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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

Two Birds, One Projection: Harmonizing Safety and Utility in LVLMs via Inference-time Feature Projection

Yewon Han, Yumin Seol, Minsoo Jo, EunGyung Kong, Taesup Kim

Existing jailbreak defense frameworks for Large Vision-LanguageModels (LVLMs) often suffer from a safety–utility tradeoff, where strength-ening safety inadvertently degrades performance on general visual-groundedreasoning tasks. In this work, we investigate whether safety and utilityare inherently antagonistic objectives. We focus on a modality-inducedbias direction consistently observed across datasets, which arises fromsuboptimal coupling between the LLM backbone and visual encoders.We further demonstrate that this direction undermines performance onboth tasks. Leveraging this insight, we propose TBOP (Two Birds, OneProjection), an efficient inference-time jailbreak defense that projectscross-modal features onto the null space of the identified bias direction toremove the corresponding components. Requiring only a single forwardpass, our method effectively breaks the conventional tradeoff, simultane-ously improving both safety and utility across diverse benchmarks.

ECCV 2026

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning

Wenhao Yuan, Chenchen Lin, Jian Chen, Jinfeng Xu, Zewei Liu, Edith C. H. Ngai

Federated Learning (FL) emerged as a promising distributedmachine learning paradigm. However, extending FL to the class incre-mental learning scenarios introduces unique challenges: 1) Capacity con-flict and catastrophic forgetting from the shared model overloading, 2)Heterogeneity from Non-Independent and Identically Distributed (Non-IID) data, and 3) Synchronized class misalignment. In this paper, we pro-pose Fisher-Routed MiXture of Experts for Federated Class-IncrementalLearning (FedFMX), a novel framework to address these challenges viaadaptive expert specialization across clients. The crucial insight is toroute each sample to an expert subset that jointly optimizes knowledgeacquisition and retention. Specifically, we introduce a Fisher-Routed Ex-pert Scoring (FRES) module to estimate expert importance via Fisher-based stability cost and gradient-based plasticity gain. Then, we designan Adaptive Expert Selection (AES) module by quantifying marginalcontributions for adaptive expert subset determination. Finally, by therouting-aware regularization (RAR), we achieve load balance and effi-cient FL training. We theoretically prove the O(T −1 ) convergence rate.Extensive experiments on multiple benchmarks compared with state-of-the-art methods demonstrate the superiority of FedFMX.

ECCV 2026

TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception

Quinlan Sykora, Sourav Biswas, Christopher Diehl, Andrew Cunningham, Thomas Gilles, Raquel Urtasun

We present TriO, a multi-modal unsupervised world modelthat predicts 4D occupancy, obstacle segmentation, flow and LiDAR.In contrast to prior work, TriO utilizes three distinct sensor modali-ties (camera, LiDAR, and RADAR) as both inputs and sources of self-supervision, eliminating the need for additional human annotations. Thanksto its novel supervision, the model is able to segment any occupancyfrom the drivable surface, overcoming the limitations of existing open-set methods in handling long-tail objects. TriO achieves state-of-the-artresults in multiple 3D and 4D tasks, including occupancy, flow, and Li-DAR prediction, as well as zero-shot road obstacle segmentation acrossmultiple datasets such as Argoverse 2, and Spotting the Unexpected.Fig. 1: We present TriO, an unsupervised tri-modal occupancy world model designedto perceive and forecast anything that could be considered path-blocking.

