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ECCV 2026

Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum

Zhizhao Liang, Yi-Lin Wei, Xuhang Chen, Mu Lin, Yi-Xiang He, Zhexi Luo, Jun-Hui Liu, Kun-Yu Lin, Weishi Zheng

In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this taskposes two key challenges: 1) Spatial understanding is challenging in com-plex 3D environments with diverse spatial relations. 2) Action generationis difficult to generalize, as limited and costly real-robot data restrictsdata-driven models generalization. To address these challenges, we pro-pose a generalizable humanoid loco-manipulation framework that lever-ages the spatial perception and action generation capabilities of multi-agent large models. Specifically, our framework includes two components:Active Spatial Brain for active spatial perception and decision-making,and Generalizable Action Cerebellum for executable robot action genera-tion. The first component actively perceives the spatial scene and makesdecisions on task planning and subtask decomposition. The second com-ponent generates executable robot actions based on the decisions madeby the first module without needs of task-specific real robot data. Tobenchmark our framework, we design a set of spatial manipulation tasksfrom two perspectives: evaluating spatial perception and understand-ing, and assessing real-robot task performance. The results demonstratestrong performance on both aspects across diverse tasks and environ-ments.

ECCV 2026

HoloTetSphere: Unified TetSphere Mesh Reconstruction for Physical Simulations

Yaqiao Dai, Renjiao Yi, Zhirui Gao, Wei Chen, Kai Xu, Chenyang Zhu

Standard pipelines for physics-ready 3D reconstruction relyon a decoupled two-stage paradigm: extracting surface geometry followedby an error-prone tetrahedralization process. While recent Lagrangianmethods like TetSphere Splatting attempt to bypass this by directlyoptimizing volumetric primitives, their homeomorphic constraints pre-vent topology-adaptive optimization. Consequently, they produce dis-joint tetrahedra rather than a single connected mesh, rendering thestructures unsuitable for further physical simulations. To address this,we propose a topology-adaptive framework for holistic tetrahedral meshreconstruction through end-to-end topological and geometric optimiza-tion. First, by coupling Gaussian spheres to tetrahedral elements andleveraging edge connections, we estimate a continuous opacity field fordifferentiable element pruning. Next, jointly minimizing mesh smoothingenergy and multi-view Gaussian rendering error drives alternating geo-metric refinement while preserving topological adaptivity. Consequently,our approach effectively constructs a unified and topologically coherenttetrahedral mesh. Extensive experiments demonstrate that our methodoutperforms state-of-the-art techniques by achieving superior geometricaccuracy and producing coherent, single-connected tetrahedral meshes,thereby effectively bypassing the error-prone conventional tetrahedraliza-tion step for reconstructed surface meshes and streamlining downstreamphysical simulation.

ECCV 2026

ExPLoRe: Expert Patch-Level Loss Routing for Multi-Objective Masked Image Modeling

Konstantinos Georgiou, Maofeng Tang, Hairong Qi

Multi-objective masked image modeling (MIM) combines com-plementary learning signals (token distillation, CLS alignment, and pixelreconstruction) but existing methods weight these objectives with globalscalars, ignoring spatial heterogeneity across patches. We present Ex-PLoRe (Expert Patch-Level Loss Routing), which repurposes Soft Mix-ture of Experts (MoE) dispatch weights as learned, per-patch loss coef-ficients. The key mechanism is loss-coupling: allowing loss gradients toflow through dispatch weights to the router enables content-dependentspecialization, where different patches receive different emphases acrossobjectives. A detach ablation confirms loss-coupling as the core mecha-nism, degrading performance by 1.6% when gradients are blocked. OnImageNet-1K with ViT-Base, ExPLoRe improves over non-MoE base-lines on two objective combinations (Token+CLS: +0.5% k-NN, +4.4%linear probe; Token+Pixel: +2.2% k-NN), achieving 80.6% linear probeand 85.3% finetuning accuracy, competitive with published methods. Fordownstream transfer, we develop adaptation recipes (Freeze Routing, Ex-pert Dropout, and Freeze Attention) that improve MoE finetuning by+1.5% over the vanilla MoE, and close a 2.5–2.9 mIoU segmentation gapso that MoE models match or exceed non-MoE baselines on ADE20K.

