GenAI4ELab/papercli-papers huggingface dataset:
FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation
Authors: Bin Yang; Alexandru Paul Condurache
Venue: WACV 2026 (main)

Abstract: 3D scene understanding is a critical yet challenging task in autonomous driving due to the irregularity and sparsity of LiDAR data, as well as the computational demands of processing large-scale point clouds. Recent methods leverage range-view representations to enhance efficiency, but they often adopt higher azimuth resolutions to mitigate information loss during spherical projection, where only the closest point is retained for each 2D grid. However, processing wide panoramic range-view images remains inefficient and may introduce additional distortions. Our empirical analysis shows that training with multiple range images, obtained from splitting the full point cloud, improves both segmentation accuracy and computational efficiency. However, this approach also poses new challenges of exacerbated class imbalance and increase in projection artifacts. To address these, we introduce FLARES, a novel training paradigm that incorporates two tailored data augmentation techniques and a specialized post-processing method designed for multi-range settings. Extensive experiments demonstrate that FLARES is highly generalizable across different architectures, yielding 2.1%-7.9% mIoU improvements on SemanticKITTI and 1.8%-3.9% mIoU on nuScenes, while delivering over 40% speed-up in inference.
Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimation
Authors: Yimu Wang; Evelien Riddell; Adrian Chow; Sean Sedwards; Krzysztof Czarnecki
Venue: WACV 2026 (main)

Abstract: Existing vision-language model (VLM)-based methods for out-of-distribution (OOD) detection typically rely on similarity scores between input images and in-distribution (ID) text prototypes. However, the modality gap between image and text often results in high false positive rates, as OOD samples can exhibit high similarity to ID text prototypes. To mitigate the impact of this modality gap, we propose incorporating ID image prototypes along with ID text prototypes. We present theoretical and empirical evidence indicating that this approach enhances VLM-based OOD detection performance without any additional training. To further reduce the gap between image and text, we introduce a novel few-shot tuning framework, suPreMe, comprising biased prompt generation (BPG) and image-text consistency (ITC) modules. BPG enhances image-text fusion and improves generalization (prevents overfitting on training data) by conditioning ID text prototypes on the Gaussian-based estimated image domain bias; ITC reduces the modality gap by minimizing intra- and inter-modal distances. Moreover, inspired by our theoretical and empirical findings, we introduce a novel OOD score S_ GMP , leveraging uni- and cross-modal similarities. Finally, we present extensive experiments to demonstrate that suPreMe consistently outperforms existing VLM-based OOD detection methods.
FairVLM: Enhancing Fairness and Prompt Sensitivity in Vision Language Models for Medical Image Segmentation
Authors: Md Motiur Rahman; Saeka Rahman; Smriti Bhatt; Miad Faezipour
Venue: WACV 2026 (main)

Abstract: Vision-language models (VLMs) have demonstrated substantial promise in medical image segmentation by utilizing radiology reports as prompts to segment regions of interest. However, VLM deployment in clinical settings is challenged by two intertwined issues: i) demographic bias, where performance varies across demographic groups, and ii) prompt sensitivity, where semantically similar prompts yield inconsistent outputs. These challenges are interconnected; demographic underrepresentation can worsen a model's sensitivity to prompts, and prompt instability can more heavily affect certain demographic groups. In this study, we present FairVLM, a unified framework that addresses both demographic disparity and prompt sensitivity in VLMs. FairVLM integrates three key components: (1) Semantic-Retaining Counterfactual Prompting (SRCP), which generates clinically consistent and diverse prompt variations via large language models; (2) Demographic-Aware Feature Normalization (DAFN), a lightweight module that mitigates latent representation bias across demographic groups; and (3) a Fairness-Calibrated Loss (FCL) that explicitly penalizes performance disparities while encouraging prompt consistency. Extensive evaluations on the Harvard-FairSeg dataset show that FairVLM significantly improves equity-scaled segmentation. It also reduces demographic disparity (DI) by over 65% and relative performance gap (RPG) by over 60%, while maintaining or boosting overall accuracy. FairVLM is robust to prompt changes, with less than 0.5% performance drop across varied prompts, and also generalizes well on unseen datasets. These findings present FairVLM as a new state-of-the-art, robust, and adaptable framework for a fair, prompt-invariant vision-language model. Code and data are available at https://github.com/Rahman-Motiur/FairVLM.
NRGMark: Localized Watermarking for Energy Transparency in Images
Authors: Shruti Agarwal; Élie Michel; Vishal Asnani; Tania Mathern; John Collomosse
Venue: WACV 2026 (main)