ECCV 2026

Towards Sparsely Annotated Open World Object Detection

HeeJu Han, AJeong Kim, Jinsun Park

Real-world object detection operates under ambiguous su-pervision, where unlabeled regions may correspond to missing annota-tions of known objects or genuinely unknown categories. These chal-lenges have been addressed separately in Sparsely Annotated Object De-tection (SAOD) and Open-World Object Detection (OWOD). In prac-tice, their co-occurrence remains an open problem. To address this prob-lem, we introduce Sparsely Annotated Open-World Object Detection(SA-OWOD), a new task that jointly considers sparse supervision andthe presence of unseen categories. We propose Dual-Perspective ObjectDiscovery (DPOD), a unified framework that jointly models unlabeledknown and unknown instances via two complementary mechanisms. TheKnown Target Recovery Module (KTRM) recovers supervision for unla-beled known instances and explicitly regularizes the feature space to sep-arate known and unknown representations. Complementarily, the Dual-Disagreement Target Generator (DDTG) identifies reliable unknown can-didates through cross-view semantic inconsistency. By integrating thesemodules, DPOD resolves contradictory supervision signals caused byambiguous unlabeled regions. As a result, it prevents misclassificationbetween known and unknown objects and stabilizes the decision bound-aries. Experimental results on sparsely annotated open-world bench-marks demonstrate that the proposed method outperforms existing open-world detection methods, particularly in detecting unknown objects. Thecode is publicly available at: https://github.com/HelloHeeju/SA-OWOD

ECCV 2026

DualTAP: A Dual-Task Adversarial Protector for Mobile MLLM Agents

Fuyao Zhang, Jiaming Zhang, Che Wang, Xiongtao Sun, Yurong Hao, Guowei Guan, Wenjie Li, Longtao Huang, Wei Lim

The reliance of mobile GUI agents on Multimodal LargeLanguage Models (MLLMs) introduces a severe privacy vulnerability:screenshots containing Personally Identifiable Information (PII) are of-ten sent to untrusted, third-party routers. These routers can exploit theirown MLLMs to mine this data, violating user privacy. Existing privacyperturbations fail the critical dual challenge of this scenario: protect-ing PII from the router’s MLLM while simultaneously preserving taskutility for the agent’s MLLM. To address this gap, we propose the Dual-Task Adversarial Protector (DualTAP), a novel framework that,for the first time, explicitly decouples these conflicting objectives. Dual-TAP trains a lightweight generator using two key innovations: (i) a con-trastive attention module that precisely identifies and targets only thePII-sensitive regions, and (ii) a dual-task adversarial objective that si-multaneously minimizes a task-preservation loss (to maintain agent util-ity) and a privacy-interference loss (to suppress PII leakage). To facilitatethis study, we introduce PrivScreen, a new dataset of annotated mobilescreenshots designed specifically for this dual-task evaluation. Compre-hensive experiments on six diverse MLLMs (e.g., GPT-5) demonstrateDualTAP’s state-of-the-art protection. It reduces the average privacyleakage rate to 31.7 percentage points (a 2.6× relative improvement)while, critically, maintaining an 80.8% task success rate—a negligibledrop from the 83.6% unprotected baseline. DualTAP presents the firstviable solution to the privacy-utility trade-off in mobile MLLM agents.Our code is available at https://github.com/fyzhang1/DualTAP.

ECCV 2026

Topology-Weighted Effective Rank: A Zero-Cost Proxy for Training Dynamics Stability in Neural Architecture Search

Haojie Zhang, Yeming Yang, Songbai Liu, Lijia Ma, Ka-Chun Wong, Qiuzhen Lin

Recent training-free Neural Architecture Search (NAS) meth-ods have introduced zero-cost proxies (ZCPs) to automate architecturedesign without expensive training or expert intervention. However, exist-ing ZCPs either completely ignore training dynamics or rely on static orinitialization-time characterizations of optimization that fail to capturefine-grained correlations with performance. Moreover, they overlook theheterogeneous importance of different model components, which limitstheir ability to generalize across datasets and search spaces. To mitigatethese limitations, we propose an Effective Rank Score called ER-Scoreas a novel ZCP to quantify stability in the training dynamics ofover-parameterized networks. Experiments show that ER-Score con-sistently outperforms existing ZCPs across multi-scale tasks and diversearchitectures, including convolutional neural networks (CNNs) and Vi-sion Transformers (ViTs). We further introduce topology-weighted strate-gies for CNNs that incorporate topological information via operation-wise feature map aggregation, resulting in a topology-weighted vari-ant termed TER-Score. Extensive experiments demonstrate that TER-Score achieves state-of-the-art ranking consistency and the best pop-ulation convergence when integrated with evolutionary search on theNAS-Bench-301 benchmark. Finally, our method achieves the lowesttest errors of 2.41% and 23.55% on the DARTS search space for theCIFAR-10 and ImageNet-1k datasets, respectively. Code is released athttps://github.com/Thiswycf/TER-Score.

