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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

NanoVSR: Towards Real-Time Video Super-Resolution on Edge Devices

Filip Pawlicki, Marcel Kańduła, Marcin Pucek, Kamil Dobies

Recent Video Super-Resolution (VSR) methods rely heavilyon transformers and explicit optical flow, creating computational over-head and custom operations that hinder deployment on hardware acceler-ators like TensorRT. To address this, we introduce NanoVSR, a scalable,fully convolutional architecture designed for resource-constrained edgedevices. Using structural reparameterization, NanoVSR collapses intostandard convolutions during inference, ensuring seamless hardware com-patibility and negligible runtime overhead. Furthermore, despite lackingexplicit motion compensation, it maintains competitive restoration qual-ity by implicitly learning spatio-temporal alignments through progres-sive training. Evaluated on the REDS4 benchmark, NanoVSR demon-strates an exceptional balance between accuracy and computational ef-ficiency, significantly improving the trade-off for compact architectures.Our NanoVSR-644k baseline yields 28.64 dB PSNR on REDS4 whiledelivering 27.20 FPS on the NVIDIA Jetson Orin NX 16GB (25W),offering massive speed gains over heavier models. The scaled NanoVSR-1.7M variant reaches 29.15 dB with a throughput of 19.58 FPS, providingsuperior, edge-optimized upscaling.

ECCV 2026

MOJITO: Modal Joint Learning for Unified End-to-End Autonomous Driving

Zhijing Cheng, Xuancheng Zhang, Donglin Di, Lei Fan, Baorui Ma, Hao Li, Xun Yang

End-to-end autonomous driving systems commonly follow a cascaded two-stage pipeline where a perception stage compresses multimodal sensor inputs into a compact context and a downstream planner predicts trajectories conditioned on this context. We argue that this oneway perception-to-planning interface forces sensor inputs into a compact representation, losing the fine-grained details critical for planning. Moreover, by constraining the planner to this compressed context, it is difficult to leverage the rich representations offered by modern vision foundation models. To address these issues, we propose MOJITO, a unified sensorto-action framework for end-to-end autonomous driving built on modal joint learning. MOJITO removes the cascaded interface and instead performs block-wise Modal Joint Attention that simultaneously updates action, image, and LiDAR features, allowing the planner to directly access multi-modal features during action generation. MOJITO achieves 88.9 PDMS on the NAVSIM v1 dataset and 88.4 EPDMS on the more challenging NAVSIM v2 dataset, setting a new state-of-the-art. Extensive experiments further demonstrate strong scalability, instruction following, and diverse trajectory generation. Code and models are available at https://github.com/mumucc01/MOJITO.

ECCV 2026

MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training

Haotian Xue, Qi Chen, Zhonghao Wang, Xun Huang, Eli Shechtman, Jinrong Xie, Yongxin Chen

Video diffusion models achieve strong frame-level fidelity butstill struggle with motion coherence, dynamics and realism, often pro-ducing jitter, ghosting, or implausible dynamics. A key limitation is thatthe standard denoising MSE objective provides no direct supervision ontemporal consistency, allowing models to achieve low loss while still gen-erating poor motion. We propose MoGAN, a motion-centric post-trainingframework that improves motion realism without reward models or hu-man preference data. Built atop a 3-step distilled video diffusion model,we train a DiT-based optical-flow discriminator to differentiate real fromgenerated motion, combined with a distribution-matching regularizer topreserve visual fidelity. With experiments on Wan2.1-T2V-1.3B, MoGANsubstantially improves motion quality across benchmarks. On VBench,MoGAN boosts motion score by +7.3% over the 50-step teacher and+13.3% over the 3-step DMD model. On VideoJAM-Bench, MoGAN im-proves motion score by +7.4% over the teacher and +8.8% over DMD,while maintaining comparable or even better aesthetic and image-qualityscores. A human study further confirms that MoGAN is preferred formotion quality (52% vs. 38% for the teacher; 56% vs. 29% for DMD).Overall, MoGAN delivers significantly more realistic motion without sac-rificing visual fidelity or efficiency, offering a practical path toward fast,high-quality video generation.