ECCV 2026

Video Generative Models as Geometry Learner

Haosen Yang, Song Jifei, Zhensong Zhang, Xiatian Zhu, Jiankang Deng

Recent generative approaches to geometry estimation adaptpretrained image diffusion models and treat the task as image-conditionedgeneration. Leveraging off-the-shelf image diffusion models, they either(i) train task-specific geometry models (for depth and surface normal es-timation) independently, losing the opportunity of exploring the intrinsiccorrelation of these geometric targets, or (ii) jointly fine-tune modifiedimage diffusion backbones (e.g., altered self-attention), which typicallydemands substantial labeled data. To overcome these limitations in aprincipled fashion, we repurpose pretrained video generative models as aunified and data-efficient framework for geometry estimation, formulatedinnovatively as a next-frames prediction task. Our method, GeoNeXt,inherits naturally structured knowledge and richer priors from the videomodel, while further adapting them for joint modeling of images and ge-ometry targets (

ECCV 2026

GameWorlds: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents

Mingyu Ouyang, Siyuan Hu, Qinghong Lin, Hwee Tou Ng, Mike Zheng Shou

Towards an embodied generalist for real-world interaction,Multimodal Large Language Model (MLLM) agents still suffer fromchallenging latency, sparse feedback, and irreversible mistakes. Videogames offer an ideal testbed with rich visual observations and closed-loopinteraction, demanding fine-grained perception, long-horizon planning,and precise control. However, systematically evaluating these capabilitiesis currently hindered by heterogeneous action interfaces and heuristicverification. To this end, we introduce GameWorld, a benchmarkdesigned for standardized and verifiable evaluation of MLLMs as generalistgame agents in browser environments. Two game agent interfaces arestudied: (i) Computer-use agents that directly emit keyboard and mousecontrols, and (ii) Generalist multimodal agents that act in a semanticaction space via deterministic Semantic Action Parsing. GameWorldcontains 34 diverse games and 170 tasks, each paired with state-verifiable metrics for outcome-based evaluation. The results across 18model-interface pairs suggest that even the best-performing agent isfar from achieving human capabilities on video games. Further studieson real-time interaction, context-memory sensitivity, and action validityexpose more challenges ahead for game agents. Together, by offering astandardized and verifiable evaluation framework, GameWorld laysa foundation for advancing research on multimodal game agents andbeyond.

ECCV 2026

SubSplat: High-Resolution Pixel-aligned 3DGS via Sub-pixel Gaussian Reparameterization

Jiun Lee, Jaekwang Kim, Sangmin Lee

Pixel-aligned Gaussian splatting enables efficient and gen-eralizable novel-view synthesis. However, high-resolution rendering facesa critical trade-off where increasing input resolution improves detail atthe expense of quadratically rising network computational cost. Con-versely, maintaining low-resolution inputs stabilizes this cost but resultsin insufficient Gaussian density and artifacts. To address this, we proposeSubSplat, which introduces Sub-pixel Gaussian Reparameterizer(SPGR)to subdivide primary Gaussians into fine-grained primitives, restoringstructural density directly from low-resolution features. We further en-hance the reparameterization quality through feature aggregation, whicheffectively captures high-frequency details across multiple views. Experi-ments on RealEstate10K and ACID demonstrate that SubSplat achieveshigh-fidelity rendering with superior efficiency. Our results validate thatthe proposed framework successfully resolves the trade-off between repa-rameterization fidelity and network computational cost inherent in pixel-aligned Gaussian Splatting.