Abstract: We present NRGMark, a region-based image watermarking framework to embed energy use and provenance metadata into images. Targeting composite graphic designs such as posters, NRGMark enables imperceptible watermarking of distinct visual elements each carrying independent metadata on their environmental impact, such as the energy consumption associated with generative AI (GenAI) use. NRGMark extends image watermark encoder-decoder models by incorporating an object localization network to detect and decode multiple watermarked regions within a document, even under image transformations and physical print-scan degradation. NRGMark interoperates with several watermarking techniques and the emerging C2PA open standard for media provenance to encode environmental impact metadata. We demonstrate NRGMark on both synthetic and real-world design layouts, illustrating its potential to support energy transparency in the age of GenAI.
V2XScene: Multi-View Consistent 3D Scene Simulation for Collaborative Perception
Authors: Yanfei Li; Yi Gong; Yuan Zeng
Venue: WACV 2026 (main)

Abstract: Realistic scene simulation is a promising way to improve autonomous driving. While existing diffusion-based 2D augmentation and 3D asset libraries show potential for synthesizing diverse driving scenarios, they often struggle with multi-view photorealistic rendering and consistency. These issues are particularly challenging for vehicle-to-everything (V2X) collaborative perception, since its effectiveness relies on precise geometric alignment and visual coherence across multiple viewpoints. To address these challenges, we propose V2XScene, a 3D driving scene editing framework. This framework enhances V2X collaborative perception by enabling high-quality 3D vehicle asset generation and consistent multi-view insertion. V2XScene consists of three components: a visual question answering (VQA) guided generation module for query-driven 3D vehicle asset synthesis; a 3D object mapping module for vehicle placement optimization and occlusion reasoning; and a realistic insertion module for lighting estimation and virtual vehicle insertion. Extensive experiments demonstrate that V2XScene can generate multi-view consistent and realistic driving scenes, which significantly improves V2X perception accuracy.
SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization
Authors: Lyuzhou Ye; Thanh Dat Le; Yan Huang
Venue: WACV 2026 (main)

Abstract: Accurately predicting directional radio maps is essential for wireless applications, yet prior approaches primarily focus on omnidirectional signals and typically treat transmitter localization and signal map reconstruction as separate tasks. In omnidirectional settings, predicting the maximum signal location often coincides with the transmitter position, which limits the need for explicit joint modeling. However, in directional propagation where angular effects, reflections, and building occlusions play critical roles, this assumption no longer holds. To address this gap, we propose SymNet, a unified framework that jointly predicts directional radio maps and transmitter locations from sparse signal measurements. SymNet incorporates a prediction head for transmitter localization alongside radio map reconstruction, enabling simultaneous learning of both tasks. This joint formulation leverages their complementary information and leads to consistent improvements over treating them separately. Experiments on challenging directional scenarios demonstrate that SymNet outperforms state-of-the-art baselines, achieving superior accuracy in both radio map reconstruction and transmitter localization.
Safe Vision-Language Models via Unsafe Weights Manipulation
Authors: Moreno D'incà; Elia Peruzzo; Xingqian Xu; Humphrey Shi; Nicu Sebe; Massimiliano Mancini
Venue: WACV 2026 (main)