ECCV 2026

MMR-Bench: A Comprehensive Benchmark for Multimodal LLM Routing

Hao-Xuan Ma, Guannan Lai, Han-Jia Ye

Multimodal large language models (MLLMs) have advancedrapidly, yet heterogeneity in architecture, alignment strategies, and effi-ciency means that no single model is uniformly superior across tasks. Inpractical deployments, workloads span lightweight OCR to complex mul-timodal reasoning; using one MLLM for all queries either over-provisionscompute on easy instances or sacrifices accuracy on hard ones. Query-level model selection (routing) addresses this tension, but extending rout-ing from text-only LLMs to MLLMs is nontrivial due to modality fusion,wide variation in computational cost across models, and the absence ofa standardized, budget-aware evaluation.We present MMR-Bench, a unified benchmark that isolates the mul-timodal routing problem and enables comparison under fixed candidatesets and cost models. MMR-Bench provides (i) a controlled environmentwith modality-aware inputs and variable compute budgets, (ii) a broadsuite of vision–language tasks covering OCR, general VQA, and mul-timodal reasoning, and (iii) strong single-model baselines, oracle upperbounds, and representative routing policies. Using MMR-Bench, we showthat incorporating multimodal signals improves routing quality. Empiri-cally, these cues improve the cost–accuracy frontier and enable the routedsystem to exceed the strongest single model’s accuracy at roughly 33\%of its cost. Furthermore, policies trained on a subset of models and tasksgeneralize zero-shot to new datasets and text-only benchmarks withoutretuning, establishing MMR-Bench as a foundation for studying adaptivemultimodal model selection and efficient MLLM deployment.

ECCV 2026

MARché: Fast Masked Autoregressive Image Generation with Cache-Aware Attention

Chaoyi Jiang, Sungwoo Kim, Lei Gao, Hossein Zarch, Won Woo Ro, Murali Annavaram

Masked autoregressive (MAR) models unify the strengths ofmasked and autoregressive generation by predicting tokens in a fixed or-der using bidirectional attention for image generation. Although effective,MAR models incur substantial computational overhead because they re-compute attention and feed-forward representations for every token ateach decoding step, even though the majority of tokens remain seman-tically unchanged across steps. We propose a training-free generationframework MARché to address this inefficiency through two key com-ponents: cache-aware attention and selective KV refresh. Cache-awareattention partitions tokens into active and cached sets, enabling sepa-rate computation paths that allow efficient reuse of previously computedkey/value projections without compromising full-context modeling. How-ever, a cached token cannot be used indefinitely without recomputationdue to the changing contextual information over multiple steps. MARchérecognizes this challenge and applies a technique called selective KV re-fresh. Selective KV refresh identifies contextually relevant tokens basedon attention scores from newly generated tokens and updates only thosetokens that require recomputation, while preserving image generationquality. MARché significantly reduces redundant computation in MARwithout modifying the underlying architecture. Empirically, MARchéachieves up to 1.7× speedup with negligible impact on image quality,offering a scalable and broadly applicable solution for efficient maskedtransformer generation.

ECCV 2026

LVSPM: Long Sequence View Synthesis and Pose Estimation Model

Xi Chen, Yachi Zhang, Linghao Chen, Minghua Liu, Hao Su, Zexiang Xu, Xiaoshuai Zhang

We present LVSPM, a generalizable model that jointly esti-mates camera poses and synthesizes novel views from uncalibrated im-age collections. Trained with only RGB images and pose supervision,LVSPM avoids dense 3D ground truth and employs test-time train-ing (TTT) layers to scale seamlessly to hundreds of input views. OnRealEstate10k, Co3Dv2, and DL3DV, LVSPM surpasses VGGT in poseestimation across 16–256 views, with especially large margins at strictthresholds. For novel view synthesis under a practical protocol wheremore views cover larger scenes, LVSPM achieves state-of-the-art pose-free quality—surpassing even pose-dependent models in PSNR—and stillmaintains high quality as scene scale grows, while baselines collapse. Thecode will be available at https://burningdust21.github.io/Projects/LVSPM/.