ECCV 2026

Pixel-wise Geo-registration of Drone and Satellite Images

Qingyang Liu, David G Shatwell, Parth Parag Kulkarni, Shah Mubarak

Cross-view geo-registration is the task of aligning a queryimage to a geodetically accurate reference (e.g., satellite image), so thateach query pixel maps to a real-world GPS coordinate. Most existingwork addresses the related problem of cross-view geo-localization, wherethe goal is typically to estimate the camera center using retrieval, classi-fication, matching, or regression. Because these approaches do not pro-vide dense supervision, they are poorly suited for learning and eval-uating pixel-wise alignment. We introduce SkyReg, a geometry-awaregeo-registration model that estimates the transformation between thequery and reference images by explicitly modeling the 3D scene ge-ometry. Applying this transformation to warp the query into the ref-erence frame yields pixel-wise geo-localization, without relying on 2Dpoint matches, and remains robust to occlusions and large viewpointchanges. To enable training and standardized evaluation, we release (i)SkyReg-Train, a large-scale dataset of drone–satellite images annotatedwith per-pixel GPS coordinates, depth maps, and camera parameters de-rived from LiDAR and structure-from-motion, and (ii) SkyReg-Bench, aheld-out benchmark of unseen Urban and Suburban scenes with the samedense annotations. SkyReg achieves state-of-the-art performance againststrong retrieval and homography baselines, demonstrating the value ofgeometry-aware models and dense geodetic benchmarks for cross-viewgeo-registration. Dataset available at https://parthpk.github.io/skyreg-webpage.

ECCV 2026

Proto-Gaussian: MRI Modality Translation Based on Learnable Structural Prototypes and 2D Gaussian Splatting

Zizheng Li, RenDong Xie, Huadeng Wang, Zhifen He, Bin Liu, Bo Li, Xiaonan Luo

Medical image modality translation is hindered by the entan-glement of geometry and modality-specific textures, often causing struc-tural distortions. Existing methods operate at the pixel or feature level,failing to separate geometry from appearance and yielding anatomicallyinconsistent results. We present Proto-Gaussian, a novel MRI modalitytranslation framework that explicitly disentangles structural geometryfrom modality-specific appearance using 2D Gaussian image representa-tion, surpassing conventional pixel or patch-based approaches in preserv-ing anatomical consistency. The framework operates in two stages: first, ashared Gaussian Bank captures modality-invariant geometric prototypesthrough 2D Gaussian primitives and self-supervised learning; second, aLabel-Adaptive Token Refinement module maps texture features acrossmodalities while leveraging the learned geometric structures, achievingclear structure-texture separation. Finally, a fully differentiable 2D Gaus-sian splatting renderer synthesizes high-fidelity target-modality images,maintaining both structural accuracy and visual realism. Extensive ex-periments on brain MRI datasets demonstrate the superiority of ourmethod through quantitative and qualitative comparisons with state-of-the-art approaches.

ECCV 2026

TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging

Guangyu Meng, Xiaowei Xu, Xueyang Li, Yiyu Shi, Erin Chambers, Danny Chen

Topological data analysis (TDA), particularly persistent ho-mology (PH), captures geometric structural properties in medical im-ages (e.g., connected components, loops, shape characteristics), whichconventional pixel-level deep learning approaches often neglect. Whilemany topological descriptors are known for converting persistence di-agrams (PDs) or raw images into topological feature vectors, existingmethods mostly default to a single fixed descriptor (e.g., persistenceimages), leaving the diversity of topological representations largely un-explored. To the best of our knowledge, there is no known large lan-guage model (LLM)-based agentic framework that can automaticallydetermine the most suitable topological descriptors for a given imagedataset and produce the corresponding topological feature vectors fordownstream tasks. To fill this gap, we propose TopoAgent, an LLM-based agentic framework that automates topology learning for medicalimage analysis. TopoAgent operates through a Perception–Reasoning–Action–Reflection loop supported by 21 domain-specific tools and dualmemory that accumulates experience across runs. Its skill set is dis-tilled from systematic evaluation of 15 topological descriptors across 26datasets with six classifiers. TopoAgent analyzes input images and theirtopological characteristics, reasons about which topological descriptorsbest suit the input, and determines the optimal descriptor and its con-figuration, all without task-specific training. To evaluate TopoAgent, weintroduce TopoBenchmark, a frozen benchmark of 113,182 samplesfrom 26 medical image datasets spanning five object types: cells, glandsand lumens, organ shapes, vessel trees, and surface lesions. Experimentsshow that TopoAgent obtains 68.21% average balanced accuracy, out-performing the strongest baseline by 9.32% and general-purpose LLMsequipped with the same tools by over 21%.

