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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

PUF: Plug-and-Play Uncertainty-Aware Fusion for Online 3D Scene Graph Generation

Yi Yang, Myrna Castillo Silva, Bodo Rosenhahn, Michael Yang

Online 3D scene graph generation builds a persistent, struc-tured representation of a scene by incrementally fusing 2D observationsinto a global 3D graph. Existing online methods treat this fusion asa fully deterministic pipeline, where we identify three sources of un-certainty that are overlooked: observation, 2D model, and 3D repre-sentation. We propose PUF: a Plug-and-play, Uncertainty-aware, andtraining-free Fusion framework. Scene graph node association is refor-mulated as a probabilistic likelihood over semantic and spatial factors,replacing binary accept/reject gates. Dirichlet evidence accumulation dis-tributes class and relationship evidence across plausible candidates pro-portional to association likelihood. An optional class-conditional priorcompletes edges for sparsely or never co-observed object pairs. We instan-tiate PUF with both a 3D Gaussian and a 3D voxel backend and observeconsistent improvements, demonstrating its ability to generalize acrossdifferent representations. Experiments on the 3DSSG and ReplicaSSGbenchmarks show that our method substantially outperforms existingapproaches while maintaining real-time latency. These results establishuncertainty-aware fusion as a principled and effective paradigm for on-line 3D scene understanding. The source code is publicly available athttps://github.com/yyyyangyi/PUF.

ECCV 2026

Point Ladder Tuning: Parameter-Efficient Hierarchical Adaptation for 3D Point Cloud Understanding

Junlin Chang, Longhao Zou, Rui Li

Fine-tuning pre-trained point-cloud backbones typically up-dates all parameters, resulting in substantial computation and memoryoverhead. More importantly, modern point backbones rely on aggressivetokenization and downsampling, which yields compact global tokens butirreversibly discards fine-grained local geometry, an inherent bottleneckfor parameter-efficient adaptation. Consequently, existing PEFT meth-ods that operate only on these coarsened tokens can modulate globalsemantics but struggle to recover the missing multi-scale locality. Wepresent Point Ladder Tuning (PLT), a locality-aware PEFT frameworkthat performs hierarchical, instance-conditioned adaptation while keep-ing the backbone frozen. PLT forms a lightweight closed loop: (i) a Hier-archical Ladder Network (HLN) constructs a multi-resolution local fea-ture pyramid directly from raw points; (ii) a Local–Global Fusion (LGF)aligns and fuses local pyramids with intermediate backbone semantics;and (iii) a Dynamic Prompt Generator produces instance-aware multi-scale prompts to modulate the frozen backbone effectively. For denseprediction, we further introduce a lightweight segmentation head thatprogressively upsamples fused features and leverages backbone priors torefine fine structures. Extensive experiments on classification and denseprediction show that PLT consistently surpasses prior PEFT baselineswith minimal tunable parameters. PLT achieves state-of-the-art perfor-mance using only 2.71% trainable parameters for classification and 7.69%for dense prediction, and scales favorably to larger backbones, requiringmerely 0.36% parameters on PointGPT-L.

ECCV 2026

WARP: Wide Attention with Rich Projections for Image Super-Resolution

Jiwon Kim, Kyoung Mu Lee

State-of-the-art super-resolution (SR) transformers stack 16–30+ blocks of small shifted-window attention. Information between dis-tant positions must therefore traverse multiple attention layers, eachbounded by the local window—an indirect path that grows with net-work depth. We show that direct wide attention over large position-fixedwindows (64×64 at training, up to 128×128 at inference) can matchor surpass these deep narrow-window designs with only 12 blocks. Akey enabler is 2D Rotary Position Embeddings (RoPE), which encoderelative positions through query-key rotations rather than additive biasmatrices, enabling memory-efficient attention over thousands of tokens.To fully exploit this large spatial context, we replace the standard linearQKV projection with a rich nonlinear projection module that producesmore expressive features for each attention operation; sharing a singlemodule across all blocks keeps the model at 20M parameters while pro-viding the representational capacity that would otherwise require 115M.The shared module jointly produces spatial and channel QKV througha unified output. RoPE further enables resolution-adaptive inference viaYaRN-style scaling [22] with an inverted temperature correction tailoredfor SR. With only 20M parameters, our model, WARP, achieves PSNRcompetitive with or superior to state-of-the-art methods on standardbenchmarks.

