GenAI4ELab/papercli-papers huggingface dataset:
CAMP-VQA: Caption-Embedded Multimodal Perception for No-Reference Quality Assessment of Compressed Video
Authors: Xinyi Wang; Angeliki Katsenou; Junxiao Shen; David Bull
Venue: WACV 2026 (main)

Abstract: The prevalence of user-generated content (UGC) on platforms such as YouTube and TikTok has rendered no-reference (NR) perceptual video quality assessment (VQA) vital for optimizing video delivery. Nonetheless, the characteristics of non-professional acquisition and the subsequent transcoding of UGC video on sharing platforms present significant challenges for NR-VQA. Although NR-VQA models attempt to infer mean opinion scores (MOS), their modeling of subjective scores for compressed content remains limited due to the absence of fine-grained perceptual annotations of artifact types. To address these challenges, we propose CAMP-VQA, a novel NR-VQA framework that exploits the semantic understanding capabilities of large vision-language models. Our approach introduces a quality-aware prompting mechanism that integrates video metadata (e.g., resolution, frame rate, bitrate) with key fragments extracted from inter-frame variations to guide the BLIP-2 pretraining approach in generating fine-grained quality captions. A unified architecture has been designed to model perceptual quality across three dimensions: semantic alignment, temporal characteristics, and spatial characteristics. These multimodal features are extracted and fused, then regressed to video quality scores. Extensive experiments on a wide variety of UGC datasets demonstrate that our model consistently outperforms existing NR-VQA methods, achieving improved accuracy without the need for costly manual fine-grained annotations. Our method achieves the best performance in terms of average rank and linear correlation (SRCC: 0.928, PLCC: 0.938) compared to state-of-the-art methods. The source code and trained models, along with a user-friendly demo, are available at: https://github.com/xinyiW915/CAMP-VQA.
HyperPose: Hyper-pose Embeddings for 3D-Aware Generative Models with Self-Supervised Disentangling of Pose and Scene
Authors: Mijeong Kim; Namgi Kim; Bohyung Han
Venue: WACV 2026 (main)

Abstract: We propose a novel framework for training 3D-aware Generative Adversarial Networks (GANs) from a collection of 2D images, effectively learning both image distribution and 3D geometric configurations without relying on strong 3D priors such as camera poses, depth information, or target-specific 3D models. To achieve these goals, we introduce hyper-pose embeddings alongside a novel pose disentanglement technique that effectively separates pose and scene information. This crucial disentanglement helps the generative model overcome the inherent conflict between learning photorealism and accurate 3D geometry. Furthermore, we propose soft contrastive learning to robustly handle the continuous nature of camera poses and a non-match loss to further enhance disentanglement and refine embedding training. Experiments on challenging datasets demonstrate the effectiveness of our method in 3D-aware image synthesis, particularly for scenes with complex or diverse objects.
AortaDiff: A Unified Multitask Diffusion Framework for Contrast-Free AAA Imaging
Authors: Yuxuan Ou; Ning Bi; Jiazhen Pan; Jiancheng Yang; Boliang Yu; Usama Zidan; Regent Lee; Vicente Grau
Venue: WACV 2026 (main)

Abstract: While contrast-enhanced CT (CECT) is standard for assessing abdominal aortic aneurysms (AAA) , the required iodinated contrast agents pose significant risks, including nephrotoxicity, patient allergies, and environmental harm. To reduce contrast agent use, recent deep learning methods have focused on generating synthetic CECT from non-contrast CT (NCCT) scans. However, most adopt a multi-stage pipeline that first generates images and then performs segmentation, which leads to error accumulation and fails to leverage shared semantic and anatomical structures.To address this, we propose a unified deep learning framework that generates synthetic CECT images from NCCT scans while simultaneously segmenting the aortic lumen and thrombus. Our approach integrates conditional diffusion models (CDM) with multi-task learning, enabling end-to-end joint optimization of image synthesis and anatomical segmentation. Unlike previous multitask diffusion models, our approach requires no initial predictions (e.g., a coarse segmentation mask), shares both encoder and decoder parameters across tasks, and employs a semi-supervised training strategy to learn from scans with missing segmentation labels, a common constraint in real-world clinical data.We evaluated our method on a cohort of 264 patients, where it consistently outperformed state-of-the-art single-task and multi-stage models. For image synthesis, our model achieved a PSNR of 25.61 dB, compared to 23.80 dB from a single-task CDM. For anatomical segmentation, it improved the lumen Dice score to 0.89 from 0.87 and the challenging thrombus Dice score to 0.53 from 0.48 (nnU-Net). These segmentation enhancements led to more accurate clinical measurements, reducing the lumen diameter MAE to 4.19 mm from 5.78 mm and the thrombus area error to 33.85% from 41.45% when compared to nnU-Net.
Beyond Faces: A Multimodal Person Clustering for Unconstrained Environments
Authors: Sahngmin Yoo; Sangwon Lee; Seongin Jo
Venue: WACV 2026 (main)