ECCV 2026

ThermoGS: Decoupling Physical Surface Attributes for Spatio-Temporal Thermal Field Emulation via 4D Gaussian Splatting

Kun Yang, Yuxiang Liu, Zeyu Cui, Shen Yan, Maojun Zhang, Yu Liu, Xue Wang, Qing Wang

Thermal infrared (TIR) imaging is indispensable for all-weatherperception and energy management, yet existing 3D thermal modelingmethods remain primarily confined to static environmental snapshots.These approaches typically struggle to capture complex temporal tem-perature variations and lack the capacity to simulate scenes under unseenenvironmental conditions. To bridge this gap, we propose ThermoGS,a unified framework that couples 4D Gaussian Splatting (4DGS) withfundamental thermodynamic principles to achieve physically-consistentthermal field emulation. Unlike previous data-driven methods that di-rectly regress radiance, ThermoGS decouples intrinsic material proper-ties, including emissivity (e), convective heat transfer coefficient (h), vol-umetric heat capacity (c), and absorptivity (α)—from transient temper-ature states. By integrating Physics-Informed Neural Networks (PINNs),our framework ensures that the predicted spatiotemporal thermal distri-bution strictly adheres to energy balance equations, enabling generalizedsimulation across arbitrary viewpoints and time periods. Furthermore,we introduce the Thermo Dataset, a comprehensive, drone-captured col-lection featuring synchronized multi-modal data and diverse environmen-tal parameters. Extensive experiments demonstrate that ThermoGS sig-nificantly outperforms comparative methods in thermal simulation accu-racy, maintaining a predicted temperature error within 1 degree Celsius.The code is available at https://github.com/NPU-CVPG/ThermoGS.

ECCV 2026

Teaching Vision-Language-Action Models What to See and Where to Look

Yuguang Yang, Canyu Chen, Zhewen Tan, Yizhi Wang, Zichao Feng, Chunyang Liu, Kehua Sheng, Bo Zhang, Yan Wang, Juan Zhang, Linlin Yang, Baochang Zhang, Xianbin Cao

Vision-Language-Action (VLA) models have emerged as apromising paradigm for end-to-end autonomous driving. However, exist-ing VLAs’ traffic relies heavily on text-centric visual question answeringand chain-of-thought reasoning data, which emphasizes linguistic rea-soning rather than action-grounded planning. As a result, the learnedrepresentations capture semantic knowledge but lack spatial dependenciescrucial for reliable trajectory prediction. We propose DriveTeach-VLA,a framework that explicitly teaches VLAs what to see and where tolook. Driving-aware Vision Distillation (DVD) injects driving-specificperceptual priors into the vision encoder, while 2D Trajectory-GuidedPrompts (2D-TGP) provide spatial conditioning aligned with feasibledriving trajectories. Together they form a vision-guided learning pipeline:what to see (DVD pretraining) → where to look (TGP-guided SFT) →how to act (TGP-guided GRPO). DriveTeach-VLA achieves the state-of-the-art performance on NAVSIM and nuScenes. Our code is available at:https://github.com/ShivaTeam/DriveTeach-VLA.