ECCV 2026

PAI-Studio: Cinematic Video Background Replacement with Camera-Aware Motion

Heyuan Gao, Bangxun Tang, Yiren Song, Guian Fang, Zijian He, Jie Yang, Mike Zheng Shou

We present PAI-Studio, a new reference-conditioned videosynthesis task that addresses a long-standing challenge in cinematic back-ground replacement: generating dynamic backgrounds aligned with fore-ground motion while preserving foreground identity, matching referencescene appearance, and achieving globally consistent illumination with re-alistic foreground relighting. Existing open-source systems and commer-cial APIs cannot simultaneously ensure motion-consistent backgroundgeneration, high-fidelity foreground relighting and foreground identitypreservation, often resulting in static backgrounds, inconsistent bound-aries, and noticeable compositing artifacts. To bridge this gap, we buildupon a Diffusion Transformer video backbone and reformulate the prob-lem as an in-context conditional generation task. Through bidirectionalattention, our model jointly captures foreground dynamics and back-ground reference information within a unified architecture. We furtherconstruct a 30K-scale dataset sourced from high-quality films and on-line videos to support this task. Extensive evaluations demonstrate thatour method significantly outperforms existing open-source and commer-cial API solutions. Project page: https://github.com/showlab/PAI-Studio.

ECCV 2026

Online 3D Instance Segmentation at task-oriented granularity with Unposed Monocular Video

Dong Wu, Baicheng Li*, Yingdian Cao, Shunkai Zhou, Yiwen Lu, Hongbin Zha

We present a real-time, task-oriented 3D instance segmenta-tion framework for unposed monocular video, enabling embodied agentsto task-adaptively perceive and interact with objects in open-world scenes.Unlike most previous bottom-up segmentation paradigms that segmentbefore recognition, we adopt a task-oriented segmentation approach.Specifically, objects are decoupled within each frame using an open-vocabulary detector combined with a prompt-based 2D segmentationmodel, while the 3D underlying geometry of the scene is simultane-ously being reconstructed using a modern dense SLAM system, whosecontinuously re-optimized camera poses and depth are unsuitable forincremental mask association. Guided by the SLAM-derived pose graph,we selectively associate multi-view masks and reuse the dense correspon-dences provided by the SLAM system, incrementally converting theminto geometric association scores with minimal additional computation.By incorporating semantic similarity and mutual exclusivity metrics, wedesign a priority-ordered mask clustering algorithm for efficient onlinemulti-view mask matching and merging. Evaluations on open-vocabulary3D instance segmentation benchmarks show that our method effectivelymitigates the performance degradation of existing approaches when usingdense SLAM reconstructions instead of depth-sensor point clouds. Onthe Replica dataset, using only unposed images, it even achieves resultscomparable to methods leveraging ground-truth depth and poses. Moreimportantly, qualitative results show that our method achieves more reli-able task-oriented 3D object disentanglement than previous bottom-upsegmentation paradigms.

ECCV 2026

Odoriko: A Shape-Aware Multimodal Diffusion Framework for Human Motion

Dongseok Shim, Julian Tanke, Kengo Uchida, christian simon, Koichi Saito, Takashi Shibuya, Shusuke Takahashi, Yuki Mitsufuji

Human motion generation has been widely studied across di-verse input modalities — text, music, and video — and recent efforts haveunified these into single multimodal frameworks. However, while morpho-logical factors such as gender and body shape are known to produce dis-tinct kinematic signatures, no existing unified framework incorporatesthis into generation, treating all subjects as morphologically equiva-lent. We present Odoriko, the first unified multimodal motion generationframework that reflects subject bio-morphological information directly insynthesized motion output. Rather than averaging over subject variation,Odoriko generates motion that is consistent with who is moving, not justwhat they are asked to do — across text, music, and video conditionswithin a single model. When explicit morphological information is un-available, Odoriko additionally recovers subject morphology alongsidemotion, unifying estimation and generation in one framework. Extensiveexperiments across text-to-motion, music-to-dance, and video-to-motionbenchmarks demonstrate that Odoriko matches or exceeds prior special-ized models on standard metrics, while enabling morphology-consistentgeneration that no existing unified framework supports. Project page:https://dsshim0125.github.io/odoriko.github.io/

