GenAI4ELab/papercli-papers huggingface dataset:
Reverse Personalization
Authors: Han-Wei Kung; Tuomas Varanka; Nicu Sebe
Venue: WACV 2026 (main)

Abstract: Recent text-to-image diffusion models have demonstrated remarkable generation of realistic facial images conditioned on textual prompts and human identities, enabling creating personalized facial imagery. However, existing prompt-based methods for removing or modifying identity-specific features rely either on the subject being well-represented in the pre-trained model or require model fine-tuning for specific identities. In this work, we analyze the identity generation process and introduce a reverse personalization framework for face anonymization. Our approach leverages conditional diffusion inversion, allowing direct manipulation of images without using text prompts. To generalize beyond subjects in the model's training data, we incorporate an identity-guided conditioning branch. Unlike prior anonymization methods, which lack control over facial attributes, our framework supports attribute-controllable anonymization. We demonstrate that our method achieves a state-of-the-art balance between identity removal, attribute preservation, and image quality. Source code and data are available at https://github.com/hanweikung/reverse-personalization
HumanBench: Two Heads, No Legs, But Mostly Human, the State of Generative Capabilities in T2I Models
Authors: Anubhooti Jain; Mayank Vatsa; Richa Singh
Venue: WACV 2026 (main)

Abstract: Despite rapid advances, text-to-image (T2I) models still falter in generating anatomically coherent and semantically grounded humans. We introduce HumanBench, a large-scale (35K-image), privacy-friendly benchmark that rigorously evaluates T2I models across four axes: template consistency, spatial reasoning, action understanding, and texture recognition. To quantify alignment, we propose two novel metrics--Agreement and Distinction--capturing both fidelity to prompts and semantic contrast with counterfactuals and negations.Evaluating six leading models, we uncover persistent failures including disfigurements, species leakage, texture-object mismatches, and counting errors, especially under compound prompts. A complementary human study reveals that image realism and correctness degrade with prompt complexity, validating our automated assessments. HumanBench offers the first comprehensive audit of human-centric T2I generation, setting a new standard for benchmarking anatomical accuracy, compositional reasoning, and trustworthiness in generative models.
SmokeBench: Evaluating Multimodal Large Language Models for Wildfire Smoke Detection
Authors: Tianye Qi; Weihao Li; Nick Barnes
Venue: WACV 2026 (main)

Abstract: Wildfire smoke is transparent, amorphous, and often visually confounded with clouds, making early-stage detection particularly challenging. In this work, we introduce a benchmark, called SmokeBench, to evaluate the ability of multimodal large language models (MLLMs) to recognize and localize wildfire smoke in images. The benchmark consists of four tasks: (1) smoke classification, (2) tile-based smoke localization, (3) grid-based smoke localization, and (4) smoke detection. We evaluate several MLLMs, including Idefics2, Qwen2.5-VL, InternVL3, Unified-IO 2, Grounding DINO, GPT-4o, and Gemini-2.5 Pro. Our results show that while some models can classify the presence of smoke when it covers a large area, all models struggle with accurate localization, especially in the early stages. Further analysis reveals that smoke volume is strongly correlated with model performance, whereas contrast plays a comparatively minor role. These findings highlight critical limitations of current MLLMs for safety-critical wildfire monitoring and underscore the need for methods that improve early-stage smoke localization.
ObjectCore - Efficient Few-shot Logical Anomaly Detection using Object Representations
Authors: Matic Fučka; Vitjan Zavrtanik; Danijel Skočaj
Venue: WACV 2026 (main)

Abstract: Anomaly Detection is an important problem in industrial processes. Two new subfields have recently emerged: logical anomaly detection and few-shot anomaly detection. The combined task, few-shot logical anomaly detection, has proven exceptionally difficult and highly important for industrial processes. Few-shot methods use suboptimal representations to model composition information necessary for detecting logical anomalies, and previous full-shot methods require a large training set. To solve both problems, we propose ObjectCore, a few-shot logical anomaly detection model that captures the composition information from only a few images without any category-specific information. The composition information of an image is modelled as a collection of object representations. Logical anomalies are detected using bipartite matching between object representations in the test image and object representations in the most similar support image. ObjectCore significantly improves over state-of-the-art methods on two standard benchmarks for few-shot logical anomaly detection, MVTec LOCO and CAD-SD, attaining an image-level AUROC of 80.8% and 96.5%, respectively, in the 4-shot setting. Code: https://github.com/MaticFuc/ObjectCore
CLIP-IT: CLIP-based Pairing of Histology Images with Privileged Textual Information
Authors: Banafsheh Karimian; Giulia Avanzato; Soufiane Belharbi; Alexis Guichemerre; Luke McCaffrey; Mohammadhadi Shateri; Eric Granger
Venue: WACV 2026 (main)