ECCV 2026

STVFocus: Query-guided Spatio-Temporal Visual Focusing for Video LLMs

Taehun Kong, Minyoung Park, Sangjun Ahn

Video Large Language Models (Video LLMs) aim to under-stand and reason over dynamic visual content. However, videos inher-ently exhibit low task-relevant information density because only a smallfraction of frames and regions provide meaningful cues. As a result, theuseful signals are sparse across space and time. We present STVFocus, atraining-free framework that unifies spatio-temporal focusing to capturesalient contexts and build informative video representations. STVFocusconsists of three components. First, Hierarchical Temporal Focus appliesa coarse-to-fine frame selection strategy that captures global temporalstructure while selectively emphasizing informative moments without ex-cessive query bias. Second, Query-informed Spatial Focus uses spatialsaliency to highlight query-relevant intra-frame regions. Finally, Tem-poral Reallocation reinvests the saved spatial capacity into additionalhigh-value frames, enabling richer temporal coverage. By jointly leverag-ing temporal and spatial saliency, STVFocus produces compact yet se-mantically enriched video representations. Experiments on VideoMME,LongVideoBench, and MLVU show consistent gains with meaningfulmargins, confirming the effectiveness of unified spatio-temporal focus-ing.

ECCV 2026

Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration

Ivor Simpson, Neill Campbell

Image registration is an ill-posed dense vision task, wheremultiple solutions achieve similar loss values, motivating probabilisticinference. Variational inference has previously been employed to capturethese distributions, however restrictive assumptions about the posteriorform can lead to poor characterisation, overconfidence and low-qualitysamples. More flexible posteriors are typically bottlenecked by the com-plexity of high-dimensional covariance matrices required for dense 3Dimage registration.In this work, we present a memory and computationally e!cient in-ference method, Structured SIR, that enables expressive, multi-modal,characterisation of uncertainty with high quality samples. We proposethe use of a Sampled Importance Resampling (SIR) algorithm witha novel memory-e!cient high-dimensional covariance parameterisationas the sum of a low-rank covariance and a sparse, spatially structuredCholesky precision factor. This structure enables capturing complex spa-tial correlations while remaining computationally tractable.We evaluate the e!cacy of this approach in 3D dense image registrationof brain MRI data, which is a very high-dimensional problem. We demon-strate that our proposed method produces uncertainty estimates that aresignificantly better calibrated than those produced by variational meth-ods, achieving equivalent or better accuracy. Crucially, we show thatthe model yields highly structured multi-modal posterior distributions,enable e"ective and e!cient uncertainty quantification.

ECCV 2026

SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence

Bowei Tian, Yexiao He, Ziyao Wang, Meng Liu, Yongkai Wu, Ang Li

Deep learning models exhibit fairness concerns when pre-dictions are inadvertently influenced by sensitive attributes. However,existing attempts to make Path-Specific Counterfactual Fairness opti-mizable rely on estimating marginal potential outcome probabilities—anapproach that fundamentally requires high-dimensional conditional den-sity estimation and breaks down in modalities such as medical images,where the curse of dimensionality renders reliable estimation infeasible.To address this limitation, we reduce the problem of enforcing Path-Specific Counterfactual Fairness to a causal conditional independenceconstraint and prove that satisfying this constraint is sufficient to elim-inate the unfair causal effect. This reduction replaces intractable coun-terfactual estimation with a discriminative optimization objective thatremains scalable in high-dimensional settings.

ECCV 2026

MED-LCDS: Multi-Expert-Domain CLIP Classification via Logit Calibration

Zheng Zeng, Deepak Sridhar, Nuno Vasconcelos

Vision-language models (VLMs) such as CLIP enable zero-shot classification by comparing image features with text prompts in ashared embedding space. A fundamental property underlying this ca-pability is the global comparability of logits across arbitrary candidateclasses. However, VLMs are often adapted to fine-grained domains usingtechniques such as LoRA. While this improves in-domain accuracy, out-of-domain accuracy degrades. This leads to a higly fragmented modelecosystem, with thousands of specialized models. Multi-Expert-Domain(MED) classification seeks to address this problem, by merging LoRAstrained independently on specialized domains. However, due to the inde-pendent training, the various domain experts no longer produce globallycalibrated logits. As a result, when evaluating over the union of multipledomain-specific class sets, heterogeneous logit scales induce cross-domaininterference and artificially high confidence for out-of-domain classes,inducing prediction errors. In this work, we identify domain supervi-sion and cross-domain logit miscalibration as the key issue to scalablemulti-domain zero-shot recognition. We propose a mixture-of-expertsMED architecture, MED-DSLC, combining domain supervised training anddomain-wise logit scaling, to explicitly restore global logit comparability.MED-DSLC is a lightweight solution for MED classification, which is shownto preserve within-domain discrimination while reducing cross-domainlogit interference with minimal data. Extensive experiments across di-verse fine-grained benchmarks demonstrate that it substantially improvesmean accuracy (+15%), cross-domain robustness, and scalability in thesize of MED classification problem. Our results show that restoring output-level calibration is essential under highly data imbalanced settings forachieving a truly zero-shot VLM under multi-domain specialization. Codeis publicly available at MED-DSLC.