ECCV 2026

Mitigating Sycophancy in Multimodal Chart Understanding via Vision-Grounded Verification

Xiaolong Wang, Zhiwei Lin, Tai Liu, Qiang Li, Jian Sun

Multimodal large language models (MLLMs) have becomethe dominant paradigm for chart understanding, yet they suffer from acritical vulnerability: sycophancy. When users embed false premises inqueries about charts, models override visual evidence to comply with theuser’s misconception. Existing mitigations incur a severe safety-utilitytrade-off : suppressing sycophantic compliance inevitably triggers over-refusal of valid queries, degrading general performance. To address thischallenge, we introduce Re-Check, a training-free inference frameworkthat follows a “Verify-then-Answer” workflow: it first decomposes userqueries into atomic claims, then verifies each claim against the chartimage, and finally routes the generation through an adaptive three-way mechanism. To ensure verification reliability, we propose the Con-trastive Visual Dependency Score (CVDS), an information-theoreticmetric that measures the KL divergence between the model’s predic-tions with and without visual input, filtering out textual-bias-drivenmisjudgments. Through extensive experiments on diverse benchmarks,Re-Check improves overall accuracy by 15.01% (from 57.49% to 72.50%with Qwen3-VL-8B) on sycophancy scenarios, while maintaining compet-itive performance on general benchmarks such as ChartQAPro. Theseresults demonstrate a superior safety-utility trade-off. Code is availableat: https://github.com/X1Wang/ReCheck-code.

ECCV 2026

Mask-guided Semantic Alignment: Robust Learning with Noisy Labels via Temporal Attention Stability

Yubo Nian, Can Gao

Noisy labels pose a significant challenge in training deep neu-ral networks, as corrupted annotations inevitably induce overfitting andsignificantly degrade generalization. However, existing methods typicallyrely on static metrics and overlook training dynamics, struggling to effec-tively separate clean samples from noisy ones under complex and highnoise scenarios. In this paper, we propose a temporal attention-basedframework for learning with noisy labels. Specifically, we introduce an at-tention temporal consistency module for reliable sample selection, whichseparates samples by quantifying the evolutionary stability of visual at-tention during training. Then, to address the scarcity of clean samples inhigh-noise scenarios, we design an adaptive attention masks module tosynthesize images from clean and noisy samples, which employs differen-tial masking strategies to generate high-quality training samples. Finally,we present a mask-guided feature alignment module, which introducesfeature consistency regularization to facilitate the learning of robustrepresentations for noisy data. Extensive experiments demonstrate thatour method outperforms state-of-the-art approaches, achieving accuracygains of 1.6%/2.8% on CIFAR-10/100 under 90% symmetric noise, re-spectively. Code is available at https://github.com/iCAN-SZU/MSA

ECCV 2026

LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching

Jinshan Liu, Haoran Qin, Xiaobing Tu, Jiacheng Liu, Jiahui Hu, Zhengan Yan, Yukun Xie, Kerui Shen, Jinkui Ren, Yuqi Lin, Xiantao Zhang, Linfeng Zhang

Di_x001B_usion models have achieved remarkable success in im-age and video generation, yet the high computational cost of iterativesampling remains a critical bottleneck for practical deployment. Featurecaching has emerged as a promising acceleration paradigm by reusingor predicting intermediate features across timesteps. However, existingtraining-free methods apply uniform prediction strategies that cannotadapt to the heterogeneous feature dynamics, causing signi_x001C_cant qualitydegradation under high acceleration ratios. We propose LinCa , a fea-ture caching framework based on learnable invertible networks. LinCadecomposes cached features into sub-components with distinct conti-nuity properties via a lightweight invertible network and applies dif-ferentiated prediction orders matched to each component. The strictinvertibility guarantees lossless reconstruction back to the original fea-ture space, forming a uni_x001C_ed Decompose-Predict-Reconstruct pipeline.By training separate predictors for di_x001B_erent models and timestep seg-ments, LinCa adapts to heterogeneous feature dynamics. Experiments onFLUX, Qwen-Image, and HunyuanVideo demonstrate that LinCa, withless than 0.2% additional parameters, signi_x001C_cantly outperforms exist-ing methods and maintains near-lossless quality at 5-7× speedup. Code:https://github.com/QHR69/LinCa.

