GenAI4ELab/papercli-papers huggingface dataset:
Beyond Real Weights: Hypercomplex Representations for Stable Quantization
Authors: Jawad Ibn Ahad; Maisha Rahman; Amrijit Biswas; Muhammad Rafsan Kabir; Robin Krambroeckers; Sifat Momen; Nabeel Mohammed; Shafin Rahman
Venue: WACV 2026 (main)

Abstract: Multimodal language models (MLLMs) demand immense parameter capacity to align high-dimensional visual features with linguistic representations, making them computationally heavy and difficult to deploy efficiently. We introduce a progressive reparameterization strategy that physically compresses these models by progressively replacing dense feed-forward network blocks with compact Parameterized Hypercomplex Multiplication (PHM) layers. A residual interpolation schedule, together with lightweight reconstruction and knowledge distillation losses, ensures that the PHM modules inherit the functional behavior of their dense counterparts throughout training. This transition yields substantial parameter and FLOP reductions while preserving strong multimodal alignment, leading to faster inference without degrading output quality. We evaluate the approach on vision-language models (VLMs). Our approach preserves strong performance comparable with base VLMs, while delivering substantial reductions in model size and inference latency. Progressive PHM substitution thus offers an architecture-compatible path toward more efficient multimodal reasoning and complements existing low-bit quantization techniques. Codes are available at https://github.com/milab-nsu/PHM.
Uncertainty-Aware Subset Selection for Robust Visual Explainability under Distribution Shifts
Authors: Madhav Gupta; Vishak Prasad; Ganesh Ramakrishnan
Venue: WACV 2026 (main)

Abstract: Subset selection-based methods are widely used to explain deep vision models: they attribute predictions by highlighting the most influential image regions and support object-level explanations. While these methods perform well in in-distribution (ID) settings, their behavior under out-of-distribution (OOD) conditions remains poorly understood. Through extensive experiments across multiple ID-OOD sets, we find that reliability of the existing subset based methods degrades markedly, yielding redundant, unstable, and uncertainty-sensitive explanations. To address these shortcomings, we introduce a framework that combines submodular subset selection with layer-wise, gradient-based uncertainty estimation to improve robustness and fidelity without requiring additional training or auxiliary models. Our approach estimates uncertainty via adaptive weight perturbations and uses these estimates to guide submodular optimization, ensuring diverse and informative subset selection. Empirical evaluations show that, beyond mitigating the weaknesses of existing methods under OOD scenarios, our framework also yields improvements in ID settings. These findings highlight limitations of current subset-based approaches and demonstrate how uncertainty-driven optimization can enhance attribution and object-level interpretability, paving the way for more transparent and trustworthy AI in real-world vision applications.
FastHMR: Accelerating Human Mesh Recovery via Token and Layer Merging with Diffusion Decoding
Authors: Soroush Mehraban; Andrea Iaboni; Babak Taati
Venue: WACV 2026 (main)

Abstract: Recent transformer-based models for 3D Human Mesh Recovery (HMR) have achieved strong performance but often suffer from high computational cost and complexity due to deep transformer architectures and redundant tokens. In this paper, we introduce two HMR-specific merging strategies: Error-Constrained Layer Merging (ECLM) and Mask-guided Token Merging (Mask-ToMe). ECLM selectively merges transformer layers that have minimal impact on the Mean Per Joint Position Error (MPJPE), while Mask-ToMe focuses on merging background tokens that contribute little to the final prediction. To further address the potential performance drop caused by merging, we propose a diffusion-based decoder that incorporates temporal context and leverages pose priors learned from large-scale motion capture datasets. Experiments across multiple benchmarks demonstrate that our method achieves up to 2.3x speed-up while slightly improving performance over the baseline.
See, Think, Learn: A Self-Taught Multimodal Reasoner
Authors: Sourabh Sharma; Sonam Gupta; Sadbhawna Sadbhawna
Venue: WACV 2026 (main)