ECCV 2026

SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models

Hongxiang Li, Hongxu Chen, Chenyang Zhu, Xiaoshuang Huang, Jiayin Cai, Xiaolong Jiang, Yao Hu, Long Chen

Multimodal Large Language Models (MLLMs) have achievedremarkable success in visual understanding but remain constrained invisual generation due to the fundamental feature discrepancy betweensemantic perception and pixel-level reconstruction. Bridging this gap re-quires overcoming two core challenges: endowing semantic encoders withhigh-fidelity reconstruction capabilities, and effectively aligning gener-ative models with semantic spaces without relying on external teach-ers. To this end, we propose a novel unified multimodal framework fea-turing Semantic-Pixel self-alignment and Adaptive Routing (SPAR).First, to reconcile semantic perception with pixel-level reconstruction, weintroduce an asymmetric dual-stream unified tokenizer. A lightweightsemantic stream anchors discriminative features, while a Transformer-augmented pixel stream recovers fine-grained visual details into a uni-fied compact latent space. Second, to eliminate external dependencies, wepropose a self-aligned generation paradigm that natively leverages thisoptimized tokenizer as an internal alignment teacher for the diffusionmodel. Furthermore, to facilitate flexible multimodal interaction withinthis unified space, we introduce Dynamic Token Routing, which enableseach token to adaptively aggregate multi-layer MLLM features based onits distinct semantic demands. Extensive experiments demonstrate thatSPAR establishes the state-of-the-art for unified architectures, achiev-ing exceptional generation and reconstruction quality while preservingfoundational visual understanding capabilities.

ECCV 2026

Spanning the Visual Analogy Space with a Weight Basis of LoRAs

Hila Manor, Rinon Gal, Haggai Maron, Tomer Michaeli, Gal Chechik

Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words. Given a triplet {a, a′, b}, the goal is to generate b′ such that a : a′ :: b : b′. Recent methods adapt text-to-image models with a single Low-Rank Adaptation (LoRA) module, but they face a fundamental limitation: attempting to capture the diverse space of visual transformations within a fixed module constrains generalization. Inspired by recent work showing that LoRAs in constrained domains span meaningful, interpolatable semantic spaces, we propose LoRWeB, which specializes the model for each analogy task in a single inference pass. LoRWeB dynamically composes learned transformation primitives, informally, choosing a point in a “space of LoRAs”. We introduce two key components: (1) a learnable basis of LoRAs to span the space of different visual transformations, and (2) a lightweight encoder that dynamically weighs these basis LoRAs given the input analogy pair. Comprehensive evaluations demonstrate state-of-the-art performance and significantly improved generalization to unseen transformations. Our findings suggest LoRA basis decompositions are a promising direction for flexible visual manipulation tasks. See our website for code.

ECCV 2026

Task-Agnostic Incremental Vision-Language Object Detection via Prompt Augmentation and Distribution-Aware Fusion