ECCV 2026

NeSy-Route: A Neural-Symbolic Benchmark for Constrained Route Planning in Remote Sensing

Ming Yang, Zhi Zhou, Shi-Yu Tian, Kun-Yang Yu, Lan-Zhe Guo, Yu-Feng Li

Remote sensing underpins crucial applications such as disas-ter relief and ecological field surveys, where systems must understandcomplex scenes and constraints and make reliable decisions. Currentremote-sensing benchmarks mainly focus on evaluating perception andreasoning capabilities of multimodal large language models (MLLMs).They fail to assess planning capability, stemming either from the diffi-culty of curating and validating planning tasks at scale or from evaluationprotocols that are inaccurate and inadequate. To address these limita-tions, we introduce NeSy-Route, a large-scale neuro-symbolic benchmarkfor constrained route planning in remote sensing. Within this benchmark,we introduce an automated data-generation framework that integrateshigh-fidelity semantic masks with heuristic search to produce diverseroute-planning tasks with provably optimal solutions. This allows NeSy-Route to comprehensively evaluate planning across 10,821 route-planningsamples, nearly 10 times larger than the largest prior benchmark. Fur-thermore, a three-level hierarchical neuro-symbolic evaluation protocol isdeveloped to enable accurate assessment and support fine-grained analy-sis on perception, reasoning, and planning simultaneously. Our compre-hensive evaluation of various state-of-the-art MLLMs demonstrates thatexisting MLLMs show significant deficiencies in perception and planningcapabilities. We hope NeSy-Route can support further research and de-velopment of more powerful MLLMs for remote sensing.The dataset andcode are available at https://mingyang1010.github.io/NeSy-Route/.

ECCV 2026

StAR: Segment Anything Reasoner

Seokju Yun, Dongheon Lee, Noori Bae, Jaesung Jun, Chanseul Cho, Youngmin Ro

As AI systems are being integrated more rapidly into di-verse and complex real-world environments, the ability to perform holis-tic reasoning over an implicit query and an image to localize a target isbecoming increasingly important. However, recent reasoning segmenta-tion methods fail to sufficiently elicit the visual reasoning capabilities ofthe base model. In this work, we present Segment Anything Reasoner(StAR), a comprehensive framework that refines the design space frommultiple perspectives—including parameter-tuning scheme, reward func-tions, learning strategies and answer format—and achieves substantialimprovements over recent baselines. In addition, for the first time, wesuccessfully introduce parallel test-time scaling to the segmentation task,pushing the performance boundary even further. To eXtend the scopeand depth of reasoning covered by existing benchmark, we also con-struct the ReasonSeg-X, which compactly defines reasoning types and in-cludes samples that require deeper reasoning. Leveraging this dataset, wetrain StAR with a rollout-expanded selective-tuning approach to activatethe base model’s latent reasoning capabilities, and establish a rigorousbenchmark for systematic, fine-grained evaluation of advanced methods.With only 5k training samples, StAR achieves significant gains over itsbase counterparts across extensive benchmarks, demonstrating that ourmethod effectively brings dormant reasoning competence to the surface.