Abstract: The increasing demand for on-device AI, driven by privacy concerns and the need for real-time processing, poses new challenges for fundamental computer vision tasks. This paper addresses one such task, person clustering in photo galleries, which has traditionally relied on server-side computation or simplistic on-device models. We introduce the Multimodal Person Clustering Architecture (MPCA), a research framework designed to explore the feasibility of a high-performance, multimodal clustering pipeline operating entirely under mobile constraints. Our framework makes three principal contributions: (1) Multimodal Appearance-Assisted Identity Recovery (MAIR), a late-fusion strategy that leverages temporal consistency to recover identities when facial data is unreliable; (2) Language-Guided Appearance Extractor (LGAE), which adapts a vision-language paradigm to construct robust appearance representations efficiently; and (3) Sequential Graph-Density Clustering (SGDC), a novel algorithm that synergistically combines graph-based and density-based methods to handle the high variance of appearance data. We demonstrate through extensive experiments that our on-device framework achieves an unprecedented 87.97% average recall, significantly outperforming leading cloud-based commercial systems like Google Photos (77.74%) and on-device systems like Apple Photos (67.84%) and Samsung Gallery (83.39%). This work provides a blueprint for future research in privacy-preserving, efficient, and robust person clustering, highlighting a viable path for deploying next-generation computer vision applications directly on mobile devices.
High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization
Authors: Masih Aminbeidokhti; Heitor Rapela Medeiros; Srikanth Muralidharan; Eric Granger; Marco Pedersoli
Venue: WACV 2026 (main)

Abstract: Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial computational costs due to the need to train and store multiple models. Dropout offers a lightweight alternative by simulating ensembles through random neuron deactivation; however, when applied to pre-trained models, it tends to over-regularize and disrupt critical representations necessary for generalization. In this work, we investigate Mixout, a stochastic regularization technique that provides an alternative to Dropout for domain generalization. Rather than deactivating neurons, Mixout mitigates overfitting by probabilistically swapping a subset of fine-tuned weights with their pre-trained counterparts during training, thereby maintaining a balance between adaptation and retention of prior knowledge. Our study reveals that achieving strong performance with Mixout on domain generalization benchmarks requires a notably high masking probability of 0.9 for ViTs and 0.8 for ResNets. While this may seem like a simple adjustment, it yields two key advantages for domain generalization: (1) higher masking rates more strongly penalize deviations from the pre-trained parameters, promoting better generalization to unseen domains; and (2) high-rate masking substantially reduces computational overhead, cutting gradient computation by up to 45% and gradient memory usage by up to 90%. Experiments across five domain generalization benchmarks--PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet--using ResNet and ViT architectures show that our approach, High-rate Mixout, achieves out-of-domain accuracy comparable to ensemble-based methods while significantly reducing training costs.
High-Level Semantics and Low-Level Features Fusion for Multi-Scale Object Detection in Dynamic Construction Environments
Authors: Mahdi Bonyani; Maryam Soleymani; Chao Wang
Venue: WACV 2026 (main)