Yonghan Jiang, Zhengyuan Xie, Wenchu Liu, Linlan Huang, Fei Yang, Xialei Liu

Incremental Vision-Language Object Detection (IVLOD) em-powers pre-trained open-vocabulary detectors to continuously learn newvisual concepts from sequential tasks without forgetting foundationalknowledge. However, existing methods predominantly rely on oracle taskidentities during inference, severely limiting their open-world practical-ity. In this paper, we formalize Task-Agnostic IVLOD (TA-IVLOD), amore realistic setting where such priors are unavailable, requiring themodel to simultaneously recognize classes from all learned tasks. Thisunconstrained setting exposes severe performance degradation due tocross-task semantic interference and parameter conflicts. To tackle thesechallenges, we propose TADA, a novel modular framework. Specifically,TADA introduces Stochastic Prompt Augmentation to mitigate semanticinterference via training-time noise injection, and Test-Time Distribution-Aware Fusion to dynamically weight class-specific LoRA experts, effec-tively resolving parameter conflicts. Extensive evaluations demonstratethat our method significantly outperforms baselines in standard IVLODand yields substantial improvements in the rigorous TA-IVLOD set-ting on the ODinW-13 benchmark, while effectively preserving zero-shot generalization on the MS COCO dataset. The code is available athttps://github.com/yonghanjiang/TADA.

ECCV 2026

RePer-360: Releasing Perspective Priors for 360° Depth Estimation via Self-Modulation

cheng guan, Chunyu Lin, Zhijie Shen, Junsong Zhang, JiYuan Wang

Recent depth foundation models trained on perspective im-agery achieve strong performance, yet generalize poorly to 360∘ imagesdue to the substantial geometric discrepancy between perspective andpanoramic domains. Moreover, fully fine-tuning these models typicallyrequires large amounts of panoramic data. To address this issue, wepropose RePer-360, a distortion-aware self-modulation framework formonocular panoramic depth estimation that adapts depth foundationmodels while preserving powerful pretrained perspective priors. Specifi-cally, we design a lightweight geometry-aligned guidance module to derivea modulation signal from two complementary projections (i.e., ERP andCP) and use it to guide the model toward the panoramic domain withoutoverwriting its pretrained perspective knowledge. We further introduce aSelf-Conditioned AdaLN-Zero mechanism that produces pixel-wise scalingfactors to reduce the feature distribution gap between the perspectiveand panoramic domains. In addition, a cubemap-domain consistency lossfurther improves training stability and cross-projection alignment. Byshifting the focus from complementary-projection fusion to panoramic do-main adaptation under preserved pretrained perspective priors, RePer-360surpasses standard fine-tuning methods while using only 1% of the train-ing data. Under the same in-domain training setting, it further achievesan approximately 20% improvement in RMSE. The code is available athttps://github.com/munimo/RePer360.

ECCV 2026

REON-NVS: Real-Time Online Novel-View Synthesis from Sparse-View Videos

Daeyeon Kim, Jinhyeok Kim, Gangmin Kwon, Seungjoo Shin, Sunghyun Cho

Recent advances in online reconstruction of dynamic scenesdemonstrate impressive visual quality, showing potential for real-worldapplications such as VR content streaming. Yet, existing online recon-struction methods still require a large number of input views and time-consuming iterative optimization. Moreover, reliable pose estimation,a prerequisite for NVS, introduces additional delay. In this paper, wepresent REON-NVS, a feedforward online NVS framework for sparse-view input video streams, which goes from pose estimation to novel-viewreconstruction in real time. REON-NVS comprises two main compo-nents. First, our method leverages a feedforward pose estimator andmapper that replace conventional camera pose optimization pipelines.Second, we design a scene reconstructor built upon a state-space model(SSM), which efficiently synthesizes temporally consistent novel-view im-ages by exploiting information from previous frames. To train and eval-uate our approach in realistic in-the-wild streaming scenarios, we intro-duce a new multi-view dynamic scene dataset of 150 dynamic scenescaptured with moving cameras. Extensive experiments demonstrate thatREON-NVS achieves high visual quality while operating in real time (32FPS), validating its applicability in real-world scenarios.