ECCV 2026

Learning Accurate Segmentation Purely from Self-Supervision

Zuyao You, Zuxuan Wu, Yu-Gang Jiang

Accurately segmenting objects without any manual anno-tations remains one of the core challenges in computer vision. In thiswork, we introduce Selfment, a fully self-supervised framework that seg-ments foreground objects directly from raw images without human labels,pretrained segmentation models, or any post-processing. Selfment firstconstructs patch-level affinity graphs from self-supervised features and ap-plies NCut to obtain an initial coarse foreground-background separation.We then introduce Iterative Patch Optimization (IPO), a feature-space refinement procedure that progressively enforces spatial coherenceand semantic consistency through iterative patch clustering. The refinedmasks are subsequently used as supervisory signals to train a lightweightsegmentation head with contrastive and region-consistency objectives,allowing the model to learn stable and transferable object representations.Despite its simplicity and complete absence of manual supervision, Self-ment sets new state-of-the-art (SoTA) results across multiple benchmarks.It achieves substantial improvements on Fmax over previous unsupervisedsaliency detection methods on ECSSD (+4.0\%), HKUIS (+4.6\%), andPASCAL-S (+5.7\%). Moreover, without any additional fine-tuning, Self-ment demonstrates remarkable zero-shot generalization to camouflagedobject detection tasks (e.g., .910 S_m on CHAMELEON and .792 \protect \mathcal {F}_{_x0008_eta }^{\omega }on CAMO), outperforming all existing unsupervised approaches andeven rivaling the SoTA fully supervised methods. Codes and weights areavailable at: https://geshang777.github.io/Selfment/.

ECCV 2026

InFlux++: Real and Synthetic Data for Estimating Dynamic Camera Intrinsics

Erich Liang, Caleb Kha-Uong, Chinmaya Saran, Sreemanti Dey, David Liu, Junhan Ouyang, Benjamin Zhou, Jia Deng

Camera intrinsics play a vital role in recovering 3D structurefrom 2D video. However, most 3D algorithms assume that intrinsics re-main fixed throughout input video, an assumption that frequently failsfor real-world in-the-wild videos. Consequently, estimating per-frame in-trinsics from RGB images is critical for enabling 3D methods to operaterobustly on dynamic intrinsics videos. Previously, InFlux has contributedto this research direction by establishing the first real-world benchmarkwith per-frame ground truth intrinsics for dynamic intrinsics videos, en-abling systematic evaluation of algorithms for this task. Nevertheless,existing methods remain inaccurate due to two key obstacles: (i) train-ing data for this task is scarce and lacks diversity in camera intrinsics;and (ii) existing benchmarks, including InFlux, are limited in scene andcamera motion diversity, making it difficult to properly evaluate methodperformance. To address both gaps, we present InFlux++, consisting oftwo components. InFlux++ Synth is a large-scale procedurally generatedsynthetic video dataset with 441K+ annotated frames from 1841 high-resolution videos, providing accurate per-frame ground truth intrinsicsfor training dynamic intrinsics prediction models; a subset additionallyincludes per-frame camera pose, depth, and surface normals. The videosfeature rich intrinsics diversity through changes in camera zoom andfocus over time, as well as dynamic objects and realistic rendering ef-fects such as lens distortion and defocus blur. InFlux++ Real is a large-scale real-world benchmark that extends InFlux with 514K+ newly cap-tured frames across 334 high-resolution videos, spanning a wider rangeof scenes and camera motions. Finetuning existing intrinsics predictionmethods on InFlux++ Synth consistently improves focal length estima-tion across both InFlux++ Real and InFlux, suggesting that syntheticsupervision is a promising direction for RGB-based intrinsics prediction.For the dataset, benchmark, code, videos, submission instructions, andlive leaderboard, please visit https://influx.cs.princeton.edu/.