ECCV 2026

Learn Once, Edit Anywhere: Visual Direction Transfer for Diffusion Models

Yusuf Dalva, Hidir Yesiltepe, Pinar Yanardag

The rapid advancement of diffusion models has enabled thegeneration of high-fidelity images from textual prompts, yet achievingprecise, disentangled control over specific attributes remains a significantchallenge. A fundamental limitation arises because visual differences be-tween images are often far more descriptive and nuanced than what canbe captured through human-crafted text descriptions, which frequentlyfail to convey fine-grained semantic details. To address this, we intro-duce ViDiT (Visual Direction Transfer for Diffusion), a frameworkthat expands the editing vocabulary by capturing latent semantics di-rectly from image-edit pairs. ViDiT learns the underlying transformationby optimizing a single, global, and continuous editing direction from asmall set of “before-and-after” examples. This optimization process trans-fers visual changes into the diffusion model’s conditioning space, allowingfor detailed edits that text alone cannot easily describe. ViDiT operateson a “Learn Once” principle, which completely eliminates the need formodel fine-tuning or expensive per-image optimization during inference.Once learned, these continuous directions enable “Edit Anywhere” capa-bilities, allowing users to apply highly disentangled manipulations, suchas changes in facial features, animal attributes, or artistic styles, to anyimage in a zero-shot manner with granular control over the edit inten-sity. Quantitative and qualitative evaluations demonstrate that ViDiToutperforms existing text-based editing methods in maintaining inputfaithfulness while achieving precise, scalable attribute control.

ECCV 2026

LangLoc: “Tell Me What You See”

Shaurya Kishore Panwar, Roham Zendehdel Nobari, Shirley Lau, Abu Bakr Rahman Shaik, Manuel Günther, Marc Pollefeys, Daniel Barath

We tackle fine-grained indoor localization from natural lan-guage: given a free-form description of one’s surroundings, estimate theobserver’s 2D position and heading within a known 3D environment.Language queries are lightweight, privacy-preserving, and need no cam-era – yet prior work stops at coarse scene retrieval and cannot resolve anintra-scene pose. We close this gap with LangLoc, a three-stage pipelinethat (i) retrieves the correct scene via a dual-branch GATv2 encoderwith CLIP semantic features, surpassing the previous best by 8 per-centage points in Top-1 recall; (ii) estimates position and heading byscoring a dense floor grid through ray-cast object visibility, reaching amedian error of 0.95 m; and (iii) resolves residual ambiguity through aBayesian dialog module that asks targeted yes/no questions and updatesa pose posterior until the location is pinpointed. To support this task wecontribute a benchmark of 13,000+ pose-indexed natural-language de-scriptions over 1,300+ indoor 3D scans. Code and data will be released.Project page: https://rzninvo.github.io/Lang-Loc/.

ECCV 2026

KATANA: Knowledge-Aligned Topology-Aware Neural Agents for RL-Driven Vision-Language Model Compression

Nafew Azim, Mir Ali, Fuad Rahman, Nabeel Mohammed

Vision-language models (VLMs) achieve remarkable multi-modal comprehension, yet their massive parameter counts impede de-ployment on resource-limited hardware. Traditional pruning techniquesrely on manual heuristics that frequently disrupt delicate cross-modalsynergies, leading to substantial performance declines. To overcome this,we introduce KATANA (Knowledge-Aligned Topology-Aware NeuralAgents), a reinforcement learning framework that autonomously discoversexecutable pruning algorithms. Guided by a synthesized multi-objectivereward signal and safely evaluated within an isolated sandbox, an LLM-driven agent iteratively evolves innovative compression strategies. Ourflagship discovered algorithm, KIRI (Kernel-Integrated ReconstructionIterator), utilizes a cubic sparsity scheduler alongside a novel Dual-NormActivation (DNA) importance metric that dynamically fuses weight mag-nitudes, structural regularizations, and data-dependent activation profiles.Extensive evaluations across four diverse VLM architectures (LLaVA-1.5,BLIP-2, Qwen2.5, and Llama-3.2-Vision) on MSCOCO, Flickr30k, andNoCaps benchmarks demonstrate KATANA’s superiority. At an aggres-sive 70% sparsity, KIRI delivers a measured 2.8× inference speedup whileconsistently outperforming 16 state-of-the-art baselines, including recentadaptive and hybrid methods such as SCOPE and GSOP. KATANA thusintroduces an automated and extensible framework for VLM compressionthat preserves vision-language coherence for practical deployment.

