GenAI4ELab/papercli-papers huggingface dataset:
Rethinking Latent Variable in Learned Image Compression
Authors: Fangzhou Yi; Zhicheng Gong; Hui Zeng
Venue: WACV 2026 (main)

Abstract: Benefiting from the powerful data learning and representation capabilities of neural networks, Learned Image Compression (LIC) methods have demonstrated better Rate-Distortion (RD) performance than traditional image compression frameworks. In this paper, we analyze the role of latent variables in image compression, both qualitatively and quantitatively. We then propose a latent variable compensation method to mitigate the loss introduced by quantization. We also introduce a regularization term for the latent variable mean square error into the loss function, providing more explicit guidance for the compression and reconstruction of the model's internal latent variables. Additionally, we propose a noise compensation method that acts as a plug-and-play component to enhance reconstruction in lossy image compression without significant additional encoding or decoding time. We also present a data augmentation technique involving image inversion, which helps the training set conform to the symmetry inherent in probabilistic modeling for image compression tasks. Extensive experiments demonstrate that the proposed method enhances rate-distortion metrics and visual quality.
Stroke Modeling Enables Vectorized Character Generation with Large Vectorized Glyph Model
Authors: Xinyue Zhang; Haolong Li; Jiawei Ma; Chen Ye
Venue: WACV 2026 (main)

Abstract: Vectorized glyphs are widely used in poster design, network animation, art display, and various other fields due to their scalability and flexibility. In typography, they are often seen as special sequences composed of ordered strokes. This concept extends to the token sequence prediction abilities of large language models (LLMs), enabling vectorized character generation through stroke modeling. In this paper, we propose a novel Large Vectorized Glyph Model (LVGM) designed to generate vectorized Chinese glyphs by predicting the next stroke. Initially, we encode strokes into discrete latent variables called stroke embeddings. Subsequently, we train our LVGM via fine-tuning DeepSeek LLM by predicting the next stroke embedding. With limited strokes given, it can generate complete characters, semantically elegant words, and even unseen verses in vectorized form. Moreover, we release a new large-scale Chinese SVG dataset containing 907,267 samples based on strokes for dynamically vectorized glyph generation. Experimental results show that our model has scaling behaviors on data scales. Our generated vectorized glyphs have been validated by experts and relevant individuals.
DCText: Scheduled Attention Masking for Visual Text Generation via Divide-and-Conquer Strategy
Authors: Jaewoo Song; Jooyoung Choi; Kanghyun Baek; Sangyub Lee; Daemin Park; Sungroh Yoon
Venue: WACV 2026 (main)

Abstract: Despite recent text-to-image models achieving highfidelity text rendering, they still struggle with long or multiple texts due to diluted global attention. We propose DCText, a training-free visual text generation method that adopts a divide-and-conquer strategy, leveraging the reliable short-text generation of Multi-Modal Diffusion Transformers. Our method first decomposes a prompt by extracting and dividing the target text, then assigns each to a designated region. To accurately render each segment within their regions while preserving overall image coherence, we introduce two attention masks--Text-Focus and Context-Expansion--applied sequentially during denoising. Additionally, Localized Noise Initialization further improves text accuracy and region alignment without increasing computational cost. Extensive experiments on single- and multisentence benchmarks show that DCText achieves the best text accuracy without compromising image quality while also delivering the lowest generation latency.
Improved Wildfire Spread Prediction with Time-Series Data and the WSTS+ Benchmark
Authors: Saad Lahrichi; Jake Bova; Jesse Johnson; Jordan Malof
Venue: WACV 2026 (main)

Abstract: Recent research has demonstrated the potential of deep neural networks (DNNs) to accurately predict wildfire spread on a given day based upon high-dimensional explanatory data from a single preceding day, or from a time series of T preceding days. For the first time, we investigate a large number of existing data-driven wildfire modeling strategies under controlled conditions, revealing the best modeling strategies and resulting in models that achieve state-of-the-art (SOTA) accuracy for both single-day and multi-day input scenarios, as evaluated on a large public benchmark for next-day wildfire spread, termed the WildfireSpreadTS (WSTS) benchmark. Consistent with prior work, we found that models using time-series input obtained the best overall accuracy, suggesting this is an important future area of research. Furthermore, we create a new benchmark, WSTS+, by incorporating four additional years of historical wildfire data into the WSTS benchmark. Our benchmark doubles the number of unique years of historical data, expands its geographic scope, and, to our knowledge, represents the largest public benchmark for time-series-based wildfire spread prediction.
CADE: Continual Weakly-supervised Video Anomaly Detection with Ensembles
Authors: Satoshi Hashimoto; Tatsuya Konishi; Tomoya Kaichi; Kazunori Matsumoto; Mori Kurokawa
Venue: WACV 2026 (main)