Abstract: Multimodal learning has shown promise in medical imaging, combining complementary modalities like images and text. Vision-language models (VLMs) capture rich diagnostic cues but often require large paired datasets and prompt or text-based inference. Their practicality is therefore limited due to annotation cost, privacy, and compute demands. Unpaired external text, like pathology reports, can still provide complementary diagnostic cues if semantically relevant content is retrievable per image. To address this, we introduce CLIP-IT, a novel framework that relies on rich unpaired text reports. Specifically, CLIP-IT uses a CLIP model pre-trained on histology image-text pairs from a separate dataset to retrieve the most relevant unpaired textual report for each image in the downstream unimodal dataset. These reports, sourced from the same disease domain and tissue type, form pseudo-pairs that reflect shared clinical semantics rather than exact alignment. Knowledge from these texts is distilled into the vision model during training, while LoRA-based adaptation mitigates the semantic gap between unaligned modalities. At inference, only the vision model is used, maintaining low overhead while still benefiting from multimodal training without requiring paired data in the downstream dataset. Experiments show that CLIP-IT consistently improves classification accuracy over both unimodal and multimodal CLIP-based baselines in most cases, without requiring paired annotations per dataset or incurring additional inference-time complexity.
UniTabBank: A Large Scale Multi-Lingual, Multi-Layout, Multi-Type, Multi-Format Dataset for Table Detection
Authors: Ajoy Mondal; Saumya Mundra; Avijit Dasgupta; C.V. Jawahar
Venue: WACV 2026 (main)

Abstract: Tables play a key role in conveying structured data across documents. Accurate table detection is crucial for downstream tasks like structure recognition and information extraction. However, current datasets lack diversity in format, language, and layout, limiting real-world generalization. This underscores the need for well-annotated datasets that are multi-lingual, layout-diverse, document-agnostic and format-rich. To address these limitations, we introduce UniTabBank, a large scale, diverse table detection dataset designed to reflect realistic use cases. UniTabBank is characterized by five key attributes: (i) Multi-Lingual --- supporting 28 languages (including Arabic, English, Hindi, etc.); (ii) Multi-Layout --- encompassing both single-column and multi-column documents; (iii) Multi-Type --- covering a wide range of document genres such as annual reports, books, newspapers, and magazines; (iv) Multi-Format --- comprising scanned documents, photographed pages, and PDFs; and finally (v) Scale and Annotation Quality --- consists of 55,443 document page images with 81,179 accurately annotated table instances, offering scale and annotation precision. Additionally, we introduce UniTabDet, a YOLO-based model for table detection, which outperforms state-of-the-arts on eight out of nine table detection benchmarks. Evaluated in a zero-shot setting on four layout analysis benchmarks, UniTabDet also shows strong generalization across diverse documents without additional fine-tuning. The UniTabBank dataset and models are avaialble here.
Temporal Object Captioning for Street Scene Videos from LiDAR Tracks
Authors: Vignesh Gopinathan; Urs Zimmermann; Michael Arnold; Matthias Rottmann
Venue: WACV 2026 (main)