ECCV 2026

JacobianAvatar: Temporally Consistent Semi-rigid Avatar Reconstruction from a Monocular Video

Changyeon Won, Min-Gyu Park, Seonghwan Park, Ju Yoon, Hae-Gon Jeon

Generating realistic human avatars in complex motions—suchas clothing dynamics—requires modeling of global and local deformationswhich remains challenging in monocular settings. We address this prob-lem by leveraging neural Jacobian fields (NJFs) for representing semi-rigid deformations. We train self-supervised neural networks for predict-ing Jacobian matrices that give the pose-dependent deformations, bysolving a Poisson equation. However, monocular input presents severaldifficulties such as self-occluded regions and invisible surfaces. To addressthese issues, we introduce three key components: a constrained Poissonsolver, signed distance-based Jacobian regularization, and a deformation-guided residual flow loss, which together suppress boundary artifacts, re-cover frequently occluded regions such as armpits and thighs, and enforcetemporal consistency during motion. Experiments on benchmark and in-the-wild videos demonstrate that our method generates temporally sta-ble and geometrically coherent avatars, outperforming state-of-the-artapproaches.

ECCV 2026

IACD: Iterative Adversarial Collaborative Detection via Dual-Perspective Blind Spot Discovery

Xiaoyan Li, Cuicui Jiang, Jiaoping Chen, Rumei Yang

Modern object detectors systematically fail on concealed, camouflaged, or strategically hidden objects, not due to insufficient capacity, but because they reason solely from visual appearance with no mechanism to consider where objects might be hidden. We propose IACD (Iterative Adversarial Collaborative Detection), a dual-perspective framework that pairs a frozen pretrained YOLOv11m (Seeker) with a lightweight trainable module (Hider) that reasons inversely: given the environment, where would a target be hidden? The Hider analyzes environmental concealment suitability across texture, edge, and semantic dimensions, then identifies the Seeker’s blind spots by contrasting suitability against detection coverage. The resulting blind spot maps residually amplify feature responses via attention gates before a second detection pass. The two branches interact across multiple rounds, with the Hider’s blind spot estimates informing the attention gates that guide the second detection pass. Crucially, the Hider is trained under weak supervision derived solely from the detector’s own false negatives, requiring no manual concealment annotations. We frame this as detector blind-region recovery—recovering a frozen detector’s false negatives—rather than as standard camouflaged object detection. Experiments on the Poppy illegal crop detection dataset and the VisDrone aerial surveillance benchmark demonstrate consistent improvements over the strong YOLOv11m baseline, while introducing only ∼9M trainable parameters atop the frozen detector.

ECCV 2026

HIVE: Understanding Post Hallucination Reasoning in Vision Language Models

Feng He, Zhenting Wang, Qifan Wang, Qiang Guan, Dongfang Liu, Ruixiang Tang, Qiankun Li

Hallucinations in vision–language models (VLMs) are com-monly treated as semantic errors, yet they often arise from partial or am-biguous visual evidence. Prior work mainly focuses on detecting or sup-pressing hallucinations at generation time, leaving the subsequent reason-ing stage largely unexplored. In this work, we study Post-HallucinationReasoning (PHR), the stage in which hallucinated semantics enter themodel’s inference context and influence downstream predictions. To sys-tematically investigate PHR, we introduce the HIVE (HallucinationInference and Verification Engine), an evaluation infrastructure that en-ables controlled comparisons between faithful and hallucinated captions.Across nine tasks and nine models, we observe structured modality-dependent patterns: hallucinated captions often improve accuracy onvision–language tasks, while text-only tasks exhibit limited or unstableeffects. Further analyses show that hallucinated cues broaden semanticcoverage and reshape reasoning dynamics while preserving stable infer-ence. These findings highlight that hallucinated semantics may influencedownstream reasoning once they enter the model’s inference context. Un-derstanding this post-hallucination stage is important for improving thereliability and interpretability of multimodal reasoning systems.