ECCV 2026

RBE-Flow:Recurrent Bayesian Estimation on Feature Manifolds for Cross-Modal Registration

Mengzhu Ding, XIN SONG, Xiaoke Ding, Hongwei Ding, Xuecong Liu

Cross-modal image registration is essential for multi-sensorperception but remains fundamentally challenging due to severe non-linear radiometric discrepancies and geometric distortions. Existing de-terministic matching methods lack uncertainty awareness, struggling tonavigate the resulting highly non-convex optimization landscape and fre-quently accumulating errors in ambiguous regions. In this paper, wepropose RBE-Flow, a novel framework that reformulates dense cross-modal flow estimation as a closed-loop recurrent Bayesian estimationproblem on learned feature manifolds. Diverging from standard feed-forward regression, RBE-Flow establishes a robust self-correcting mech-anism by deeply coupling feature-metric non-linear optimization withprobabilistic state updates. Specifically, a Recurrent Manifold Optimiza-tion (RMO) block iteratively generates flow observations and their as-sociated uncertainties, which are then optimally assimilated into theprior state via an Uncertainty-Adaptive Probabilistic Update (UAPU)using deterministic sigma-point projection. Crucially, the resulting cal-ibrated posterior covariance is fed back to adaptively regularize thedamping of subsequent optimization steps, allowing the system to mod-ulate its convergence based on predictive confidence. To ensure stableprobabilistic training, we introduce a hybrid supervision scheme featur-ing a geometry-aware rectified NLL loss that structurally prevents vari-ance collapse. Extensive experiments on challenging OSdataset, WHU-OPT-SAR, and RoadScene benchmarks demonstrate that RBE-Flowconsistently achieves state-of-the-art performance, outperforming exist-ing methods by a significant margin, particularly under strict sub-pixelcriteria. Project page: https://github.com/NEU-Liuxuecong/RBE-Flow

ECCV 2026

Progressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalities

Jindi Bao, Jianjun Qian, Mengkai Yan, Jian Yang

Multimodal Sentiment Analysis (MSA) seeks to infer humanemotions by integrating textual, acoustic, and visual cues. However, ex-isting approaches often rely on all modalities are completeness, whereasreal-world applications frequently encounter noise, hardware failures, orprivacy restrictions that result in missing modalities. There exists a sig-nificant feature misalignment between incomplete and complete modali-ties, and directly fusing them may even distort the well-learned represen-tations of the intact modalities. To this end, we propose PRLF, a Pro-gressive Representation Learning Framework designed for MSA underuncertain missing-modality conditions. PRLF introduces an AdaptiveModality Reliability Estimator (AMRE), which dynamically quantifiesthe reliability of each modality using recognition confidence and Fisherinformation to determine the dominant modality. In addition, the Pro-gressive Interaction (ProgInteract) module iteratively aligns the othermodalities with the dominant one, thereby enhancing cross-modal con-sistency while suppressing noise. Extensive experiments on CMU-MOSI,CMU-MOSEI, and SIMS verify that PRLF outperforms state-of-the-artmethods across both inter- and intra-modality missing scenarios, demon-strating its robustness and generalization capability.

ECCV 2026

PolyLayout: Multi-room Manhattan Layout Estimation

Gustav Hanning, Shaohui Liu, Remi Pautrat, Marc Pollefeys, Kalle Astrom, Viktor Larsson

Estimating room layouts from multi-view imagery is a coretask for indoor scene understanding. Existing methods are typically lim-ited either by poor generalization to new datasets or restrictive geometricassumptions of the room shape or camera configuration. Most also es-timate rooms independently, failing to exploit shared building structuresuch as dominant directions, ground plane or ceiling height.We propose PolyLayout, a multi-room layout estimation method that pa-rameterizes room layouts as Manhattan 3D polygons and optimizes themjointly across multiple rooms. The optimization objective is predicted bya neural network on top of robust pre-trained visual features and trainedend-to-end with supervision only on output room layouts. At the sametime, camera projection and polygon updates remain explicit and model-based. This separation between learned scoring and geometry improvesgeneralization to new datasets and camera parameters. During optimiza-tion, PolyLayout adaptively refines the polygon topology through iter-ative wall split and merge operations while jointly utilizing structuralcues across rooms. We introduce two new multi-view multi-room layoutbenchmarks by providing layout annotations to existing datasets, andexperiments show that PolyLayout outperforms prior approaches, bothin terms of accuracy and robustness.Project page: https://ghanning.github.io/PolyLayout

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