Abstract: Video captioning models have seen notable advancements in recent years, especially with regard to their ability to capture temporal information. While many research efforts have focused on architectural advancements, such as temporal attention mechanisms, there remains a notable gap in understanding how models capture and utilize temporal semantics for effective temporal feature extraction, especially in the context of Advanced Driver Assistance Systems. We propose an automated LiDAR-based captioning procedure that focuses on the temporal dynamics of traffic participants. Our approach uses a rule-based system to extract essential details such as lane position and relative motion from object tracks, followed by a template-based caption generation. Our findings show that training SwinBERT, a video captioning model, using only front camera images and supervised with our template-based captions, specifically designed to encapsulate fine-grained temporal behavior, leads to improved temporal understanding consistently across three datasets. In conclusion, our results clearly demonstrate that integrating LiDAR-based caption supervision significantly enhances temporal understanding, effectively addressing and reducing the inherent visual/static biases prevalent in current state-of-the-art model architectures.
MDUNet: Multimodal Decoding UNet for Passive Occluder-Aided Non-line-of-sight 3D Imaging
Authors: Fadlullah Raji; John Murray-Bruce
Venue: WACV 2026 (main)

Abstract: A conventional camera captures an image of a directly visible scene by measuring the light intensity (and color) arriving at each pixel of its image sensor from a corresponding scene patch. Accordingly, conventional photography treats the measured light as being solely informative of the directly visible scene. Recent research has shown that subtle variations in measured light intensity can carry information about scenes outside the camera's line of sight. A subset of these methods exploits preexisting obstructions---i.e., occluders---which cast barely perceptible yet highly informative soft shadows onto the observed planar surface. Whereas most prior works assume that exploitable occluders are either partly or wholly known or almost planar, a recent work blended a trained diffusion-based sampler to reconstruct the hidden occluding structures in 3D with a transverse 2D radiosity map of all other hidden non-occluding structures. This work proposes a fully trained novel multipath decoding UNet (MDUNet) architecture, in which the multimodal, multipath decoder parallels recent physics-based methods that achieve success by explicitly separating the representations and reconstructions of occluding and non-occluding hidden scene structures. However, by sharing a latent feature representation between the occluding and non-occluding structures, MDUNet couples their reconstruction pathways. Empirical results show that MDUNet improves inference times by over 100x compared to a state-of-the-art diffusion-based method and by 1000x compared to an iterative optimization-based method, while also improving reconstruction quality. In addition, MDUNet is trained solely on simulation data but generalizes to real experimental data, maintaining accuracy and stability even as ambient illumination increases.
Being Positive about Negative Queries: Exclusion Aware Multimodal Retrieval using Disentangled Representations
Authors: Prachi Jha; Sumit Bhatia; Srikanta Bedathur
Venue: WACV 2026 (main)

Abstract: The handling of exclusion in multimodal retrieval remains an underexplored challenge with significant implications for the accuracy and reliability of information retrieval systems. Although existing approaches have advanced multimodal understanding, they typically lack mechanisms to process exclusion explicitly. To address this, we propose a novel model ExclMM that leverages disentangled representations to effectively handle exclusion in multimodal retrieval. Our approach enables precise differentiation between the presence and absence of specific elements in an image, outperforming existing methods. To rigorously evaluate our model, we construct a dataset, ExcluCOCO that pairs exclusion-based queries with ground truth images, sourced from MSCOCO. This dataset serves as a robust benchmark for assessing exclusion comprehension in multimodal contexts. By explicitly incorporating exclusion, our work advances multimodal retrieval by introducing both a model tailored for exclusion-aware retrieval and a benchmark to facilitate future research in this domain.
Gaussian Swaying: Surface-Based Framework for Aerodynamic Simulation with 3D Gaussians
Authors: Hongru Yan; Xiang Zhang; Zeyuan Chen; Fangyin Wei; Zhuowen Tu
Venue: WACV 2026 (main)

Abstract: Branches swaying in the breeze, flags rippling in the wind, and boats rocking on the water all show how aerodynamics shape natural motion -- an effect crucial for realism in vision and graphics. In this paper, we present Gaussian Swaying, a surface-based framework for aerodynamic simulation using 3D Gaussians. Unlike mesh-based methods that require costly meshing, or particle-based approaches that rely on discrete positional data, Gaussian Swaying models surfaces continuously with 3D Gaussians, enabling efficient and fine-grained aerodynamic interaction. Our framework unifies simulation and rendering on the same representation: Gaussian patches, which support force computation for dynamics while simultaneously providing normals for lightweight shading. Comprehensive experiments on both synthetic and real-world datasets across multiple metrics demonstrate that Gaussian Swaying achieves state-of-the-art performance and efficiency, offering a scalable approach for realistic aerodynamic scene simulation.
Enhancing Vision Language Corruption Robustness using Cross-Distribution & Prompted Denoisers
Authors: Sameer Shafayet Latif; Sadab Shiper; K. M. Rahiduzzaman Kiran; Md Farhan Ishmam; Md Azam Hossain; Abu Raihan Mostofa Kamal; Md Hamjajul Ashmafee
Venue: WACV 2026 (main)