ECCV 2026

In-context Region-based Drag: Drag Any Region to Any Shape

Jiacheng Sui, Tianyu Hao, Bingjie Gao, Li Niu, Guangtao Zhai

Diffusion models have shown promise in drag-style editing.Previous works mainly focus on point-based drag, which is inherentlyambiguous. This paper focuses on region-based drag and introduces anovel In-Context Region-based Drag (ICRDrag) method. Under the in-context learning framework, ICRDrag consumes a source image, a sourceregion mask, and a target region mask, producing the target draggedimage. Built upon the basic in-context learning model, we introduce twonovel attention regularization: 1) image-mask attention consistency toensure that a target region attends to similar source regions for image andmask modalities; 2) source-target attention correspondence to ensure themutual correspondence between source and target regions. To facilitateregion-based drag, we also construct Paired Region Dataset (PRD), alarge-scale dataset with paired masks and images. Extensive experimentsshow that ICRDrag significantly outperforms existing methods in bothquantitative metrics and user studies, achieving superior editing accuracyand visual fidelity. The dataset, code, and model are available at https://github.com/bcmi/ICRDrag-Region-Drag-Editing.

ECCV 2026

IC-World: In-Context Generation for Shared World Modeling

Fan Wu, Jiacheng Wei, Ruibo Li, Yi Xu, Junyou Li, Deheng Ye, Guosheng Lin

Video-based world models have recently garnered increas-ing attention for their ability to synthesize diverse and dynamic vi-sual environments. In this paper, we focus on shared world modeling,where a model generates multiple videos from a set of input images,each representing the same underlying world in di!erent camera poses.We propose IC-World, a novel generation framework, enabling paral-lel generation for all shared world input images via activating the in-herent in-context generation capability of large video models. We fur-ther finetune IC-World via reinforcement learning, Group Relative Pol-icy Optimization, together with two proposed novel reward models toenforce scene-level geometry consistency and object-level motion consis-tency among the set of generated videos. Extensive experiments demon-strate that IC-World substantially outperforms state-of-the-art methodsin both geometry and motion consistency. To the best of our knowledge,this is the first work to systematically explore the shared world modelingproblem with video-based world models.

ECCV 2026

HomeGuard: VLM-based Embodied Safeguard for Identifying Contextual Risk in Household Task

Xiaoya Lu, Yijin Zhou, Zeren Chen, Ruocheng Wang, Bingrui Sima, Enshen Zhou, Lu Sheng, Dongrui Liu, Jing Shao

Vision-Language Models (VLMs) empower embodied agentsto execute complex instructions, yet they remain vulnerable to contex-tual safety risks where benign commands become hazardous due to subtleenvironmental states. Existing safeguards often prove inadequate. Rule-based methods lack scalability in object-dense scenes, whereas model-based approaches relying on prompt engineering suffer from unfocusedperception, resulting in missed risks or hallucinations. To address this,we propose an architecture-agnostic safeguard featuring Context-GuidedChain-of-Thought (CG-CoT). This mechanism decomposes risk assess-ment into active perception that sequentially anchors attention to inter-action targets and relevant spatial neighborhoods, followed by semanticjudgment based on this visual evidence. We support this approach witha curated grounding dataset and a two-stage training strategy utilizingReinforcement Fine-Tuning (RFT) with process rewards to enforce pre-cise intermediate grounding. Experiments demonstrate that our modelsignificantly enhances safety, improving risk match rates by over 30%compared to base models while reducing oversafety. Beyond hazard de-tection, the generated visual anchors serve as actionable spatial con-straints for downstream planners, facilitating explicit collision avoidanceand safety trajectory generation.

ECCV 2026

HOIMask: Towards Generative Masked Modeling for Human Object Interaction Generation