Abstract: Vision-Language Models (VLMs) have achieved remarkable progress in integrating visual perception with language understanding. However, effective multimodal reasoning requires both accurate perception and robust reasoning, and weakness in either limits the performance of VLMs. Prior efforts to enhance reasoning often depend on high-quality chain-of-thought (CoT) data, obtained via labor-intensive human annotations, costly proprietary models or self-training methods that overlook perception. To address these limitations, we propose a simple yet effective self-training framework called "See-Think-Learn" (STL). At its core, STL introduces a structured reasoning template that encourages the model to see before thinking: first extracting visual attributes in textual form, then using them to guide reasoning. The framework jointly improves perception and reasoning by having the model generate and learn from its own structured rationales in a self-training loop. Furthermore, we augment the training data with negative rationales, i.e. explanations that justify why certain answer choices are incorrect, to enhance the model's ability to distinguish between correct and misleading responses. This fosters more discriminative and robust learning. Experiments across diverse domains show that STL consistently outperforms baselines trained directly only on answers or self-generated reasoning, while qualitative analysis confirms the high quality of its rationales. STL thus provides a cost-effective solution to enhance multimodal reasoning ability of VLMs. Our code is available at https://github.com/srbhcs/see-think-learn.
CSF-Net: Context-Semantic Fusion Network for Large Mask Inpainting
Authors: Chae-Yeon Heo; Yeong-Jun Cho
Venue: WACV 2026 (main)

Abstract: In this paper, we propose a semantic-guided framework to address the challenging problem of large-mask image inpainting, where essential visual content is missing and contextual cues are limited. To compensate for the limited context, we leverage a pretrained Amodal Completion (AC) model to generate structure-aware candidates that serve as semantic priors for the missing regions. We introduce Context-Semantic Fusion Network (CSF-Net), a transformer-based architecture that fuses these candidates with contextual features to produce a semantic guidance image for image inpainting. This guidance improves inpainting quality by promoting structural accuracy and semantic consistency. CSF-Net can be seamlessly integrated into existing inpainting models without architectural changes and consistently enhances performance across diverse masking conditions. Extensive experiments on the Places365 dataset demonstrate that CSF-Net effectively reduces object hallucination while enhancing visual realism and semantic alignment.
Virtually Unrolling the Herculaneum Papyri by Diffeomorphic Spiral Fitting
Authors: Paul Henderson
Venue: WACV 2026 (main)

Abstract: The Herculaneum Papyri are a collection of rolled papyrus documents that were charred and buried by the famous eruption of Mount Vesuvius. They promise to contain a wealth of previously unseen Greek and Latin texts, but are extremely fragile and thus most cannot be unrolled physically. A solution to access these texts is virtual unrolling, where the papyrus surface is digitally traced out in a CT scan of the scroll, to create a flattened representation. This tracing is very laborious to do manually in gigavoxel-sized scans, so automated approaches are desirable. We present the first top-down method that automatically fits a surface model to a CT scan of a severely damaged scroll. We take a novel approach that globally fits an explicit parametric model of the deformed scroll to existing neural network predictions of where the rolled papyrus likely passes. Our method guarantees the resulting surface is a single continuous 2D sheet, even passing through regions where the surface is not detectable in the CT scan. We conduct comprehensive experiments on high-resolution CT scans of two scrolls, showing that our approach successfully unrolls large regions, and exceeds the performance of the only existing automated unrolling method suitable for this data.
Joint Modeling of Corruption-Driven and Information-Limited Uncertainty for Robust 3D Gaussian Splatting
Authors: Zeji Hui; Amirali Khodadadian Gostar; WeiQin Chuah; Alireza Bab-Hadiashar; Ruwan Tennakoon
Venue: WACV 2026 (main)

Abstract: Real-time 3D Gaussian Splatting (3DGS) has emerged as an efficient, high-fidelity alternative to neural radiance fields for novel view synthesis, enabling second-scale training and rendering via GPU rasterization. However, when input image collections contain transient disturbances (e.g., dynamic objects, exposure variations, motion blur) or suffer from sparse view coverage at scene boundaries, 3DGS performance degrades significantly due to reconstruction artifacts such as ghosting, floating points, and blurred surfaces. In this work, we present a unified framework that jointly addresses two types of artifacts: (1) corruption-driven artifacts, caused by transient or occluded content; and (2) information-limited artifacts, caused by insufficient multi-view observations. Our method leverages the training gradient signal, as well as the shape and spatial distribution of Gaussians, to adaptively suppress unreliable splats through a soft-masking strategy, without relying on any pretrained segmentation or feature networks.Extensive experiments on two real-world datasets with dynamic scenes and sparse camera trajectories demonstrate that our approach outperforms state-of-the-art robust 3DGS and uncertainty-pruning techniques in artifact suppression and reconstruction fidelity, while preserving real-time performance.
TalkingHeadBench: A Multi-Modal Benchmark & Analysis of Talking-Head DeepFake Detection
Authors: Xinqi Xiong; Prakrut Patel; Qingyuan Fan; Amisha Wadhwa; Sarathy Selvam; Xiao Guo; Luchao Qi; Xiaoming Liu; Roni Sengupta
Venue: WACV 2026 (main)