Abstract: While the current generation of Vision Language Models (VLMs) has excelled in ideal conditions, their performance drops significantly when exposed to realistic multimodal corruptions, such as blurry images and grammatically incorrect text. Our work addresses this by establishing a novel multimodal corruption and denoising benchmark, VLSRB, with a rich suite of 18 visual and 18 textual corruption functions, to evaluate the system robustness of VLMs. To enhance robustness, we employ: (i) cross-distribution visual denoisers, inspired by the Mixture of Experts (MoE) architecture, and (ii) a prompted zero-shot textual denoiser. Our experiments reveal an overall accuracy gain of up to 5.5%, while revealing the vulnerability of models to specific corruptions and their over-reliance on the textual modality. We envision that the behavioral insights from our benchmark will help in developing robust VLM systems. Our code is available at: https://github.com/farhanishmam/VLMDenoising.
FALCONEye: Finding Answers and Localizing Content in ONE-hour-long videos with multi-modal LLMs
Authors: Carlos Plou; Cesar Borja; Ruben Martinez-Cantin; Ana C. Murillo
Venue: WACV 2026 (main)

Abstract: Finding information in hour-long videos is a challenging task even for top-performing Vision Language Models (VLMs), as encoding visual content quickly exceeds available context windows. To tackle this challenge, we present FALCONEye, a novel video agent based on a training-free, model-agnostic meta-architecture composed of a VLM and a Large Language Model (LLM). FALCONEye answers open-ended questions using an exploration-based search algorithm guided by calibrated confidence from the VLM's answers. We also introduce FALCON-Bench, a benchmark that extends question answering problem to Video Answer Search--requiring models to return both the answer and its supporting temporal window for open-ended questions in hour-long videos. With just a 7B VLM and a lightweight LLM, FALCONEye outscores all open-source 7B VLMs and comparable agents in FALCONBench. It further demonstrates its generalization capability in MLVU benchmark with shorter videos and different tasks, surpassing GPT-4o on single-detail tasks while slashing inference cost by roughly an order of magnitude.
WALDO: Where Unseen Model-based 6D Pose Estimation Meets Occlusion
Authors: Sajjad Pakdamansavoji; Yintao Ma; Amir Rasouli; Tongtong Cao
Venue: WACV 2026 (main)

Abstract: Accurate 6D object pose estimation is vital for robotics, augmented reality, and scene understanding. For seen objects, high accuracy is often attainable via per-object fine-tuning but generalizing to unseen objects remains a challenge. To address this problem, past arts assume access to CAD models at test time and typically follow a multi-stage pipeline to estimate poses: detect and segment the object, propose an initial pose, and then refine it. Under occlusion, however, the early-stage of such pipelines are prone to errors, which can propagate through the sequential processing, and consequently degrade the performance. To remedy this shortcoming, we propose four novel extensions to model-based 6D pose estimation methods: (i) a dynamic non-uniform dense sampling strategy that focuses computation on visible regions, reducing occlusion-induced errors; (ii) a multi-hypothesis inference mechanism that retains several confidence-ranked pose candidates, mitigating brittle single-path failures; (iii) iterative refinement to progressively improve pose accuracy; and (iv) series of occlusion-focused training augmentations that strengthen robustness and generalization. Furthermore, we propose a new weighted by visibility metric for evaluation under occlusion to minimize the bias in the existing protocols. Via extensive empirical evaluations, we show that our proposed approach achieves more than 5% improvement in accuracy on ICBIN and more than 2% on BOP dataset benchmarks, while achieving approximately 3 times faster inference.
Enabling High-Quality In-the-Wild Imaging from Severely Aberrated Metalens Bursts
Authors: Debabrata Mandal; Zhihan Peng; Yujie Wang; Praneeth Chakravarthula
Venue: WACV 2026 (main)