Abstract: Vision-language models (VLMs) often inherit the biases and unsafe associations present within their large-scale training dataset. While recent approaches mitigate unsafe behaviors, their evaluation focuses on how safe the model is on unsafe inputs, ignoring potential shortcomings on safe ones. In this paper, we first revise safety evaluation by introducing SafeGround, a new set of metrics that evaluate safety at different levels of granularity. With this metric, we uncover a surprising issue of training-based methods: they make the model less safe on safe inputs. From this finding, we take a different direction and explore whether it is possible to make a model safer without training, introducing Unsafe Weights Manipulation (UWM). UWM uses a calibration set of safe and unsafe instances to compare activations between safe and unsafe content, identifying the most important parameters for processing the latter. Their values are then manipulated via negation. Experiments show that UWM achieves the best tradeoff between safety and knowledge preservation, consistently improving VLMs on unsafe queries while outperforming even training-based state-of-the-art methods on safe ones.
CONCORD: Concept-Informed Diffusion for Dataset Distillation
Authors: Jianyang Gu; Haonan Wang; Ruoxi Jia; Saeed Vahidian; Vyacheslav Kungurtsev; Wei Jiang; Yiran Chen
Venue: WACV 2026 (main)

Abstract: Dataset distillation (DD) has witnessed significant progress in creating small datasets that encapsulate rich information from large original ones. Particularly, methods based on generative priors show promising performance while maintaining computational efficiency and cross-architecture generalization. However, the generation process lacks explicit controllability for each sample. Previous distillation methods primarily match the real distribution from the perspective of the entire dataset, whereas overlooking concept completeness at the instance level. The missing or incorrectly represented object details cannot be efficiently compensated due to the constrained sample amount typical in DD settings. To this end, we propose incorporating the concept understanding of large language models (LLMs) to perform Concept-Informed Diffusion (CONCORD) for dataset distillation. Specifically, distinguishable and fine-grained concepts are retrieved based on category labels to inform the denoising process and refine essential object details. By integrating these concepts, the proposed method significantly enhances both the controllability and interpretability of the distilled image generation, without relying on pre-trained classifiers. We demonstrate the efficacy of CONCORD by achieving state-of-the-art performance on ImageNet-1K and its subsets. The code implementation is in the supplementary material.
CAPE: A CLIP-Aware Pointing Ensemble of Complementary Heatmap Cues for Embodied Reference Understanding
Authors: Fevziye Irem Eyiokur; Dogucan Yaman; Hazım Kemal Ekenel; Alexander Waibel
Venue: WACV 2026 (main)

Abstract: We address Embodied Reference Understanding, the task of predicting the object a person in the scene refers to through pointing gesture and language. This requires multimodal reasoning over text, visual pointing cues, and scene context, yet existing methods often fail to fully exploit visual disambiguation signals. We also observe that while the referent often aligns with the head-to-fingertip direction, in many cases it aligns more closely with the wrist-to-fingertip direction, making a single-line assumption overly limiting. To address this, we propose a dual-model framework, where one model learns from the head-to-fingertip direction and the other from the wrist-to-fingertip direction. We introduce a Gaussian ray heatmap representation of these lines and use them as input to provide a strong supervisory signal that encourages the model to better attend to pointing cues. To fuse their complementary strengths, we present the CLIP-Aware Pointing Ensemble module, which performs a hybrid ensemble guided by CLIP features. We further incorporate an auxiliary object center prediction head to enhance referent localization. We validate our approach on YouRefIt, achieving 75.0 mAP at 0.25 IoU, alongside state-of-the-art CLIP and C_D scores, and demonstrate its generality on unseen CAESAR and ISL Pointing, showing robust performance across benchmarks.
Learning Action Hierarchies via Hybrid Geometric Diffusion
Authors: Arjun Ramesh Kaushik; Nalini K. Ratha; Venu Govindaraju
Venue: WACV 2026 (main)