Abstract: Video anomaly detection (VAD) has long been studied as a crucial problem in public security and crime prevention. In recent years, weakly-supervised VAD (WVAD) has attracted considerable attention due to its easy annotation process and promising research results. While existing WVAD methods mainly tackle static datasets, the possibility that the domain of data can vary has been neglected. To adapt such domain shift, the continual learning (CL) perspective is required because otherwise additional training using only newly incoming data could easily cause performance degradation for previous data, i.e., forgetting. Therefore, we propose a brand-new approach, called Continual Anomaly Detection with Ensembles (CADE) that is the first work combining CL and WVAD viewpoints. Specifically, CADE uses the Dual-Generator (DG) to address data imbalance and label uncertainty in WVAD. We also found that forgetting exacerbates the "incompleteness" where the model becomes biased towards certain anomaly modes, leading to missed detections of various anomalies. To address this, we propose a Multi-Discriminator (MD) ensemble that captures missed anomalies in past scenes due to forgetting, using multiple models. Extensive experiments show that CADE significantly outperforms existing VAD methods on the common multi-scene VAD datasets, such as the ShanghaiTech, UCF-Crime, and Charlotte Anomaly Dataset.
DOODLE: Diffusion-based Out-of-Distribution Learning for Open-set LiDAR Semantic Segmentation
Authors: Changgyoon Oh; Hyeonseong Kim; Daehyun We; Jongoh Jeong; Yujeong Chae; Kuk-Jin Yoon
Venue: WACV 2026 (main)

Abstract: Open-set driving in complex real-world environments requires reliable identification of out-of-distribution (OOD) objects to avoid overconfident predictions on unseen categories. However, the sparsity and limited semantic richness of LiDAR point clouds make separating known and unknown classes difficult. This work proposes DOODLE, a diffusion model-based OOD learning framework for open-set 3D semantic segmentation. DOODLE trains a diffusion model to reconstruct in-distribution semantic features; feature-level reconstruction discrepancies then serve as OOD evidence. The resulting OOD scores are used to enhance backbone semantic features, improving discrimination of unknown regions during segmentation. To further reduce false positives arising from nonuniform measurements, a density-aware post-processing (DAP) module incorporates spatial variation in LiDAR point density when refining OOD predictions. DOODLE integrates seamlessly with existing open-set models and does not constrain backbone design. Experiments on SemanticKITTI and nuScenes demonstrate state-of-the-art OOD performance. On SemanticKITTI, DOODLE improves area under the precision-recall curve (AUPR) by 1.85%p and area under the receiver operating characteristic (AUROC) by 1.29%p over prior methods. Ablation studies confirm complementary benefits from diffusion-based reconstruction and DAP. Code is available at https://github.com/chang9711/DOODLE.
MUSE: Model-based Uncertainty-aware Similarity Estimation for zero-shot 2D Object Detection and Segmentation
Authors: Sungmin Cho; Sungbum Park; Insoo Oh
Venue: WACV 2026 (main)

Abstract: In this work, we present MUSE (Model-based Uncertainty-aware Similarity Estimation), a training-free framework for model-based zero-shot 2D object detection and segmentation. First, MUSE incorporates 2D multi-view templates from 3D unseen objects and 2D object proposals from the input query image, respectively. In the embedding stage, we propose a new feature embedding scheme which integrates class and patch embeddings. Specifically, the patch embeddings are normalized using the generalized mean pooling (GeM). In the matching stage, a joint similarity score is introduced, which integrates an absolute score and a relative score. Finally, we update the similarity score using an uncertainty-aware object prior. MUSE achieves state-of-the-art performance on the BOP Challenge 2025, ranking first in the Classic Core, H3, and Industrial tracks--without any additional training or fine-tuning. Therefore, we believe that MUSE is a promising framework for zero-shot 2D object detection and segmentation.
HistoMILKD: A Multiple Instance Learning based Multi-Teacher Knowledge Distillation Framework for Whole Slide Image Classification
Authors: Mayur Mallya; Ali Khajegili Mirabadi; Hossein Farahani; Ali Bashashati
Venue: WACV 2026 (main)