Abstract: Object detection in dynamic construction environments presents significant challenges due to vast scale variations, occlusions, and clutter. Conventional deep learning models struggle to balance the semantic information needed for classification with the spatial detail required for localization. This paper introduces a novel framework that systematically fuses features from different network depths to resolve this trade-off. Our primary contribution is a Hierarchical Feature Adjustment architecture that employs a coarse-to-fine strategy, progressively adjusting detections. We enhance robustness with an Efficient RoI Aggregation module for contextual aggregation and improve localization with a Modified IoU loss. Furthermore, a proposed Overlap Discriminating Module aids non-maximum suppression in dense scenes. Extensive experiments on the SODA, COD, and Small Tools datasets show our integrated approach significantly outperforms state-of-the-art methods, establishing a new benchmark for this critical application. We also demonstrate the model's generalizability to other domains with strong results on the BDD100K and COCO datasets.
BiNAR: A Bi-Modal Framework for Non-Aligned RGB-IR 3D Reconstruction via Gaussian Splatting
Authors: Zhongwen Wang; Han Ling; Weihao Zhang; Yinghui Sun; Quansen Sun
Venue: WACV 2026 (main)

Abstract: Existing RGB-IR (infrared) bi-modal 3D reconstruction methods generally have difficulty in simultaneously processing non-aligned multi-modal data with significant differences in resolution and spectral characteristics and achieving high-precision pixel-level reconstruction. Non-aligned RGB-IR 3D reconstruction and rendering represents a new domain. To this end, we propose BiNAR, a bi-modal framework that can directly process non-aligned data collected by conventional RGB and IR cameras and generate high-resolution, pixel-level aligned renderings. BiNAR first uses cross-modal multi-camera joint calibration to estimate the internal and external parameters of the RGB-IR camera and unify the coordinate system; then, it fuses the features of different modalities in the Unified Gaussian Field and jointly optimizes the Gaussians to achieve cross-modal consistent 3D scene expression. Experimental results show that BiNAR significantly outperforms traditional single-modal and bi-modal Gaussian splatting methods in rendering quality, achieving a sub-pixel average reprojection error of 0.242 px and improves IR PSNR by 8.00 dB. We also build a pixel-level aligned RGB-IR dataset covering a variety of indoor and outdoor scenes and including real temperature data, providing a reliable benchmark for subsequent multi-modal research. The code and dataset are available at https://github.com/jankin-wang/BiNAR.
Timestamp Query Transformer for Temporal Action Segmentation
Authors: Tieqiao Wang; Sinisa Todorovic
Venue: WACV 2026 (main)

Abstract: This work addresses action segmentation in videos under sparse timestamp supervision, where only a single frame per action segment---referred to as a timestamp---is labeled during training. We propose the Timestamp Query Transformer (TQT) that treats timestamps as learnable class query tokens. While existing approaches rely on iterative, multi-step generation of framewise pseudo-labels, TQT directly predicts temporal segmentation masks by leveraging query-feature cross-attention. This design enables fully end-to-end learning and maximizes the utility of sparse labels from the entire training dataset, rather than relying on only a few local timestamps within each training video as in prior work. Experiments on the GTEA, 50Salads, and Breakfast datasets demonstrate that TQT outperforms SOTA methods by up to 5.8% in accuracy and 7.7% in F1@50. The model and code will be released.
Mitigating Backdoor Attacks via Trigger Reconstruction and Model Hardening
Authors: Guanhong Tao; Siyuan Cheng; Guangyu Shen; Yingqi Liu; Shengwei An; Zhuo Zhang; Zhenting Wang; Hanxi Guo; Xiangyu Zhang
Venue: WACV 2026 (main)

Abstract: Backdoor attacks are among the most prominent security threats to deep learning models. Traditional backdoors rely on fixed trigger patterns (e.g., a red square) that existing defenses can often effectively remove. However, recent attacks embed semantic triggers that vary with the input and blend with meaningful features, rendering prior defenses ineffective. We propose MARTINI, a novel backdoor mitigation framework that addresses both traditional and semantic backdoors. MARTINI reconstructs backdoor samples via a dedicated trigger reconstruction procedure, producing malicious inputs that replicate the injected attack effect across a spectrum of attacks. Using these reconstructed samples paired with their correct labels, MARTINI then hardens the model through retraining to neutralize the targeted misclassification. Our evaluation on 14 types of backdoor attacks in image classification shows that MARTINI can reduce the attack success rate (ASR) from 96.56% to 5.17% on average, outperforming 12 state-of-the-art backdoor removal approaches, which at best reduce the ASR to 26.56%. It can also mitigate backdoors in self-supervised learning, object detection and NLP sentiment analysis.
Visibility guided Self-Supervised Occlusion-Resilient Human Pose Estimation
Authors: Arindam Dutta; Sarosij Bose; Rohit Kundu; Calvin-Khang Ta; Saketh Bachu; Konstantinos Karydis; Amit K. Roy-Chowdhury
Venue: WACV 2026 (main)