ECCV 2026

Beyond Filter Pruning: Top-K Spatial Selection for Efficient Neural Networks

Sarthak Ketanbhai Modi, Hans Soegeng, Thomas Peyrin

Deep neural networks have shown remarkable performance across diverse domains, but their substantial model size and computational requirements hinder deployment in memoryand computationconstrained environments. Despite progress in pruning, existing methods primarily target inter-filter redundancy and leave significant spatial redundancy within convolutional kernels unaddressed. The resulting models retain many correlated spatial patterns, limiting achievable efficiency gains. We propose Top-K Pruning (TKP), a pruning framework that extends sparsity to the spatial dimension by retaining only the most informative positions within each convolutional kernel. TKP introduces a two-stage procedure: an auxiliary regularization phase that concentrates information into dominant spatial locations, followed by deterministic Top-K selection that yields semi-structured sparsity. This transforms dense convolutions into efficient selective-sampling operations with strictly bounded computational cost. Across diverse architectures, including CNNs, vision transformers, vision-language models, and a large language model, TKP consistently achieves strong accuracy–efficiency trade-offs. On CIFAR and ImageNet models, TKP matches or exceeds the accuracy of prior structured pruning methods while delivering up to 8.9× theoretical FLOP reduction. TKP remains robust under quantization-aware training, achieving 16–17× compression with minimal accuracy loss on ResNet–18 and VGG–19. Moreover, TKP generalizes to BLIP-Base and LLaMA-2-7B, outperforming state-of-the-art pruning baselines. These results highlight TKP as a simple and effective approach for removing spatial redundancy in modern vision and largescale models.

ECCV 2026

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging

Kuangpu Guo, Aijing Yu, Jian Liang, Yuhe Ding, Zilei Wang, Ran He, Tieniu Tan

Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, traditional basic merging methods often experience performance degradation due to parameter conflicts, even when applied to similar tasks. While recent personalized merging frameworks successfully preserve taskspecific information to maintain performance, they typically incur storage overhead. In this paper, we propose Decomposition, Thresholding, and Scaling (DTS), an approximation-based personalized merging framework that pushes task-specific storage efficiency. DTS first applies singular value decomposition to the task-specific information and retains only a small subset of singular values and vectors. It then introduces a novel thresholding strategy that partitions singular vector elements into groups and assigns a scaling factor to each group. To enable generalization to unseen tasks, we further extend DTS with a variant that fuses task-specific information in a data-free manner based on the semantic similarity of task characteristics. Extensive experiments demonstrate that DTS consistently outperforms state-of-the-art baselines while requiring only 1% extra storage per task. Furthermore, experiments on unseen tasks show that the DTS variant achieves significantly better generalization performance. Our code is available at https://github.com/krumpguo/DTS.

ECCV 2026

InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360° Image

Gwanhyeong Koo, Hyunsu Kim, Youngji Kim, Taejae Lee, Siwoo Lim, Sunjae Yoon, Suyong Yeon, Chang D. Yoo

Recent advances in single image-to-3D generation have en-abled high-quality asset synthesis, yet extending these capabilities toindoor scene generation remains challenging. Existing methods focus onasset-level generation while neglecting the structural layout, which isessential for downstream applications and serves as the spatial anchorfor grounding assets. However, a single image with a limited field ofview lacks the spatial coverage to recover a coherent global layout. Tothis end, we use a 360° image represented in equirectangular projec-tion (ERP) and propose InSpace, a structure-aware framework for 3Dindoor scene generation. InSpace comprises three stages: (1) estimat-ing partial scene geometry as spatial priors, (2) generating coarse scene* †Work done during an internship at NAVER LABS. Co-corresponding authorsArtifactsMisplacementMisplacementERP Image ERP ImageInput Result Floating Input Result ResultResultSceneGen SAM3D InSpace (Ours)(a) Current Single Image to 3D Scene Generation (b) ERP Image to 3D Scene GenerationFig. 2: (a) Existing single-image methods generate individual assets without structurallayout, causing floating, misplacement, and artifacts. (b) InSpace uses an ERP imageto generate complete indoor scenes with structural layout and well-grounded assets.structure with view-selective cross-attention, and (3) producing detailedlayout and asset geometry with textures through a global-local hybridattention, using flow matching. We also propose ERP-FRONT, a pairedERP-Image-to-3D indoor scene dataset based on 3D-FRONT. Exper-iments show that InSpace generates complete 3D indoor scenes withstructural layout, along with separate textured assets from a single ERPimage, achieving strong performance across 3D and 2D metrics.