Abstract: Temporal action segmentation is a critical task in video understanding, where the goal is to assign action labels to each frame in a video. While recent advances leverage iterative refinement-based strategies, they fail to explicitly utilize the hierarchical nature of human actions. In this work, we propose HybridTAS - a novel framework that incorporates a hybrid of Euclidean and hyperbolic geometries into the denoising process of diffusion models to exploit the hierarchical structure of actions. Hyperbolic geometry naturally provides tree-like relationships between embeddings, enabling us to guide the action label denoising process in a coarse-to-fine manner: higher diffusion timesteps are influenced by abstract, high-level action categories (root nodes), while lower timesteps are refined using fine-grained action classes (leaf nodes). Extensive experiments on three benchmark datasets, GTEA, 50Salads, and Breakfast, demonstrate that our method achieves state-of-the-art performance, validating the effectiveness of hyperbolic-guided denoising for the temporal action segmentation task.
Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation
Authors: Liu He; Xiao Zeng; Yizhi Song; Albert Y. C. Chen; Lu Xia; Shashwat Verma; Sankalp Dayal; Min Sun; Cheng-Hao Kuo; Daniel Aliaga
Venue: WACV 2026 (main)

Abstract: Multimodal Large Language Models (MLLMs) struggle with accurately capturing camera-object relations, especially for object orientation, camera viewpoint, and camera shots. This stems from the fact that existing MLLMs are trained on images with limited diverse camera-object relations and corresponding textual descriptions. To address this, we propose a synthetic generation pipeline to create large-scale 3D visual instruction datasets. Our framework takes 3D assets as input and uses rendering and diffusion-based image generation models to create photorealistic images preserving precise camera-object relations. Additionally, large language models (LLMs) are used to generate text prompts for guiding visual instruction tuning and controlling image generation. We create Ultimate3D, a dataset of **240K** VQAs with precise camera-object annotations, and corresponding benchmark. MLLMs fine-tuned on our proposed dataset outperform commercial models by a large margin, achieving an average accuracy improvement of **33.4%** on camera-object relation recognition tasks. Our code, dataset, and benchmark will contribute to broad MLLM applications.
Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings
Authors: Jinyung Hong; Yearim Kim; Keun Hee Park; Sangyu Han; Nojun Kwak; Theodore P. Pavlic
Venue: WACV 2026 (main)

Abstract: Inner interpretability is a promising field aiming to uncover the internal mechanisms of AI systems through scalable, automated methods. While significant research has been conducted on large language models, limited attention has been paid to applying inner interpretability to large-scale image tasks, focusing primarily on architectural and functional levels to visualize learned concepts. In this paper, we first present a conceptual framework that supports inner interpretability and multilevel analysis for large-scale image classification tasks. Specifically, we introduce the Bi-directional Interaction between Concept and Input Embeddings (Bi-ICE) module, which facilitates interpretability across the computational, algorithmic, and implementation levels. This module enhances transparency by generating predictions based on human-understandable concepts, quantifying their contributions, and localizing them within the inputs. Finally, we showcase enhanced transparency in image classification, measuring concept contributions, and pinpointing their locations within the inputs. Our approach highlights algorithmic interpretability by demonstrating the process of concept learning and its convergence.
Transformer-Based Inpainting for Real-Time 3D Streaming in Sparse Multi-Camera Setups
Authors: Leif Van Holland; Domenic Zingsheim; Mana Takhsha; Hannah Dröge; Patrick Stotko; Markus Plack; Reinhard Klein
Venue: WACV 2026 (main)

Abstract: High-quality 3D streaming from multiple cameras is crucial for immersive experiences in many AR/VR applications. The limited number of views - often due to real-time constraints - leads to missing information and incomplete surfaces in the rendered images. Existing approaches typically rely on simple heuristics for the hole filling, which can result in inconsistencies or visual artifacts. We propose to complete the missing textures using a novel, application-targeted inpainting method independent of the underlying representation as an image-based post-processing step after the novel view rendering. The method is designed as a standalone module compatible with any calibrated multi-camera system. For this we introduce a multi-view aware, transformer-based network architecture using spatio-temporal embeddings to ensure consistency across frames while preserving fine details. Additionally, our resolution-independent design allows adaptation to different camera setups, while an adaptive patch selection strategy balances inference speed and quality, allowing real-time performance. We evaluate our approach against state-of-the-art inpainting techniques under the same real-time constraints and demonstrate that our model achieves the best trade-off between quality and speed, outperforming competitors in both image and video-based metrics.
Test-Time Adaptation for Video Highlight Detection Using Meta-Auxiliary Learning and Cross-Modality Hallucinations
Authors: Zahidul Islam; Sujoy Paul; Mrigank Rochan
Venue: WACV 2026 (main)

