GenAI4ELab/papercli-papers huggingface dataset:
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
Authors: Sunghyun Ahn; Youngwan Jo; Kijung Lee; Sein Kwon; Inpyo Hong; Sanghyun Park
Venue: WACV 2026 (main)

Abstract: Video anomaly detection (VAD) is crucial for video analysis and surveillance in computer vision. However, existing VAD models rely on learned normal patterns, which makes them difficult to apply to diverse environments. Consequently, users should retrain models or develop separate AI models for new environments, which requires expertise in machine learning, high-performance hardware, and extensive data collection, limiting the practical usability of VAD. To address these challenges, this study proposes customizable video anomaly detection (C-VAD) technique and the AnyAnomaly model. C-VAD considers user-defined text as an abnormal event and detects frames containing a specified event in a video. We effectively implemented AnyAnomaly using a context-aware visual question answering without fine-tuning the large vision language model. To validate the effectiveness of the proposed model, we constructed C-VAD datasets and demonstrated the superiority of AnyAnomaly. Furthermore, our approach showed competitive results on VAD benchmarks, achieving state-of-the-art performance on UBnormal and UCF-Crime and surpassing other methods in generalization across all datasets.
Global Focal and Radial Distortion Averaging from Radial Fundamental Matrices for Robust Self-Calibration
Authors: Sergei Solonets; Daniil Sinitsyn; Daniel Cremers
Venue: WACV 2026 (main)

Abstract: Classical self-calibration techniques either perform computationally expensive bundle adjustment to estimate all camera parameters or initialize the focal lengths alone by globally averaging fundamental matrices, typically ignoring radial distortion. Both strategies can degrade accuracy in large-scale Structure from Motion pipelines.We present a calibration method that overcomes these limitations by averaging focal lengths and radial distortion parameters using radial fundamental matrices. This method avoids costly point-wise optimization. Our algorithm minimizes the geometric distance between an observed fundamental matrix and the essential-matrix manifold. This provides a mathematically consistent and highly scalable framework for calibrating the camera's intrinsic parameters.Experiments on diverse real-world datasets demonstrate that our joint estimator provides more precise focal length and distortion parameter estimates than existing methods. Furthermore, we demonstrate that naive, independent distortion averaging is suboptimal, which reinforces the importance of joint focal-radial estimation. These results underscore the importance of incorporating radial distortion averaging into modern self-calibration methods to improve reconstruction accuracy and stability.
Enhancing Reverse Distillation with Core Exemplar Learning for Unified Multi-Class Anomaly Detection
Authors: Heechul Lim; Min-Soo Kim; Hyun-Boo Lee; Suk-Ju Kang; Kang-Wook Chon; Haeyun Lee
Venue: WACV 2026 (main)

Abstract: In electronics manufacturing, anomaly detection methods face significant challenges due to class distribution imbalance and training instability when handling multiple classes simultaneously under varying imaging conditions. To address these challenges, we propose Reverse Distillation with Core-Exemplar Learning (RDCEL), a unified anomaly detection framework incorporating domain adaptation and novel metric learning strategies. RDCEL integrates unsupervised domain adaptation to align the covariance of the source and target domains and uses soft label-based coreset learning to handle diverse class distributions. It also leverages a coreset repulsion loss to minimize redundancy among coreset representations, fostering a more stable and dispersed embedding space across multiple classes. By aligning spatial statistics across different classes, RDCEL effectively addresses inter-class discrepancies, enabling consistent anomaly scoring under a unified test setting. Extensive experiments show RDCEL significantly outperforms state-of-the-art methods on MVTec AD and VisA datasets, achieving superior accuracy, stable AUROC performance, and faster convergence.
AFL-PRF: Adaptive Federated Learning for Low-Quality Data: Enhancing Performance, Robustness, and Fairness
Authors: Pinrui Yu; Yiming Xie; Longtian Ye; Geng Yuan; Ningfang Mi; Xue Lin
Venue: WACV 2026 (main)