ECCV 2026

Geometry-Aware Style Transfer in 3D Gaussian Splatting

Min Hyeok Bang, Jun Hyeong Kim, Seung Wook Kim, Se-Ho Lee

In this paper, we present a novel geometry-aware style trans-fer framework for 3D Gaussian splatting (3DGS) that simultaneouslytransfers appearance attributes and geometric structures. Unlike priorworks that primarily focus on color-based stylization and often over-look structural adaptation, our method explicitly incorporates geometryadaptation through a decoupled optimization scheme that alternatelyupdates color and geometry parameters. This strategy alleviates poten-tial interference between color and geometry updates, leading to stableand consistent scene-level geometry transformation. The decoupled opti-mization is enabled by the proposed geometry-aware contrastive featurematching (GCFM). GCFM integrates RGB, depth, and edge cues intoa contrastive objective and is employed in both optimization phases toeffectively transfer structural characteristics from style images to Gaus-sian primitives. Extensive experiments show that our approach achievessuperior performance in both qualitative fidelity and quantitative met-rics, significantly outperforming existing 3DGS-based stylization meth-ods. Our code is available at https://github.com/oweixx/gast.

ECCV 2026

Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth

Jung-Hee Kim, Xiaoming Liu

Monocular depth foundation models have demonstrated re-markable generalization capabilities across diverse environments. How-ever, they continue to struggle with metric depth estimation in diverseenvironments. This limitation stems from the inherent scale ambiguity ofsingle-view inference, leading to misaligned scale predictions even whenthe relative geometry is accurate. Conversely, recent multi-view founda-tion models leverage cross-view cues to learn robust scene-level geometryand consistent scale. Yet, these benefits typically vanish during single-image inference, as the absence of explicit geometric constraints causesperformance to degrade. To bridge this gap, we propose a novel frame-work that transfers the scale-aware geometric priors of multi-view modelsinto monocular depth foundation models. Specifically, we introduce anEpipolar Distillation (EpiDistill), an approach utilizing Rectified StereoTokens, which enables the single-view prediction model to retain epipolarattention patterns and maintain geometric consistency without requiringmulti-view inputs at inference. Experimental results demonstrate thatour method significantly improves zero-shot metric depth estimation,particularly on challenging datasets like ETH3D and DIODE where scalealignment is critical. Furthermore, our approach is model-agnostic, con-sistently boosting the performance of state-of-the-art ViT-based models,including UniDepthV2 and DepthPro. Project Link

ECCV 2026

GEM: Generative Supervision Helps Embodied Intelligence

Ruowen Zhao, Bangguo Li, Zuyan Liu, Yinan Liang, Junliang Ye, Fangfu Liu, Diankun Wu, Zhengyi Wang, Xumin Yu, Yongming Rao, Han Hu, Jun Zhu

Embodied Vision-Language Models (VLMs) have demon-strated impressive performance and generalization in robotics, partic-ularly within Vision-Language-Action frameworks. However, a signifi-cant gap remains between the high-level semantic focus of standardtext-guided pre-training paradigms and the low-level spatial and phys-ical knowledge critical for execution in embodied environments. In thispaper, we introduce GEM, a Generative-supervised Embodied vision-language Model designed to bridge this divide. We propose integratinga depth map generation task directly into the VLM pre-training phase.By training this generative objective jointly with the main model, weobserve substantial improvements in embodied intelligence, significantlyenhancing both semantic understanding and physical operation capa-bilities. To support this paradigm, we curate and release GEM-4M, acomprehensive large-scale dataset featuring a mixture of grounding, rea-soning, and planning data paired with high-quality depth supervision.Extensive experiments demonstrate that GEM achieves state-of-the-artresults across diverse embodied benchmarks. Furthermore, our deployedaction model, GEM-VLA, exhibits vastly superior task execution abilitiesin both simulation environments and real-world evaluations. Code, mod-els, and datasets are available at https://zhaorw02.github.io/GEM/.