Abstract: Foundation models (FM) in digital pathology have revolutionized the field of whole slide image (WSI) analysis, with models such as UNI, Virchow, Prov-GigaPath, and many more outperforming the previously established benchmarks set by the ImageNet-based backbones. However, despite several benchmarking studies, there has been no clear consensus on the choice of a single FM that is best suited for a variety of histopathology datasets and/or tasks. With more than 25 pathology FMs in the literature so far, the challenge of model selection is a growing concern. Although an ensemble of FMs can circumvent this issue, given the bulky nature of individual FMs, the inference time and computational cost drastically increase with the addition of each FM into the ensemble. To this end, we propose HistoMILKD, the first multi-teacher knowledge distillation (MKD) framework for WSI classification. To handle the gigapixel resolution of WSIs, we use multiple instance learning (MIL), making this also the first work to integrate MIL and MKD frameworks into a single model. Our approach leverages the complementary representations of different FMs to distill collective task-specific knowledge into a single trainable MIL adapter on top of the student FM, which is utilized during inference. Evaluated on five public datasets, the proposed approach significantly (p<0.05) outperforms the individual FMs, their ensemble, and previous MKD approaches in WSI classification. Codes are available at https://github.com/AIMLab-UBC/HistoMILKD.
RobustFormer: Noise-Robust Pre-training for Images and Videos
Authors: Ashish Bastola; Nishant Luitel; Hao Wang; Danda Pani Paudel; Roshni Poudel; Abolfazl Razi
Venue: WACV 2026 (main)

Abstract: While deep learning-based models like transformers, have revolutionized time-series and vision tasks, they remain highly susceptible to noise and often overfit on noisy patterns rather than robust features. This issue is exacerbated in vision transformers, which rely on pixel-level details that can easily be corrupt. To address this, we leverage the discrete wavelet transform (DWT) for its ability to decompose into multi-resolution layers, isolating noise primarily in the high frequency domain while preserving essential low-frequency information for resilient feature learning. Conventional DWT-based methods, however, struggle with computational inefficiencies due to the requirement for a subsequent inverse discrete wavelet transform (IDWT) step. In this work, we introduce RobustFormer, a novel framework that enables noise-robust masked autoencoder (MAE) pre-training for both images and videos by using DWT for efficient downsampling, eliminating the need for expensive IDWT reconstruction and simplifying the attention mechanism to focus on noise-resilient multi-scale representations. To our knowledge, RobustFormer is the first DWT-based method fully compatible with video inputs and MAE-style pre-training. Extensive experiments on noisy image and video datasets demonstrate that our approach achieves up to 8% increase in Top-1 classification accuracy under severe noise conditions in Imagenet-C and up to 2.7% in Imagenet-P standard benchmarks compared to the baseline and up to 13% higher Top-1 accuracy on UCF-101 under severe custom noise perturbations while maintaining similar accuracy scores for clean datasets. We also observe the reduction of computation complexity by up to 4.4% through IDWT removal compared to VideoMAE baseline without significant performance drop.
Modeling and Learning Multiple Hypotheses for Monocular 3D Object Detection
Authors: Hyeonjeong Park; Peixi Xiong; Pei Yu; Wei Tang
Venue: WACV 2026 (main)