Abstract: Federated learning (FL) enables collaborative model training across distributed clients while preserving privacy, yet its decentralized nature makes it vulnerable to poisoned updates and performance degradation under highly skewed data. Prior studies typically treat accuracy, robustness, and fairness separately, leaving open the challenge of a unified solution. We propose AFL-PRF, an adaptive federated learning framework that simultaneously enhances accuracy, robustness, and fairness in adversarial and heterogeneous environments. AFL-PRF integrates three key techniques. First, an exponential adaptive weighting mechanism dynamically scales client updates, suppressing poisoned or unreliable contributions while retaining meaningful signals from benign but low-quality clients. Second, a client prioritization strategy guided by the novel Weight Update Divergence (WUD) score promotes reliable updates and their benign neighbors, preventing malicious gradients from dominating aggregation. Third, sensitivity profiling identifies fully connected (FC) layers as highly vulnerable due to large weight variance, motivating a selective clipping strategy that filters extreme updates in these layers while preserving normal learning dynamics. Extensive experiments on benchmark datasets demonstrate that AFL-PRF consistently outperforms state-of-the-art baselines, achieving over 30% improvement in robustness and 20% enhancement in fairness, while maintaining superior predictive accuracy. By unifying adaptive weighting, client prioritization, and targeted clipping, AFL-PRF establishes a new benchmark for federated learning under poisoned and highly non-IID conditions.
LightGazeNet: A Lightweight GNN-based Architecture for Gaze Estimation
Authors: Heena Patel; Anirban Chowdhury; Pooja Jigar Choksy; Samiksha Pradeep Pachade; Ajinkya Puar
Venue: WACV 2026 (main)

Abstract: Gaze estimation remains a fundamental yet challenging task, requiring a careful balance between accuracy and efficiency for real-world deployment. We introduce LightGazeNet, a lightweight Graph Neural Network (GNN) framework designed for appearance-based gaze estimation. LightGazeNet effectively integrates multi-modal inputs--including facial features, eye cues, 3D eye centers, head pose, and calibration data--within a compact graph-based architecture. To enhance feature fusion across heterogeneous inputs, the framework leverages multi-head attention to model complex spatial dependencies. Extensive evaluations on multiple benchmark datasets show that LightGazeNet achieves competitive or superior accuracy with significantly fewer parameters than existing methods. Furthermore, it demonstrates strong cross-dataset generalization, with calibration-based adaptation improving robustness under domain shift. By combining accuracy, efficiency, and adaptability, LightGazeNet offers a practical solution for gaze estimation in real-world settings while advancing graph-based modeling in computer vision.
CountingDINO: A Training-free Pipeline for Class-Agnostic Counting using Unsupervised Backbones
Authors: Giacomo Pacini; Lorenzo Bianchi; Luca Ciampi; Nicola Messina; Giuseppe Amato; Fabrizio Falchi
Venue: WACV 2026 (main)

Abstract: Class-agnostic counting (CAC) aims to estimate the number of objects in images without being restricted to predefined categories. However, while current exemplar-based CAC methods offer flexibility at inference time, they still heavily rely on labeled data for training, which limits scalability and generalization to many downstream use cases. In this paper, we introduce CountingDINO, the first training-free exemplar-based CAC framework that exploits a fully unsupervised feature extractor. Specifically, our approach employs self-supervised vision-only backbones to extract object-aware features, and it eliminates the need for annotated data throughout the entire proposed pipeline. At inference time, we extract latent object prototypes via ROI-Align from DINO features and use them as convolutional kernels to generate similarity maps. These are then transformed into density maps through a simple yet effective normalization scheme. We evaluate our approach on the FSC-147 and CARPK benchmarks, where we consistently outperform a baseline based on an SOTA unsupervised object detector under the same label- and training-free setting. Additionally, we achieve competitive results -- and in some cases surpass -- training-free methods that rely on supervised backbones, non-training-free unsupervised methods, as well as several fully supervised SOTA approaches. This demonstrates that label- and training-free CAC can be both scalable and effective. Website: https://lorebianchi98.github.io/CountingDINO/.
ENCORE : A Neural Collapse Perspective on Out-of-Distribution Detection in Deep Neural Networks
Authors: A. Q. M. Sazzad Sayyed; Nathaniel D. Bastian; Francesco Restuccia
Venue: WACV 2026 (main)