Yihong Ji, Jinsong Zhang, He Hu, Hongboxu Hongboxu

Diffusion-based methods have dominated the HOI genera-tion, as they enable critical contact fusions or signals to guide the dif-fusion process. However, they often result in high artifacts and unstableinteraction quality due to error accumulation during iterative denois-ing. In this work, we propose HOIMask, the first generative maskedframework for modeling HOI motion in discrete space. HOIMask firstencodes both motion sequences and contact-aware signals into discrete2D human and object token maps via HOI Vector Quantization (VQ),preserving fine-grained spatial-temporal structure beyond conventional1D representations. On this basis, a generative masked modeling frame-work is employed to jointly capture human-object interaction dynamics,leveraging a transformer architecture designed to model complex spatial-temporal and interaction dependencies. To generate more coherent andphysically plausible motions, we further introduce a novel contact-awarereconstruction guidance in discrete space during inference, which fusescontact signals to optimize HOI tokens that forces the generated mo-tion with higher spatio-temporal consistency. With craftily designed mo-tion interaction tokens, dedicated architecture and guidance strategy,HOIMask outperforms state-of-the-art diffusion-based methods, gener-ating more realistic and semantically aligned HOI motions. Please referto our project page for more results.

ECCV 2026

H2SVC: Head-aware Heterogeneous Streaming Video Cache for Online Video Understanding

Han Li, Xinyi Zhang, Wenrui Dai, Yaoming Wang, Xinyu Peng, FAN He, Hang Xu, Ziyang Zheng, Chenglin Li, Junni Zou, Hongkai Xiong

Multimodal Large Language Models (MLLMs) suffer fromsevere memory bottlenecks in streaming video understanding due to ac-cumulated Key-Value (KV) cache with the growth of video length. Exist-ing training-free compression methods employ the same strategy acrossall attention heads in one layer, but ignore their diverse functional roles intemporal representation. In this paper, we reveal the inherent temporalheterogeneity of attention heads in MLLMs through systematic Spatio-temporal Attention Profiling (SAP), and discover that heads naturallyspecialize into instantaneous, short-term, and episodic roles agnostic toinput video frames and their queries. Motivated by this finding, we pro-pose the Head-aware Heterogeneous Streaming Video Cache (H2 SVC),a training-free KV cache compression framework for efficient streamingvideo understanding. H2 SVC features an offline routing strategy that ex-ploits the distinct temporal behavior of heads in each layer and adapttheir KV cache to dedicated memory banks tailored for varying temporalreceptive fields. Furthermore, we develop Semantic Trajectory CurvatureEviction (STCE) for the long-term episodic bank to enhance semanticdiversity under strict streaming budget. STCE models the Value statesas a geometric trajectory to continuously evict predictable frames onsmooth paths, while preserving sharp semantic inflection points. Exten-sive experiments on widely adopted streaming benchmarks demonstratethat H2 SVC achieves state-of-the-art performance, and is comparableor superior to full-context offline models with significantly reduced KVcache memory and accelerated inference speed.

ECCV 2026

Graph-GSReg: Leveraging 3D Scene Graphs for Gaussian Splatting Registration

Jaewon Lee, Mangyu Kong, Euntai Kim

Merging multiple 3D Gaussian Splatting (3DGS) scenes intoa single unified Gaussian representation is essential for large-scale 3Dmapping and long-term map management. Despite its importance, thisarea remains underexplored, and existing solutions exhibit several limi-tations. Learning-based methods attempt direct correspondence betweenGaussian primitives and require training on large 3DGS datasets. Image-based optimization methods depend heavily on coarse initialization fromgeneric foundation models and often incur expensive refinement. Wepresent Graph-GSReg. Our method constructs a 3D scene graph froma 3DGS and its rendered images, reformulating 3DGS registration asa graph registration problem. The proposed 3D scene graph representseach 3DGS at a higher-level representation, enabling a globally consis-tent understanding of semantic information and structural context foraccurate registration. To further construct a seamless unified scene, weintroduce a Self-Supervised Test-Time Optimization. Naively mergingtwo 3D Gaussian scenes often suffers from occlusion artifacts such ashollows and floaters. To alleviate this issue, we refine the merged Gaus-sians to preserve visual consistency between the original scenes and themerged scene. We evaluate our method on real and synthetic bench-marks, demonstrating competitive registration accuracy and merged scenerendering quality.