ECCV 2026

From Visual Primitives to Semantic Masks: Fine-Grained Visual-Linguistic Alignment for Open-Vocabulary Remote Sensing Image Segmentation

Yuchen Deng, Yang Xu, Zhihui Wei, Zebin Wu

Open-vocabulary semantic segmentation (OVSS) makes seg-mentation driven by text descriptions, enabling generalization to novelclasses. However, fine-grained OVSS in remote sensing is challenged byvisual similarity and semantic ambiguity, with existing methods lackingexplicit fine-grained visual-linguistic alignment and relying on a singleforward pass. To address these issues, we propose SANO3, a training-free framework that leverages refined textual and spatial guidance toboost the performance of SAM 3. Leveraging the visual self-similaritypriors of vision foundation models, we extract discriminative visual prim-itives and align them with text through visual-conditioned relation-awarealignment strategy. Next, we enhance text representations with hybridprototype injection for deep visual-linguistic fusion. To refine bound-aries, we treat spatial prompts generation as an iterative optimizationprocess, gradually selecting the most informative points via a greedy en-tropy reduction strategy. Further, we introduce FG-OVSSRS Bench, thefirst fine-grained remote sensing OVSS benchmark, encompassing 8 di-verse datasets across different sensors and domains. Evaluated on thesedatasets, SANO3 outperforms existing approaches and achieves state-of-the-art performance.

ECCV 2026

Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent

Binger Chen, Tacettin Bök, Behnood Rasti, Volker Markl, Begum Demir

Foundation Models (FMs) are increasingly integrated into remote sensing (RS)pipelines for applications such as environmental monitoring, disaster assessment, and land-usemapping. These models include unimodal vision encoders trained in a single data modalityand multimodal architectures trained in multiple sensor modalities, such as synthetic aper-ture radar (SAR), multispectral, and hyperspectral imagery, or jointly in image-text pairs invision-language settings. FMs are adapted to diverse perception tasks, such as semantic seg-mentation, image classification, change detection, and visual question answering, dependingon their pretraining objectives and architectural design. However, selecting the most suit-able remote sensing foundation model (RSFM) for a specific task remains challenging dueto scattered documentation, heterogeneous formats, and complex deployment constraints.To address this, we first introduce the RSFM Database (RS-FMD), the first structuredand schema-guided resource covering over 160 RSFMs trained on various data modalities,spanning different spatial, spectral, and temporal resolutions, considering different learn-ing paradigms. Built upon RS-FMD, we further present Remsa (Remote-sensing ModelSelection Agent), a constraint-aware agent that enables automated RSFM selection from nat-ural language queries. Remsa combines structured FM metadata grounding with task-awareorchestration for retrieval, clarification, ranking, and explanation. In detail, it interprets userinput, clarifies missing constraints, ranks models via in-context learning, and provides trans-parent justifications. Our system supports various RS tasks and data modalities, enablingpersonalized, reproducible, and efficient FM selection. To evaluate Remsa, we constructa benchmark of 100 expert-verified RS query scenarios. Each query is evaluated across 4systems and 3 LLM backbones, with the top-3 selected models assessed by domain expertsusing a fixed suitability rubric. This results in 3,000 expert-scored task–system–model con-figurations under our rubric-based expert evaluation protocol. Remsa outperforms multiplebaselines, including naive agent-based method, dense retrieval, and unstructured retrievalaugmented generation approaches, showing its practical utility in real decision-making ap-plications. Remsa operates entirely on publicly available metadata of open source RSFMs,without accessing private or sensitive data. Our code and data are publicly available at:https://github.com/be-chen/REMSA.