Abstract: Out-of-Distribution (OOD) detection is of paramountimportance in guaranteeing safe and reliable deploymentof a Deep Neural Network (DNN) model in real-world set-tings. However, most OOD detection approaches still lackmotivation rooted in established properties of the DNNs.This disconnect between the proposed approach and theo-retical underpinning to measurable DNN properties makesthese approaches unreliable. To bridge this gap, we takea different perspective to using energy scoring for OODdetection. Specifically, we look at energy score throughthe lens of the properties of neural collapse and observethat simple feature scaling can improve the separation be-tween In-Distribution (ID) and OOD inputs. Based on thisobservation, we propose ENCORE , which scales featuresof each input adaptively and uses them to obtain modi-fied logits based on insights from theory of neural collapse.We show that ENCORE outperforms state-of-the-art ap-proaches across a variety of benchmarks; for example, by1.37% on CIFAR10 and by 1.07% on Imagenet benchmarks.
FAE-Net: Fashion Attribute Editing via Disentangled Latent Conditioning in Diffusion Models
Authors: P. Rajith Bhargav; Gaurab Bhattacharya; B S Vivek; Jayavardhana Gubbi
Venue: WACV 2026 (main)

Abstract: Image editing using generative models has recently advanced through GAN- and diffusion-based techniques. While the current image manipulation methods shows considerable performance in general image editing, their effectiveness drops when extended to fashion attribute editing. This is due to multiple challenges such as category-specific and overlapping attributes, inherent entanglement in real-world datasets that leads to degraded editing quality and unintended attribute shifts. To address these challenges, we propose FAE-Net (Fashion Attribute Editing Network), a latent diffusion framework that leverages disentangled latent projections for precise and reliable attribute manipulation. Our method first disentangles the latent projections to mitigate the inherent entanglement in the data and then conditions the diffusion model with those projections to improve the manipulation control in the presence of overlapping attributes. The attribute presence detector in FAE-Net handles category-specific attributes and prevents invalid attribute manipulations during inference. Extensive experiments on three large-scale datasets demonstrate that our proposed method achieves more controllable, disentangled, and faithful attribute editing compared to state-of-the-art methods.
Robust Multimodal Emotion Recognition from Incomplete Modalities via Query-Based Unimodal and Cross-Modal Learning
Authors: Ryo Miyoshi; Mayu Otani; Yuki Okafuji
Venue: WACV 2026 (main)

Abstract: Multimodal emotion recognition (MER) aims to identify human emotions from inputs such as text, vision, and audio. However, existing methods often assume complete modality availability during training and inference, which is unrealistic in real-world scenarios due to sensor failures or privacy constraints.We propose Dual-Query Fusion (DQF), a framework that enables robust MER using only incomplete modality inputs, without relying on reconstruction or knowledge distillation.DQF introduces two types of learnable queries: Q-UA for extracting informative unimodal features, and Q-CA for adaptive cross-modal integration. These modules are designed to operate effectively even when some modalities are missing.Experiments on two public datasets demonstrate that DQF achieves superior performance and robustness compared to existing methods, even when trained exclusively on incomplete inputs. These results highlight the effectiveness and practicality of DQF for real-world MER tasks.
R3: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domain
Authors: Nate Rothschild; Moshe Kimhi; Avi Mendelson; Chaim Baskin
Venue: WACV 2026 (main)

Abstract: Image reconstruction from corrupted (rain) images is crucial across many domains. Most deraining networks are trained on **post-ISP** RGB images, eventhough the image-signal-processing pipeline irreversibly mixes colors,clips dynamic range and blurs fine detail. This paper indicates that these lossesare avoidable and show that learning **directly on raw Bayermosaics** yields superior reconstructions from a **single** camera.To substantiate the claim we (i) curate **Raw-Rain**, the firstpublic benchmark of real rainy scenes captured in both 12-bit Bayer andbit-depth-matched sRGB, (ii) design a lightweight U-Net that ingests thesingle-channel Bayer tensor, and (iii) introduce InformationConservation Score (**ICS** , a color-invariant metric that aligns moreclosely with human opinion than PSNR or SSIM. On the test split ourraw-domain model improves RGB results by up to **+0.99 dB PSNR and +1.2 % ICS**, while running faster with half of the GFLOPs. The results advocate an ISP-lastparadigm for low-level vision and open the door to end-to-end learnablecamera pipelines.
Joint Optimization of Camera Model and Deep Neural Network for Image Recognition
Authors: Youta Noboru; Yuko Ozasa; Masayuki Tanaka
Venue: WACV 2026 (main)