Abstract: The rapid advancement of talking-head deepfake generation fueled by advanced generative models has elevated the realism of synthetic videos to a level that poses substantial risks in domains such as media, politics, and finance. However, current benchmarks for deepfake talking-head detection fail to reflect this progress, relying on outdated generators and offering limited insight into model robustness and generalization. We introduce TalkingHeadBench, a new benchmark designed to address this gap, featuring talking-head videos from six modern generators, with an additional two emerging generators used exclusively for testing generalization. The dataset is built on an expert-led curation process that filters over 60% of samples to remove videos with noticeable artifacts, presenting a more difficult challenge for detectors. Our evaluation protocols are designed to measure generalization across identity and generator shifts. Benchmarking seven state-of-the-art detectors reveals that models with high accuracy on older datasets like FaceForensics++ show a significant performance drop on our curated data, particularly at strict false positive rates (e.g., TPR@FPR=0.1%). In addition, we identify a trend where detectors focus on background cues instead of facial features using Grad-CAM visualization. Our benchmark aims to accelerate research towards more robust and generalizable detection models in the face of rapidly evolving generative techniques. We release our benchmark and dataset with all data splits and protocols at https://anaxqx.github.io/talkingheadbench.github.io.
SOAF: Scene Occlusion-aware Neural Acoustic Field
Authors: Huiyu Gao; Jiahao Ma; David Ahmedt-Aristizabal; Chuong Nguyen; Miaomiao Liu
Venue: WACV 2026 (main)

Abstract: This paper tackles the problem of novel view acoustic synthesis along an arbitrary trajectory in an indoor scene, given the audio-video recordings from other known trajectories of the scene. Existing methods often overlook the effect of room geometry, particularly wall occlusions on sound propagation, making them less accurate in multi-room environments. In this work, we propose a new approach called Scene Occlusion-aware Acoustic Field (SOAF) for accurate sound generation. Our approach derives a global prior for the sound field learning through distance-aware parametric sound propagation modeling and then transforms it based on the scene structure learned from the input video. We extract features from the local acoustic field centered at the receiver using a Fibonacci Sphere to generate binaural audio for novel views with a direction-aware attention mechanism. Extensive experiments on the real dataset RWAVS and the synthetic dataset SoundSpaces demonstrate that our method achieves superior performance in spatial audio generation.
Exploring the Boundaries of Diffusion Models for Offline Writer Identification with Sparse and Intra-Variable Data
Authors: Aritra Dey; Chandranath Adak; Kumari Priya; Soumi Chattopadhyay; Sukalpa Chanda
Venue: WACV 2026 (main)

Abstract: Offline writer identification poses significant challenges when training data is scarce, and handwriting styles exhibit high intra-writer variability. This scenario is common in practical applications such as forensic analysis and historical document authentication, where only a limited number of handwritten samples are available per writer. In this paper, we explore the viability of using diffusion models to capture writer-specific traits under such challenging conditions. Specifically, we investigate their performance in both text-dependent and text-independent setups, where lexical similarity varies across samples. We propose a novel diffusion-based writer identification framework that integrates a style encoder and handcrafted textural features in a joint training pipeline. Our approach is evaluated on a recent dataset with high intra-writer variability as well as three benchmark datasets (IAM, CERUG-EN, and CVL). Experimental results demonstrate that while diffusion models excel in text-dependent scenarios, their generalization capability diminishes in text-independent settings due to the entanglement of content and style features. This study highlights both the promise and the current limitations of generative diffusion models for fine-grained handwriting style modeling. We identify avenues for improving generalization through disentangled representations, domain adaptation, and hybrid discriminative-generative architectures. The proposed framework contributes to the growing efforts toward scalable, style-aware writer identification in real-world, unconstrained handwriting scenarios.
QC-SF: Improving Computer Vision for Airborne LiDAR Point Clouds of Boreal Forests with Quebec Simulated Forest Dataset
Authors: Olivier Stocker; Reza Mahmoudi Kouhi; Omid Reisi Gahrouei; Thierry Badard; Eric Guilbert
Venue: WACV 2026 (main)