Abstract: Detecting objects in 3D space using a monocular image is inherently a highly ill-posed problem: multiple plausible 3D bounding boxes can explain the same 2D observation of an object. Existing approaches typically follow a single-point prediction paradigm, failing to capture this multimodal nature and often regressing to an implausible mean solution. This paper introduces MonoMH, a novel multi-hypothesis framework for monocular 3D object detection. By explicitly modeling and learning the multimodal distribution of plausible 3D object configurations, MonoMH not only significantly improves detection performance but also provides richer information to support downstream decision-making. MonoMH introduces three key innovations: (1) a novel multi-hypothesis predictor that leverages spatially diverse features across different windows within an RoI to generate a rich variety of hypotheses without increasing model complexity; (2) a new multi-hypothesis learning approach that derives diverse and relevant hypotheses from single-modal ground truth by integrating uncertainty modeling with best-of-many learning; and (3) a novel adaptive hypothesis filtering mechanism that enhances detection capability by dynamically retaining a variable number of plausible hypotheses based on each object's uncertainty. Experimental results demonstrate the effectiveness of our approach. Notably, MonoMH achieves 29.12 (easy), 20.88 (mod.), and 17.93 (hard) Car AP3D on the KITTI test set, a significant boost over the previous state-of-the-art methods. The code can be found at https://github.com/HyeonjeongPark37/MonoMH.
Context-Preserving Dermoscopic Editing: Mask-Guided Lesion-Aware Diffusion for Attribute Modification
Authors: Tao Sun; Yun Jiang; Yarong Jin; Huanting Guo; Zequn Zhang
Venue: WACV 2026 (main)

Abstract: Precise manipulation of dermoscopic attributes is essential for augmenting long-tailed datasets and enhancing diagnostic interpretability. However, generic diffusion-based editing methods often induce global distortions or fail to preserve the peri-lesional context critical for diagnosis. To address these limitations, we propose Context-Preserving Dermoscopic Editing (CPDE), a framework tailored for lesion-aware attribute modification. CPDE introduces a dual-branch diffusion pipeline that disentangles lesion editing from background reconstruction. Attribute changes are predicted by a Spatial--channel Transformer at the U-Net bottleneck and are trained with a lesion-aware objective that enforces semantic directionality only inside pathology regions. On the ISIC 2017 and 2018 datasets, CPDE produces spatially localized, clinically coherent edits that preserve lesion extent and surrounding skin. Our method achieves superior image fidelity with an FID of 0.274, while significantly outperforming existing approaches in terms of semantic alignment and background preservation.
Guided Texture Segmentation via Coordinate-Aware Class-Ratio Mapping
Authors: Bishal Ranjan Swain; Kyung Joo Cheoi; Jaepil Ko
Venue: WACV 2026 (main)

Abstract: Segmenting texture-rich images is challenging due to the presence of various local ambiguities. These are common in domains like metallography, where scanning electron microscopy (SEM) images must be segmented to quantify different elements that determine material properties. Conventional encoder-decoder models often struggle in these scenarios because they rely solely on local features and lack global distributional awareness. We propose a guided segmentation framework that introduces coordinate-aware class-ratio mapping. It is a mechanism that transforms expected class proportions into spatial maps and integrates them with encoder features via an adaptive gate fusion module. This enforces consistency between global class ratios and local pixel predictions, allowing the model to resolve ambiguous textures more effectively. Unlike traditional methods, we condition the model on image-specific global ratios, which can be obtained from experts or estimated in an auto-regressive method. Extensive experiments on metallographic SEM benchmarks demonstrate that our framework consistently improves performance across diverse backbones, achieving up to +6.1% Dice score improvements with minimal parameter overhead (<2%). Ablation studies confirm that both coordinate-aware class-ratio mapping and the adaptive gate fusion module contribute complementary benefits. In addition, we demonstrate that segmentation performance improves provided that the accuracy of the input class ratios is at least 60%.
SD-CSFL: A Synthetic Data-Driven Conformity Scoring Framework for Robust Federated Learning
Authors: Ebtisaam Alharbi; Abdulrahman Kerim; Leandro Soriano Marcolino; Qiang Ni
Venue: WACV 2026 (main)