Abstract: In this paper, we propose joint optimization of a camera model and a deep neural network (DNN) for image classification and object detection tasks. The camera model consists of an image sensor model which is parameterized by the camera spectral sensitivity (CSS) and an image signal processing (ISP) model. We assume the camera model is composed of a three-sensor imager without demosaicing process and an ISP with simple color correction and gamma correction. The DNNs follow to classify or detect objects. A key contribution of this paper is the joint optimization of not only the ISP model and DNN but also the image sensor model. For stable joint optimization, we have implemented a fully differentiable camera model. Therefore, we can jointly optimize the camera model and the DNN. Experimental comparisons with the flower and leaf datasets show that our approach outperforms existing approaches. Furthermore, we demonstrate that our approach is also effective for the object detection task. The source code will be made publicly available upon publication of this paper.
SCORP: Scene-Consistent Object Refinement via Proxy Generation and Tuning
Authors: Ziwei Chen; Ziling Liu; Zitong Huang; Mingqi Gao; Feng Zheng
Venue: WACV 2026 (main)

Abstract: Viewpoint missing of objects is common in scene reconstruction, as camera paths typically prioritize capturing the overall scene structure rather than individual objects. This makes it highly challenging to achieve high-fidelity object-level modeling while maintaining accurate scene-level representation. Addressing this issue is critical for advancing downstream tasks requiring high-fidelity object reconstruction. In this paper, we introduce Scene-Consistent Object Refinement via Proxy Generation and Tuning (SCORP), a novel 3D enhancement framework that leverages 3D generative priors to recover fine-grained object geometry and appearance under missing views. Starting with proxy generation by substituting degraded objects using a 3D generation model, SCORP then progressively refines geometry and texture by aligning each proxy to its degraded counterpart in 7-DoF pose, followed by correcting spatial and appearance inconsistencies through registration-constrained enhancement. This two-stage proxy tuning ensures the high-fidelity geometry and appearance of the original object in unseen views while maintaining consistency in spatial positioning, observed geometry, and appearance. Across challenging benchmarks, SCORP achieves consistent gains over recent state-of-the-art baselines on both novel view synthesis and geometry completion tasks. SCORP is available at https://github.com/PolySummit/SCORP.
DirectDrag: High-Fidelity, Mask-Free, Prompt-Free Drag-based Image Editing via Readout-Guided Feature Alignment
Authors: Sheng-Hao Liao; Shang-Fu Chen; Tai-Ming Huang; Wen-Huang Cheng; Kai-Lung Hua
Venue: WACV 2026 (main)

Abstract: Drag-based image editing using generative models provides intuitive control over image structures. However, existing methods rely heavily on manually provided masks and textual prompts to preserve semantic fidelity and motion precision. Removing these constraints creates a fundamental trade-off: visual artifacts without masks and poor spatial control without prompts. To address these limitations, we propose DirectDrag, a novel mask- and prompt-free editing framework. DirectDrag enables precise and efficient manipulation with minimal user input while maintaining high image fidelity and accurate point alignment. DirectDrag introduces two key innovations. First, we design an Auto Soft Mask Generation module that intelligently infers editable regions from point displacement, automatically localizing deformation along movement paths while preserving contextual integrity through the generative model's inherent capacity. Second, we develop a Readout-Guided Feature Alignment mechanism that leverages intermediate diffusion activations to maintain structural consistency during point-based edits, substantially improving visual fidelity. Despite operating without manual mask or prompt, DirectDrag achieves superior image quality compared to existing methods while maintaining competitive drag accuracy. Extensive experiments on DragBench and real-world scenarios demonstrate the effectiveness and practicality of DirectDrag for high-quality, interactive image manipulation. Project Page: https://frakw.github.io/DirectDrag/. Code is available at: https://github.com/frakw/DirectDrag.
Single-step Diffusion for Image Compression at Ultra-Low Bitrates
Authors: Chanung Park; Joo Chan Lee; Jong Hwan Ko
Venue: WACV 2026 (main)