ECCV 2026

GCMRD: Global Consistency Multi-teacher Robustness Distillation

Yuhang Zhou, Zhongyun Hua, Rushi Lan, Qing Liao, Wei Jiang

Adversarial Robustness Distillation (ARD) aims to trans-fer both natural and robust knowledge from a strong teacher modelto a lightweight student model, thereby achieving robust performancein resource-constrained settings. The dual-teacher robustness distilla-tion paradigm further provides an inspiring solution for optimizing the‘accuracy-robustness’ trade-off. However, existing dual-teacher distilla-tion frameworks fail to fully align the supervision provided by teachergroup, resulting in suboptimal performance. To fully utilize the poten-tial of dual-teacher robustness distillation, we propose a novel multi-teacher framework called global consistency multi-teacher robustness dis-tillation (GCMRD), which improves the existing dual-teacher paradigmfrom three perspectives. First, for internal maximization, we introducean integrated soft-label attack objective to replace hard-label or single-teacher-guided objective, leading to consistent attack preferences acrossboth natural and robust teachers. Second, for external minimization, wedesign a fully-repulsion regularization mechanism based on the erroneousadversarial outputs of the natural teacher, which enables more compre-hensive utilization of teacher supervision. Finally, we employ the outputsof the natural teacher to guide the fine-tuning of the robust teacher, in-directly balancing the student model’s accuracy-robustness trade-off byenforcing consistency with the teacher ensemble. Experimental resultsdemonstrate that our GCMRD achieves state-of-the-art performance inboth natural accuracy and adversarial robustness.

ECCV 2026

FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion

Yuang Meng, Chenyang Wu, Xianshun Liu, Chun-Le Guo, Zichen Liang, Lina Lei, Jie Liang, Hui Zeng, Chongyi Li, Lei Zhang

Existing optical flow estimation methods broadly follow twoparadigms: iterative optimization and diffusion-based estimation. Iter-ative methods, exemplified by RAFT, achieve accurate flow estimationthrough recurrent refinement, but can still be challenged by large displace-ments and complex motion patterns. Diffusion-based methods introducegenerative modeling into optical flow and have shown promising resultsin such ambiguous regions. However, existing diffusion-based flow modelsusually denoise the entire dense flow field from Gaussian noise, includingsimple regions where reliable motion structure can already be estimatedby a lightweight network. This increases the denoising burden and maylead to slow convergence and unstable training. To address these issues,we introduce FlowPainter, a diffusion-based optical flow framework thatreformulates dense-flow generation as confidence-guided soft inpainting.FlowPainter first employs a lightweight confidence-aware network to pre-dict a rough flow and a pixel-wise confidence mask, which serves as areliability gate for distinguishing reliable simple regions from uncertainhard regions. The resulting simple-flow prior is used for confidence-basedinitialization and is further injected into the iterative denoising processthrough confidence-gated residual guidance. With a dynamically decayingguidance strength, FlowPainter stabilizes early denoising while preservingthe flexibility of the diffusion model for late-stage detail refinement. Ex-tensive experiments on public benchmarks, including Sintel, KITTI, andSpring, demonstrate that FlowPainter achieves strong accuracy undercomparable training settings and improves convergence efficiency overexisting diffusion-based optical flow methods, with notable gains on chal-lenging benchmark splits. Our approach provides a practical direction forintegrating reliable discriminative priors with diffusion-based refinementfor optical flow estimation.

ECCV 2026

FedDO: Dynamic Client Optimization for Adaptive Federated Learning

Peixuan Tang, Xuehe Wang

Federated Learning (FL) allows multiple devices to collaboratively train machine learning models while keeping their raw data private. However, in real-world scenarios, FL often struggles with unstable optimization and slow convergence. This is largely caused by heterogeneous client data, where both the data distributions and dataset sizes vary significantly across devices. To address these challenges, we propose FedDO, an adaptive federated optimization framework guided by reinforcement learning. Instead of using predefined rules or binary selection, FedDO formulates client coordination as a continuous control problem. Specifically, we employ a sample-efficient Distributional Soft Actor-Critic with Three Refinements (DSAC-T) agent to dynamically allocate a continuous data-usage ratio for each client per round. This fine-grained adjustment effectively mitigates gradient drift. Furthermore, we introduce a low-rank parameterization technique to compress the agent’s action space, ensuring scalability to thousands of clients. Extensive experiments on standard image classification benchmarks show that FedDO consistently achieves higher accuracy and much faster convergence than state-of-the-art FL baselines, especially under highly unbalanced and heterogeneous data settings. Code is available at https: //github.com/leafuan/FedDO_code.

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