Abstract: Occlusion remains a significant challenge for existing human pose estimation algorithms, often resulting in inaccurate and anatomically implausible predictions. Although recent occlusion-robust methods report strong performance, they typically rely heavily on supervised learning and privileged information, such as multiview data or temporal sequences. Furthermore, these models often fail under domain changes. Domain-adaptive human pose estimation seeks to mitigate this issue; however, when occlusions are present in the target domain, a common occurrence in real-world applications, performance of these algorithms deteriorates significantly. To address these challenges, we propose VisOR, a novel Visibility guided Self-Supervised algorithm for Occlusion-Resilient Human Pose Estimation. VisOR achieves robustness to both domain shifts and occlusions by integrating contextual reasoning with iterative pseudo-label refinement. It mitigates the overfitting to noisy labels from occluded regions via a visibility-driven curriculum learning strategy, which progressively introduces the model to increasingly occluded training samples. Additionally, VisOR is regularized by a learned human pose prior that maintains anatomical plausibility throughout the adaptation process. Recognizing the scarcity of human pose datasets with realistic occlusions, we introduce BOW: Blended Occlusions in-the-Wild, a rigorously constructed context-aware synthetic benchmark designed to evaluate the occlusion resilience of human pose estimation algorithms. BOW offers a diverse range of context-aware occlusions across both indoor and outdoor environments, simulating real-world conditions. Through extensive experiments, we demonstrate that VisOR outperforms current state-of-the-art methods by  7% in challenging occluded human pose estimation benchmarks and provides a baseline performance on BOW, against existing algorithms.
Feature-Disentangling RGB-NIR Fusion Network for Remote Driver Physiological Measurement
Authors: Tayssir Bouraffa; Ziyuan Wang; Daniel Strüber
Venue: WACV 2026 (main)

Abstract: Remote photoplethysmography (rPPG) is a crucial technique for non-contact heart rate (HR) estimation using facial videos, gaining significance in driver monitoring systems where contact-based measurements are impractical. Existing rPPG methods often rely on either RGB or NIR data, each susceptible to limitations under motion artifacts and varying illumination in real-world driving scenarios. To address these challenges, we introduce a novel RGB-NIR fusion model tailored for robust rPPG and HR estimation in dynamic vehicle environments. Our approach features two main contributions, an NIR-specific decoder that facilitates effective cross-modal knowledge transfer from RGB to NIR, enhancing model adaptability, and a dual autoencoder architecture for efficient feature disentanglement and reconstruction, mitigating noise from driver motion and changing lighting conditions. Comprehensive evaluations, including inter- and cross-dataset testing and ablation studies across various driving and garage conditions, demonstrate that our model achieves superior performance on the MR-NIRP car dataset, showcasing significant robustness in complex vehicular environments.
VividAnimator: An End-to-End Audio and Pose-driven Half-Body Human Animation Framework
Authors: Donglin Huang; Yongyuan Li; Tianhang Liu; Junming Huang; Xiaoda Yang; Chi Wang; Weiwei Xu
Venue: WACV 2026 (main)