Abstract: Existing video highlight detection methods, although advanced, struggle to generalize well to all test videos. These methods typically employ a generic highlight detection model for each test video, which is suboptimal as it fails to account for the unique characteristics and variations of individual test videos. Such fixed models do not adapt to the diverse content, styles, or audio and visual qualities present in new, unseen test videos, leading to reduced highlight detection performance. In this paper, we propose Highlight-TTA, a test-time adaptation framework for video highlight detection that addresses this limitation by dynamically adapting the model during testing to better align with the specific characteristics of each test video, thereby improving generalization and highlight detection performance. Highlight-TTA is jointly optimized with an auxiliary task, cross-modality hallucinations, alongside the primary highlight detection task. We utilize a meta-auxiliary training scheme to enable effective adaptation through the auxiliary task while enhancing the primary task. During testing, we adapt the trained model using the auxiliary task on the test video to further enhance its highlight detection performance. Extensive experiments with three state-of-the-art highlight detection models and three benchmark datasets show that the introduction of Highlight-TTA to these models improves their performance, yielding superior results.
INRetouch: Context Aware Implicit Neural Representation for Photography Retouching
Authors: Omar Elezabi; Marcos V. Conde; Zongwei Wu; Radu Timofte
Venue: WACV 2026 (main)

Abstract: Professional photo editing remains challenging, requiring extensive knowledge of imaging pipelines and significant expertise. While recent deep learning approaches, particularly style transfer methods, have attempted to automate this process, they often struggle with output fidelity, editing control, and complex retouching capabilities. We propose a novel retouch transfer approach that learns from professional edits through before-after image pairs, enabling precise replication of complex editing operations. We develop a context-aware Implicit Neural Representation that learns to apply edits adaptively based on image content and context, and is capable of learning from a single example. Our method extracts implicit transformations from reference edits and adaptively applies them to new images. To facilitate this research direction, we introduce a comprehensive Photo Retouching Dataset comprising 100,000 high-quality images edited using over 170 professional Adobe Lightroom presets. Through extensive evaluation, we demonstrate that our approach not only surpasses existing methods in photo retouching but also enhances performance in related image reconstruction tasks like Gamut Mapping and Raw Reconstruction. By bridging the gap between professional editing capabilities and automated solutions, our work presents a significant step toward making sophisticated photo editing more accessible while maintaining high-fidelity results.
3DSceneEditor: Controllable 3D Scene Editing with Gaussian Splatting
Authors: Ziyang Yan; Yihua Shao; Minwen Liao; Siyu Chen; Nan Wang; Muyuan Lin; Jenq-Neng Hwang; Hao Zhao; Fabio Remondino; Lei Li
Venue: WACV 2026 (main)

Abstract: The creation of 3D scenes has traditionally been both labor-intensive and costly, requiring designers to meticulously configure 3D assets and environments. Recent advancements in generative AI, including text-to-3D and image-to-3D methods, have dramatically reduced the complexity and cost of this process. However, current techniques for editing complex 3D scenes continue to rely on generally interactive multi-step, 2D-to-3D projection methods and diffusion-based techniques, which often lack precision in control and hamper interactive-rate performance. In this work, we propose ***3DSceneEditor***, a fully 3D-based paradigm for interactive-rate, precise editing of intricate 3D scenes using Gaussian Splatting. Unlike conventional methods, 3DSceneEditor operates through a streamlined 3D pipeline, enabling direct Gaussian-based manipulation for efficient, high-quality edits based on input prompts. The proposed framework (i) integrates a pre-trained instance segmentation model for semantic labeling; (ii) employs a zero-shot grounding approach with CLIP to align target objects with user prompts; and (iii) applies scene modifications, such as object addition, repositioning, recoloring, replacing, and removal--directly on Gaussians. Extensive experimental results show that 3DSceneEditor surpasses existing state-of-the-art techniques in terms of both editing precision and efficiency, establishing a new benchmark for efficient and interactive 3D scene customization.