ECCV 2026

TanGO: Training-Free 3D Editing via Tangent-Space Guidance and Optimization

Siwoo Lim, Sunjae Yoon, Gwanhyeong Koo, Hyeonseo Yun, Chang D. Yoo

While recent flow-matching 3D generative models (e.g., Vec-Set) adopt structured representations, their tokens share global context,causing conventional training-free editing to suffer from semantic arti-facts such as collapsed preserved regions or incomplete transformations.To address this, we propose TanGO, a training-free framework thatenables adaptive per-token steering in the tangent space of generativedynamics. To realize this selective control, we formulate a one-step opti-mal control rule and determine the strength of each token’s control signalusing a von Mises-Fisher inspired directional discrepancy derived fromthe source and target velocity fields. Experiments show that TanGOsubstantially reduces structural artifacts and achieves state-of-the-artperformance, outperforming existing 3D editing baselines. The code ispublicly available at https://github.com/siw00-lim/TanGO.

ECCV 2026

Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations

Andrii Zadaianchuk, Leonardo Barcellona, Lennard Schuenemann, Christian Gumbsch, Zehao Wang, Muhammad Zubair Irshad, Fabien Despinoy, Rahaf Aljundi, Efstratios Gavves, Sergey Zakharov

Accurately reconstructing complex full multi-object scenesfrom sparse observations remains a core challenge in computer vision anda key step toward scalable and reliable simulation for robotics. In thiswork, we introduce RecGen, a generative framework for probabilisticjoint estimation of object and part shapes, as well as their pose underocclusions and partial visibility from one or multiple RGB-D images. Byleveraging compositional synthetic scene generation and strong 3D shapepriors, RecGen generalizes across diverse object types and real-worldenvironments. RecGen achieves state-of-the-art performance on complex,heavily occluded datasets, robustly handling severe occlusions, symmetricobjects, objects parts, and intricate geometry and texture. Despite usingnearly 80% fewer training meshes than the previous state of the artSAM3D, RecGen outperforms it by 30.1% in geometric shape quality,9.1% in texture reconstruction, and 33.9% in pose estimation. We willrelease our code, training data and evaluation benchmark at our website.

ECCV 2026

SupIR-GS: Thermal Infrared Super-Resolution Novel View Synthesis with Imaging-Calibrated 3D Gaussian Splatting

Jin Liu, Haodong LI, Jiagang Chen, Dabin leng, Jiguang Li, Zhao Huang, Xiaoshuai Zhang, Qi Xu, Zhiwen Zheng, Xingru Huang

Thermal infrared (TIR) imaging facilitates perception, se-curity surveillance, and industrial inspection. Compared with visiblespectrum, TIR observations are typically resolution-limited and texture-scarce. More critically, thermal diffusion smooths true geometric bound-aries into low-frequency blur, whereas non-uniform sensor response andfixed-pattern noise introduce structured high-frequency artifacts, yield-ing a pronounced signal-to-noise inversion. Consequently, reconstructinga high-resolution (HR) 3D TIR scene and synthesizing high-quality novelviews from only low-resolution (LR) infrared inputs remains challeng-ing. To address this, we propose the first imaging-calibrated TIR super-resolution 3D Gaussian Splatting (3DGS) framework that reconstructshigh-resolution 3D TIR scenes using only LR inputs. Specifically, we ex-plicitly model infrared imaging degradation on the clean radiation signalsrendered by 3DGS using physics-informed priors, enabling decoupling ofthe underlying thermal radiation from sensor-induced artifacts. To allevi-ate texture scarcity, an intensity-conditioned tone adapter is designed toapply local affine residual modulation, thereby surfacing latent thermalgradients. Finally, we implement a frequency-aware curriculum learn-ing strategy that leverages edge priors from a super-resolution teachernetwork, and employs a cosine ramp-up schedule to smoothly shift theoptimization focus from coarse low-frequency structures to fine high-frequency details. Extensive evaluations across TIR benchmarks showthat our method significantly outperforms existing works in both high-resolution synthesis quality and quantitative temperature fidelity.