Abstract: Existing for audio- and pose-driven human animation methods often struggle with stiff head movements and blurry hands, primarily due to the weak correlation between audio and head movements and the structural complexity of hands.To address these issues, we propose VividAnimator, an end-to-end framework for generating high-quality, half-body human animations driven by audio and sparse hand pose conditions. Our framework introduces three key innovations. First, to overcome the instability and high cost of online codebook training, we pre-train a Hand Clarity Codebook (HCC) that encodes rich, high-fidelity hand texture priors, significantly mitigating hand degradation. Second, we design a Dual-Stream Audio-Aware Module (DSAA) to model lip synchronization and natural head pose dynamics separately while enabling interaction. Third, we introduce a Pose Calibration Trick (PCT) that refines and aligns pose conditions by relaxing rigid constraints, ensuring smooth and natural gesture transitions. Extensive experiments demonstrate that Vivid Animator achieves state-of-the-art performance, producing videos with superior hand detail, gesture realism, and identity consistency, validated by both quantitative metrics and qualitative evaluations.
Delta-LLaVA: Base-then-Specialize Alignment for Token-Efficient Vision-Language Models
Authors: Mohamad Zamini; Diksha Shukla
Venue: WACV 2026 (main)

Abstract: Multimodal Large Language Models (MLLMs) combine visual and textual representations to enable rich reasoning capabilities. However, the high computational cost of processing dense visual tokens remains a major bottleneck. A critical component in this pipeline is the visual projector, which bridges the vision encoder and the language model. Standard designs often employ a simple multi-layer perceptron for direct token mapping, but this approach scales poorly with high-resolution inputs, introducing significant redundancy. We present Delta-LLaVA, a token-efficient projector that employs a low-rank DeltaProjection to align multi-level vision features into a compact subspace before further interaction. On top of this base alignment, lightweight Transformer blocks act as specialization layers, capturing both global and local structure under constrained token budgets. Extensive experiments and ablations demonstrate that this base-then-specialize design yields consistent gains across multiple benchmarks with only 144 tokens, highlighting the importance of token formation prior to scaling interaction capacity. With Delta-LLaVA, inference throughput improves by up to 55%, while end-to-end training accelerates by nearly 4-5x in pretraining and over 1.5x in finetuning, highlighting the dual benefits of our design in both efficiency and scalability.
CommonForms: A Large, Diverse Dataset for Form Field Detection
Authors: Joe Barrow
Venue: WACV 2026 (main)

Abstract: This paper introduces CommonForms, a web-scale dataset for form field detection. It casts the problem of form field detection as object detection: given an image of a page, predict the location and type (Text Input, Choice Button, Signature) of form fields. The dataset is constructed by filtering Common Crawl to find PDFs that have fillable elements. Starting with 8 million documents, the filtering process is used to arrive at a final dataset of roughly 55k documents that have over 450k pages. Analysis shows that the dataset contains a diverse mixture of languages and domains; one third of the pages are non-English, and among the 14 classified domains, no domain makes up more than 25% of the dataset.In addition, this paper presents a family of form field detectors, FFDNet-Small and FFDNet-Large, which attain a very high average precision on the CommonForms test set. Each model cost less than 500 to train. Ablation results show that high-resolution inputs are crucial for high-quality form field detection, and that the cleaning process improves data efficiency over using all PDFs that have fillable fields in Common Crawl. A qualitative analysis shows that they outperform a popular, commercially available PDF reader that can prepare forms. Unlike the most popular commercially available solutions, FFDNet can predict checkboxes in addition to text and signature fields. This is, to our knowledge, the first large scale dataset released for form field detection, as well as the first open source models. The dataset, models, and code will be released after review.
OpenLVLM-MIA: A Controlled Benchmark Revealing the Limits of Membership Inference Attacks on Large Vision-Language Models
Authors: Ryoto Miyamoto; Xin Fan; Fuyuko Kido; Tsuneo Matsumoto; Hayato Yamana
Venue: WACV 2026 (main)

Abstract: OpenLVLM-MIA is a new benchmark that highlights fundamental challenges in evaluating membership inference attacks (MIA) against large vision-language models (LVLMs). While prior work has reported high attack success rates, our analysis suggests that these results often arise from detecting distributional bias introduced during dataset construction rather than from identifying true membership status. To address this issue, we introduce a controlled benchmark of 6,000 images where the distributions of member and non-member samples are carefully balanced, and ground-truth membership labels are provided across three distinct training stages. Experiments using OpenLVLM-MIA demonstrated that the performance of state-of-the-art MIA methods approached chance-level. OpenLVLM-MIA, designed to be transparent and unbiased benchmark, clarifies certain limitations of MIA research on LVLMs and provides a solid foundation for developing stronger privacy-preserving techniques.