Abstract: Federated Learning (FL) enables collaborative model training without sharing raw data, but remains highly vulnerable to gradient manipulation and backdoor attacks, particularly under heterogeneous client distributions. Most existing defenses either target a narrow class of attacks, rely on client data, or fail to adapt in heterogeneous settings. We propose SD-CSFL (Synthetic Data-Driven Conformity Scoring for Federated Learning), a unified and privacy-preserving defense algorithm. SD-CSFL leverages a synthetic calibration dataset, independent of client data, to compute entropy-based nonconformity scores that capture irregularities in client updates. An adaptive percentile thresholding mechanism with stratified calibration dynamically distinguishes benign from malicious updates across training rounds. We establish a conformal-prediction-based guarantee showing that percentile thresholds bound false positives under arbitrary score distributions. Experiments on CIFAR-10 and Birds-525 demonstrate up to 35% higher detection of gradient manipulation and an 80% reduction in backdoor success rates, outperforming recent defenses in heterogeneous environments. Our implementations and synthetic datasets are available at https://github.com/EbtisaamCS/SD-CSFL
Patch-wise Retrieval: A Bag of Practical Techniques for Instance-level Matching
Authors: Wonseok Choi; Sohwi Lim; Nam Hyeon-Woo; Moon Ye-Bin; Dong-Ju Jeong; Jinyoung Hwang; Tae-Hyun Oh
Venue: WACV 2026 (main)

Abstract: Instance-level image retrieval aims to find images containing the same object as a given query, despite variations in size, position, or appearance. To address this challenging task, we propose Patchify, a simple yet effective patch-wise retrieval framework that offers high performance, scalability, and interpretability without requiring fine-tuning. Patchify divides each database image into a small number of structured patches and performs retrieval by comparing these local features with a global query descriptor, enabling accurate and spatially grounded matching. To assess not just retrieval accuracy but also spatial correctness, we introduce LocScore, a localization-aware metric that quantifies whether the retrieved region aligns with the target object. This makes LocScore a valuable diagnostic tool for understanding and improving retrieval behavior. We conduct extensive experiments across multiple benchmarks, backbones, and region selection strategies, showing that Patchify outperforms global methods and complements state-of-the-art reranking pipelines. Furthermore, we apply Product Quantization for efficient large-scale retrieval and highlight the importance of using informative features during compression, which significantly boosts performance.
Reviving Unsupervised Optical Flow: Concept Reevaluation, Multi-Scale Advances and Full Open-Source Release
Authors: Azin Jahedi; Marc Rivinius; Noah Berenguel Senn; Andres Bruhn
Venue: WACV 2026 (main)

Abstract: Unsupervised optical flow methods have become more popular in the last decade, enabling the training of models across domains without ground truth data. Although RAFT and its successors have achieved significant success in the supervised settings, many unsupervised approaches continue to use older backbones such as PWC-Net. One reason for this architectural stagnation is that the current RAFT-based SOTA approach has proven challenging for the community to reproduce. In this paper, we revive and advance unsupervised optical flow: First, we introduce Sun-RAFT: a simple unsupervised RAFT. Second, building on Sun-RAFT, we present Muun-RAFT: a novel multi-scale unsupervised RAFT, where we propose a gradual context-based upsampling to refine the flow, further improving both accuracy and preservation of details. Third, we reexamine previously advised unsupervised strategies to identify effective training settings. In terms of results, both our methods demonstrate strong generalization capabilities and set a new SOTA for unsupervised two-frame approaches on MPI-Sintel, with Muun-RAFT surpassing even the current multi-frame SOTA by up to 28%. Finally, we open-source our PyTorch code, enabling further developments in the field: https://cv-stuttgart.github.io/Reviving-Unsupervised-OpticalFlow.
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences
Authors: Soroush Mehraban; Mohammad Javad Rajabi; Andrea Iaboni; Babak Taati
Venue: WACV 2026 (main)

Abstract: Self-supervised pretraining methods with masked prediction demonstrate remarkable within-dataset performance in skeleton-based action recognition. However, we show that, unlike contrastive learning approaches, they do not produce well-separated clusters. Additionally, these methods struggle with generalization in few-shot settings. To address these issues, we propose Self-supervised Tuning for 3D Action Recognition in Skeleton sequences (STARS). Specifically, STARS first uses a masked prediction stage using an encoder-decoder architecture. It then employs nearest-neighbor contrastive learning to partially tune the weights of the encoder, enhancing the formation of semantic clusters for different actions. By tuning the encoder for a few epochs, and without using hand-crafted data augmentations, STARS achieves state-of-the-art self-supervised results in various benchmarks, including NTU-60, NTU-120, and PKU-MMD. In addition, STARS exhibits significantly better results than masked prediction models in few-shot settings, where the model has not seen the actions throughout pretraining.