exo4D's expert commentary annotation. The EgoExoAsk training set is used to obtain the retriever, and the benchmark is constructed on the validation set with Ego-Exo4D video segments. Experimental results demonstrate our metric reasonably aligns with question generation settings: models accessing richer context are evaluated better, supporting that our protocol works as intended. The EgoExoAsk dataset is available in https://github.com/omron-sinicx/VQG4ExpertKnowledge.
DiRe: Diversity-promoting Regularization for Dataset Condensation
Authors: Saumyaranjan Mohanty; Aravind Reddy; Konda Reddy Mopuri
Venue: WACV 2026 (main)

Abstract: In Dataset Condensation, the goal is to synthesize a small dataset that replicates the training utility of a large original dataset. Existing condensation methods synthesize datasets with significant redundancy, so there is a dire need to reduce redundancy and improve the diversity of the synthesized datasets. To tackle this, we propose an intuitive Diversity Regularizer (DiRe) composed of cosine similarity and Euclidean distance, which can be applied off-the-shelf to various state-of-the-art condensation methods. Through extensive experiments, we demonstrate that the addition of our regularizer improves state-of-the-art condensation methods on various benchmark datasets from CIFAR-10 to ImageNet-1K with respect to generalization and diversity metrics.
PredMapNet: Future and Historical Reasoning for Consistent Online HD Vectorized Map Construction
Authors: Bo Lang; Nirav Savaliya; Zhihao Zheng; Jinglun Feng; Zheng-Hang Yeh; Mooi Choo Chuah
Venue: WACV 2026 (main)

Abstract: High-definition (HD) maps are crucial to autonomous driving, providing structured representations of road elements to support navigation and planning. However, existing query-based methods often employ random query initialization and depend on implicit temporal modeling, which lead to temporal inconsistencies and instabilities during the construction of a global map. To overcome these challenges, we introduce a novel end-to-end framework for consistent online HD vectorized map construction, which jointly performs map instance tracking and short-term prediction. First, we propose a Semantic-Aware Query Generator that initializes queries with spatially aligned semantic masks to capture scene-level context globally. Next, we design a History Rasterized Map Memory to store fine-grained instance-level maps for each tracked instance, enabling explicit historical priors. A History-Map Guidance Module then integrates rasterized map information into track queries, improving temporal continuity. Finally, we propose a Short-Term Future Guidance module to forecast the immediate motion of map instances based on the stored history trajectories. These predicted future locations serve as hints for tracked instances to further avoid implausible predictions and keep temporal consistency. Extensive experiments on the nuScenes and Argoverse2 datasets demonstrate that our proposed method outperforms state-of-the-art (SOTA) methods with good efficiency.
MM-TS: Multi-Modal Temperature and Margin Schedules for Contrastive Learning with Long-Tail Data
Authors: Siarhei Sheludzko; Dhimitrios Duka; Bernt Schiele; Hilde Kuehne; Anna Kukleva
Venue: WACV 2026 (main)

Abstract: Contrastive learning has become a fundamental approach in both uni-modal and multi-modal frameworks. This learning paradigm pulls positive pairs of samples closer while pushing negatives apart. In the uni-modal setting (e.g., image-based learning), previous research has shown that the strength of these forces can be controlled through the temperature parameter.In this work, we propose Multi-Modal Temperature and Margin Schedules, extending the concept of uni-modal temperature scheduling to multi-modal contrastive learning. Our method dynamically adjusts the temperature in the contrastive loss during training, modulating the attraction and repulsion forces in the multi-modal setting.Additionally, recognizing that standard multi-modal datasets often follow imbalanced, long-tail distributions, we adapt the temperature based on the local distribution of each training sample. Specifically, samples from dense clusters are assigned a higher temperature to better preserve their semantic structure.Furthermore, we demonstrate that temperature scheduling can be effectively integrated within a max-margin framework, thereby unifying the two predominant approaches in multi-modal contrastive learning: InfoNCE loss and max-margin objective. We evaluate our approach on four widely used image- and video-language datasets, Flickr30K, MSCOCO, EPIC-KITCHENS-100, and YouCook2, and show that our dynamic temperature and margin schedules improve performance and lead to new state-of-the-art results in the field.