Abstract: Boreal forest ecosystems are under immense pressure, and while airborne LiDAR has emerged as a powerful monitoring tool, leveraging its large data volumes requires automated analysis. Deep learning methods offer a solution but are hindered by the scarcity of large-scale, labeled datasets in forestry, contrasting to the data-rich urban environments. To address this gap, we introduce the Quebec Simulated Forest (QC-SF), a large-scale, synthetic airborne LiDAR dataset fully labeled for semantic segmentation. QC-SF contains 60,000 forest plots, each composed of a controlled mixture of two dominant species in Quebec's boreal forest: Black Spruce and Balsam Fir. Using this benchmark, we evaluate the performance of five renowned and state-of-the-art point cloud networks: KPConv, MinkUNet, DGCNN, Point Mamba and Point Transformer V3. Our results identify Point Transformer V3 as the most effective architecture, achieving 91.66% mIoU. Furthermore, we validate the sim-to-real transferability of our dataset, demonstrating that augmenting a small number of real-world scans with our synthetic data improves segmentation performance by 6% in mIoU score. QC-SF is available at : qc-sf.org.
Grounding Descriptions in Images informs Zero-Shot Visual Recognition
Authors: Shaunak Halbe; Junjiao Tian; K J Joseph; James Seale Smith; Katherine Stevo; Vineeth N. Balasubramanian; Zsolt Kira
Venue: WACV 2026 (main)

Abstract: Vision-language models (VLMs) like CLIP have been cherished for their ability to perform zero-shot visual recognition on open-vocabulary concepts. This is achieved by selecting the object category whose textual representation bears the highest similarity with the query image. While successful in some domains, this method struggles with identifying fine-grained entities as well as generalizing to unseen concepts that are not captured by the training distribution. Recent works attempt to mitigate these challenges by integrating category descriptions at test time, albeit yielding modest improvements. We attribute these limited gains to a misalignment between image regions and textual descriptions, which stems from CLIP's global alignment objective. In this paper, we propose GRAIN, a new pretraining strategy aimed at aligning representations at both fine and coarse levels simultaneously. Our approach learns to jointly ground textual descriptions in image regions along with aligning overarching captions with global image representations. To drive this pre-training, we leverage frozen Multimodal Large Language Models (MLLMs) to derive large-scale synthetic annotations. We demonstrate the enhanced zero-shot performance of our model compared to current state-of-the art methods across 11 diverse image classification datasets. Additionally, we introduce Products-2023, a newly curated, manually labeled dataset featuring novel concepts, and showcase our model's ability to recognize these concepts by benchmarking on it. Significant improvements achieved by our model on other downstream tasks like retrieval further highlight the superior quality of representations learned by our approach. Code available at https://github.com/shaunak27/grain-clip.
iMotion-LLM: Instruction-Conditioned Trajectory Generation
Authors: Abdulwahab Felemban; Nussair Hroub; Jian Ding; Eslam Abdelrahman; Xiaoqian Shen; Abduallah Mohamed; Mohamed Elhoseiny
Venue: WACV 2026 (main)