ECCV 2026

FedMental: Topology-Aware Federated Prototype Learning for Polymorphic Multimodal Psychiatry

Haoyu Li, He Li, Wenke Huang, Yujing Rao, Xiaofen Zong, Mang Ye

Psychiatric diagnosis is moving from subjective clinical ob-servation toward objective multimodal assessment using audio, visual,and textual signals. Federated learning offers a natural framework formulti-center collaboration without centralizing sensitive patient data,but psychiatric data introduce a form of non-IID heterogeneity thatis not captured by standard label or domain skew: patients with thesame diagnosis can exhibit highly polymorphic manifestations, form-ing multi-peak within-class distributions. Existing prototype-based FLmethods usually aggregate client or class features into coarse centroids,which can collapse distinct symptom modes into semantically ambigu-ous prototypes. We propose FedMental, a topology-aware federatedprototype framework for multi-center multimodal psychiatry. FedMen-tal first preserves subsampled sample-level embeddings before prema-ture client-mean aggregation, then applies Topology-Aware Proto-type Refinement (TAPR) on the server to discover class-view la-tent modes through parameter-free clustering. On the client side, Intra-View Mode Alignment (IVMA) softly anchors local representationsto matched mode prototypes, while Cross-View Semantic Contrast(CVSC) promotes view-invariant semantics across modalities and cen-ters. Experiments on the real-world Psycare psychiatric benchmark andtwo standard heterogeneous FL benchmarks show that FedMental consis-tently improves over strong FL baselines, achieving +8.83% over FedAvgon Psycare, +11.42% on Office-Caltech, and +4.41% on Digits.

ECCV 2026

FeatTracker: Short- and Long-Range Temporal Feature Consistency for Robust Underwater Object Tracking

Jiaqing Li, Bin Lin, Chaocan Xue, Wu Ai, Qingping Zheng

Existing underwater object tracking (UOT) methods can bebroadly divided into two categories: frame-level trackers and video-leveltrackers. Frame-level trackers process each frame independently, usingenhancement or adaptation techniques to handle underwater distortions.However, this often results in inconsistent feature styles and weakenedtemporal correlations. Video-level trackers attempt to improve temporalconsistency by modeling dependencies across frames through autoregres-sive mechanisms. Despite this, they still struggle with persistent featuredegradation and tracking drift caused by challenging underwater condi-tions. To overcome these limitations, we propose FeatTracker, a feature-level tracking framework that enforces both short- and long-range tem-poral feature consistency, effectively preserving semantic integrity andensuring temporal stability for robust underwater object tracking. Theframework integrates two key innovations: the Short-Range TemporalFeature Consistency module, which combines diffusion-like feature en-hancement with a dynamic memory pool to mitigate degradation andpreserve local temporal coherence; and the Long-Range Temporal Fea-ture Consistency module, which leverages wavelet decomposition to sepa-rate stable structural components from transient details within historicaltokens, effectively preventing the loss of trajectory details. Extensive ex-periments demonstrate that FeatTracker achieves state-of-the-art perfor-mance across four UOT benchmarks. Our code and models are availableat https://github.com/fishgfish/FeatTracker.

ECCV 2026

ReQuest: Rethinking-based Question-Aware Frame Selection for Long-Form Video QA

Minkuk Kim, Suyong Yun, Young Kim, Jinyoung Moon, Jinwoo Choi, Seong Tae Kim

Recent multimodal large language models (MLLMs) havesubstantially advanced video understanding, yet long-form video QAremains challenging under fixed input token budgets, where uniform sam-pling can be inefficient for evidence localization. We propose ReQuest, an uncertainty-driven, question-adaptive keyframe selection pipelinethat aligns question intent with relevant video content through selec-tive computation. ReQuest integrates (i) a lightweight question-awareselector distilled from MLLM-generated supervision, (ii) Re-thinkingRouting that triggers additional inference only when the model is uncer-tain with a length-adaptive criterion, and (iii) uncertainty-guided adap-tive non-maximum suppression that selects temporally diverse frameswhile adjusting spacing based on question difficulty. As a plug-and-play method, ReQuest improves long-video QA without modifyingor fine-tuning the underlying MLLM. Experiments on Video-MME,MLVU, and LongVideoBench demonstrate consistent accuracy gainswith competitive computational cost, with particularly strong improve-ments in medium and long video regimes. The code is available athttps://geppa.github.io/ReQuest

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