Abstract: We tackle the challenge of robust, in-the-wild imaging using ultra-thin nanophotonic metalens cameras. Meta-lenses, composed of planar arrays of nanoscale scatterers, promise dramatic reductions in size and weight compared to conventional refractive optics. However, severe chromatic aberration, pronounced light scattering, narrow spectral bandwidth, and low light efficiency continue to limit their practical adoption. In this work, we present an end-to-end solution for in-the-wild imaging that pairs a metalens over 12000x thinner than conventional optics with a bespoke multi-image restoration framework optimized for practical metalens cameras. Our method centers on a lightweight convolutional network paired with a memory-efficient burst fusion algorithm that adaptively corrects noise, saturation clipping, and lens-induced distortions across rapid sequences of extremely degraded metalens captures. Extensive experiments on diverse, real-world handheld captures demonstrate that our approach consistently outperforms existing burst-mode and single-image restoration techniques. These results point toward a practical route for deploying metalens-based cameras in everyday imaging applications.
CORA: Consistency-Guided Semi-Supervised Framework for Reasoning Segmentation
Authors: Prantik Howlader; Hoang Nguyen-Canh; Srijan Das; Jingyi Xu; Hieu Le; Dimitris Samaras
Venue: WACV 2026 (main)

Abstract: Reasoning segmentation seeks pixel-accurate masks for targets referenced by complex, often implicit instructions, requiring context-dependent reasoning over the scene. Recent multimodal language models have advanced instruction following segmentation, yet generalization remains limited. The key bottleneck is the high cost of curating diverse, high-quality pixel annotations paired with rich linguistic supervision leading to brittle performance under distribution shift. Therefore, we present CORA, a semi-supervised reasoning segmentation framework that jointly learns from limited labeled data and a large corpus of unlabeled images. CORA introduces three main components: 1) conditional visual instructions that encode spatial and contextual relationships between objects; 2) a noisy pseudo-label filter based on the consistency of Multimodal LLM's outputs across semantically equivalent queries; and 3) a token-level contrastive alignment between labeled and pseudo-labeled samples to enhance feature consistency. These components enable CORA to perform robust reasoning segmentation with minimal supervision, outperforming existing baselines under constrained annotation settings. CORA achieves state-of-the-art results, requiring as few as 100 labeled images on Cityscapes, a benchmark dataset for urban scene understanding, surpassing the baseline by +2.3%. Similarly, CORA improves performance by +2.4% with only 180 labeled images on PanNuke, a histopathology dataset.
Gradient-Free Classifier Guidance for Diffusion Model Sampling
Authors: Rahul Shenoy; Zhihong Pan; Kaushik Balakrishnan; Qiseng Cheng; Yongmoon Jeon; Heejune Yang; Jaewon Kim
Venue: WACV 2026 (main)

Abstract: Unguided sampling in diffusion models is known to generate images of wide variations, albeit with a trade-off in image fidelity. Guided sampling methods, such as classifier guidance (CG) and classifier-free guidance (CFG), focus on sampling in well-learned high-probability regions to generate images of high fidelity, but each has its limitations. CG is computationally expensive due to the costly classifier gradient descent process, while CFG, being gradient-free, is more efficient but compromises class label alignment compared to CG. In this work, we introduce Gradient-Free Classifier Guidance (GFCG), a novel method utilizing a pre-trained classifier solely in inference mode, entirely avoiding gradient calculations. GFCG introduces two innovative mechanisms: (1) adaptive reference class selection, dynamically determining an appropriate undesired reference class; and (2) adaptive guidance scaling, dynamically adjusting the guidance strength based on classifier confidence during sampling. Experiments on both class-conditioned and text-to-image generation demonstrate that the proposed GFCG method consistently improves class prediction accuracy while preserving diversity. We also show that GFCG is complementary to other guided sampling methods. When combined with Autoguidance (ATG), GFCG attains record performance on ImageNet 512x512, with a FD_DINOv2 of 23.39, while maintaining a high Precision of 94.0% compared to ATG's 89.9%.