Abstract: We introduce iMotion-LLM, a large language model (LLM) integrated with trajectory prediction modules for interactive motion generation. Unlike conventional approaches, it generates feasible, safety-aligned trajectories based on textual instructions, enabling adaptable and context-aware driving behavior. It combines an encoder-decoder multimodal trajectory prediction model with a pre-trained LLM fine-tuned using LoRA, projecting scene features into the LLM input space and mapping special tokens to a trajectory decoder for text-based interaction and interpretable driving. To support this framework, we introduce two datasets: 1) InstructWaymo, an extension of the Waymo Open Motion Dataset with direction-based motion instructions, and 2) Open-Vocabulary InstructNuPlan, which features safety-aligned instruction-caption pairs and corresponding safe trajectory scenarios. Our experiments validate that instruction conditioning enables trajectory generation that follows the intended condition. iMotion-LLM demonstrates strong contextual comprehension, achieving 84% average accuracy in direction feasibility detection and 96% average accuracy in safety evaluation of open-vocabulary instructions. This work lays the foundation for text-guided motion generation in autonomous driving, supporting simulated data generation, model interpretability, and robust safety alignment testing for trajectory generation models. Our code, pre-trained model, and datasets are available at: vision-cair.github.io/iMotion-LLM/.
Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Information
Authors: Neelima Prasad; Jarek Reynolds; Neel Karsanbhai; Tanusree Sharma; Lotus Zhang; Abigale Stangl; Yang Wang; Leah Findlater; Danna Gurari
Venue: WACV 2026 (main)

Abstract: We propose a novel task, hierarchical instance tracking, which entails tracking all instances of predefined categories of objects and parts, while maintaining their hierarchical relationships. We introduce the first benchmark dataset sup- porting this task, consisting of 2,765 unique entities that are tracked in 552 videos and belong to 40 categories (across objects and parts). Evaluation of seven variants of four models tailored to our novel task reveals the new dataset is challenging. Our dataset is available at https://vizwiz.org/tasks-and-datasets/hierarchical-instance-tracking/.
Inpaint360GS: Efficient Object-Aware 3D Inpainting via Gaussian Splatting for 360deg Scenes
Authors: Shaoxiang Wang; Shihong Zhang; Christen Millerdurai; Rüdiger Westermann; Didier Stricker; Alain Pagani
Venue: WACV 2026 (main)

Abstract: Despite recent advances in single-object front-facing inpainting using NeRF and 3D Gaussian Splatting (3DGS), inpainting in complex 360^\circ scenes remains largely underexplored. This is primarily due to three key challenges: (i) identifying target objects in the 3D field of 360deg environments, (ii) dealing with severe occlusions in multi-object scenes, which makes it hard to define regions to inpaint, and (iii) maintaining consistent and high-quality appearance across views effectively. To tackle these challenges, we propose Inpaint360GS, a flexible 360^\circ editing framework based on 3DGS that supports multi-object removal and high-fidelity inpainting in 3D space. By distilling 2D segmentation into 3D and leveraging virtual camera views for contextual guidance, our method enables accurate object-level editing and consistent scene completion. We further introduce a new dataset tailored for 360^\circ inpainting, addressing the lack of ground truth object-free scenes. Experiments demonstrate that Inpaint360GS outperforms existing baselines and achieves state-of-the-art performance. The dataset and code will be released to facilitate future research.
Model-free Domain Adaptation for Concealed Multimodal Large-Language Models
Authors: Yu Mitsuzumi; Akisato Kimura; Hisashi Kashima
Venue: WACV 2026 (main)

Abstract: Multimodal large-language models (MLLMs) exhibit remarkable capability for various vision tasks but still struggle with the domain-shift problem, in which their performance degrades for data from unfamiliar domains. Since the latest MLLMs often conceal their model resources (i.e., data, parameters, and outputs) from training purposes, current domain adaptation methods cannot satisfactorily address this problem due to their dependence on those resources. To this end, we introduce a novel domain adaptation setup, "model-free domain adaptation (MFDA)" of MLLMs, to investigate whether we can address domain adaptation problems without using any resources of the concealed models. As a proof of concept for MFDA, we built a method named **model-transferable domain-adaptable visual prompting (MTDA-VP)**.In the training, this method executes cross-model visual prompting on surrogate models with a domain adaptation objective so that the visual prompts simultaneously acquire model transferability and domain adaptability. In the testing, we can adapt the concealed MLLMs to the target domain by just inputting the test images with the trained prompt into the models. Besides, we developed two techniques, cross-model pseudo labeling (CMPL) and cross-model gradient alignment (CMGA), to further enhance model transferability and domain adaptability of the visual prompts. We empirically confirmed that MFDA-VP improved the performance of several MLLMs with large margins on two datasets.