Abstract: Although there have been significant advancements in image compression techniques, such as standard and learned codecs, these methods still suffer from severe quality degradation at extremely low bits per pixel. While recent diffusion-based models provided enhanced generative performance at low bitrates, they often yields limited perceptual quality and prohibitive decoding latency due to multiple denoising steps.In this paper, we propose the first single-step diffusion model for image compression that delivers high perceptual quality and fast decoding at ultra-low bitrates. Our approach incorporates two key innovations: (i) Vector-Quantized Residual (VQ-Residual) training, which factorizes a structural base code and a learned residual in latent space, capturing both global geometry and high-frequency details; and (ii) rate-aware noise modulation, which tunes denoising strength to match the desired bitrate. Extensive experiments show that ours achieves comparable compression performance to state-of-the-art methods while improving decoding speed by about 50x compared to prior diffusion-based methods, greatly enhancing the practicality of generative codecs.
TED-4DGS: Temporally Activated and Embedding-based Deformation for 4DGS Compression
Authors: Cheng-Yuan Ho; He-Bi Yang; Jui-Chiu Chiang; Yu-Lun Liu; Wen-Hsiao Peng
Venue: WACV 2026 (main)

Abstract: Building on the success of 3D Gaussian Splatting (3DGS) in static 3D scene representation, its extension to dynamic scenes, commonly referred to as 4DGS or dynamic 3DGS, has attracted increasing attention. However, designing more compact and efficient deformation schemes together with rate-distortion-optimized compression strategies for dynamic 3DGS representations remains an underexplored area. Prior methods either rely on space-time 4DGS with overspecified, short-lived Gaussian primitives or on canonical 3DGS with deformation that lacks explicit temporal control. To address this, we present TED-4DGS, a temporally activated and embedding-based deformation scheme for rate-distortion-optimized 4DGS compression that unifies the strengths of both families. TED-4DGS is built on a sparse anchor-based 3DGS representation. Each canonical anchor is assigned learnable temporal-activation parameters to specify its appearance and disappearance transitions over time, while a lightweight per-anchor temporal embedding queries a shared deformation bank to produce anchor-specific deformation. For rate-distortion compression, we incorporate an implicit neural representation (INR)-based hyperprior to model anchor attribute distributions, along with a channel-wise autoregressive model to capture intra-anchor correlations. With these novel elements, our scheme achieves state-of-the-art rate-distortion performance on several real-world datasets. To the best of our knowledge, this work represents one of the first attempts to pursue a rate-distortion-optimized compression framework for dynamic 3DGS representations.
The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs
Authors: Tejas Anvekar; Fenil Bardoliya; Pavan K. Turaga; Chitta Baral; Vivek Gupta
Venue: WACV 2026 (main)

Abstract: Recent advances in multimodal large language models (MLLMs) have yielded increasingly powerful models, yet their perceptual capacities remain poorly characterized. In practice, most model families scale language component while reusing nearly identical vision encoders (e.g., Qwen2.5-VL 3B/7B/72B), which raises pivotal concerns about whether progress reflects genuine visual grounding or reliance on internet-scale textual world knowledge. Existing evaluation methods emphasize end-task accuracy, overlooking robustness, attribution fidelity, and reasoning under controlled perturbations. We present The Perceptual Observatory, a framework that characterizes MLLMs across verticals like: (i) simple vision tasks, such as face matching and text-in-vision comprehension capabilities; (ii) local-to-global understanding, encompassing image matching, grid pointing game, and attribute localization, which tests general visual grounding. Each vertical is instantiated with ground-truth datasets of faces and words, systematically perturbed through pixel-based augmentations and diffusion-based stylized illusions. The Perceptual Observatory moves beyond leaderboard accuracy to yield insights into how MLLMs preserve perceptual grounding and relational structure under perturbations, providing a principled foundation for analyzing strengths and weaknesses of current and future models.