ECCV 2026

Unified and Efficient Point-Line Local Features

François Costa, Raphael Kreft, Felix Möller, Hardik Shah, Ramanathan Rajaraman, Eckhard Goedeke, Shaohui Liu, Remi Pautrat, Marc Pollefeys

Multi-view computer vision pipelines typically rely on accu-rate sparse keypoints and robust descriptors. While incorporating linefeatures has shown clear benefits for matching and pose estimation, exist-ing point-line approaches remain inefficient: they detect points and linesseparately, use increasingly heavy networks, and depend on CPU-boundheuristics that hinder real-time performance. We introduce a Unified Effi-cient Points and Lines (UPAL) feature extractor that jointly extracts key-points, line segments, and feature descriptors within a single lightweightarchitecture. A shared backbone provides common representations thatfeed different branches for point and line features. Line segments are recov-ered through an accelerated post-processing stage, an enhanced and highlyefficient variant of the LSD algorithm. UPAL matches or exceeds state-of-the-art performance in both point and line applications while significantlyreducing computational cost, achieving, for instance, a 4× speedup and10× smaller memory footprint over the ALIKED + DeepLSD pipeline.Code is publicly available at https://github.com/francois141/upal.

ECCV 2026

BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detection

Declan McIntosh, Alexandra Branzan Albu

Detecting anomalies in images and video is an essential task for multi-ple real-world problems, including industrial inspection, computer-assisted diag-nosis, and environmental monitoring. Anomaly detection is typically formulatedas a one-class classification problem, where the training data consists solely ofnominal values, leaving methods built on this assumption susceptible to train-ing label noise. We present Bootstrap Aggregation Anomaly Filtering (BAAF),a method that transforms an arbitrary one-class classifier-based anomaly detec-tor into a fully unsupervised method. This is achieved by leveraging the uniqueintrinsic properties of anomaly detection: anomalies are uncommon in the sam-pled data and generally heterogeneous. These properties enable us to design amodified Bootstrap Aggregation method that uses multiple independently trainedinstances of supervised one-class classifiers to filter the training dataset for anoma-lies. This transformation requires no modifications to the underlying anomaly de-tector; only the algorithmically selected data bags used for training change. Wedemonstrate empirically that our method can transform a wide variety of one-classclassifier-based image anomaly detectors into unsupervised ones. Consequently,we present the first fully unsupervised logical anomaly detection method for im-ages. We also demonstrate that our method achieves state-of-the-art performancein fully unsupervised anomaly detection on the MVTec AD, ViSA, Real-IAD andMvTec LOCO AD datasets. As improvements to one-class classifiers are made,our method directly transfers those improvements to the unsupervised domain,linking the domains.

ECCV 2026

Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning

Asim Ukaye, Nurbek Tastan, Mubarak Abdu-Aguye, Karthik Nandakumar

Federated learning (FL) enables collaborative learning ofcomputer vision models, where privacy and regulatory constraints pre-vent centralizing data across devices or organizations. However, prac-tical FL deployments often exhibit severe class imbalance and labelskew, causing standard aggregation protocols to overfit dominant clientsand degrade minority-class performance. We propose a data-free, class-wise contribution estimation and aggregation framework based on logitmaximization (CELM) that does not require raw data, client metadata,or auxiliary public datasets at the server. The FL server probes clientupdates to obtain class-wise evidence scores and assembles a cross-clientevidence matrix, which quantifies both per-class competence and classcoverage. Using this matrix, we compute contribution weights that up-weight clients providing discriminative evidence for underrepresentedclasses. The resulting aggregation is stable due to simplex constraints andmomentum smoothing, and remains compatible with standard FL train-ing pipelines. We evaluate the approach on representative vision bench-marks under controlled non-IID and pathological label splits, demon-strating that CELM-based aggregation improves robustness to imbalanceand statistical heterogeneity, while yielding better performance with-out requiring any additional data exchange. The code is available at:https://github.com/asimukaye/celm.

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