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ECCV 2026

StrucTab: A Structured Optimization Framework for Table Parsing

Gengluo Li, Shangpin Peng, Chengquan Zhang, Binghong Wu, Hao Feng, Weinong Wang, Pengyuan Lyu, Huawen Shen, Xingyu Wan, Zhuotao Tian, Han Hu, Can Ma, Yu Zhou

Table parsing aims to convert table images into structured,machine-readable representations, a task requiring the joint perceptionof complex spatial layouts and textual content. While recent vision-language models (VLMs) enable end-to-end parsing, they typically relyon direct supervision of the final output, thereby bypassing the explicitintermediate reasoning that is crucial for understanding complex tablestructures. Furthermore, attempts to optimize these models using re-inforcement learning (RL) are often hindered by unstable or ambigu-ous reward designs, limiting potential performance gains. To addressthese limitations, we propose StrucTab, a table parsing model learnedthrough intermediate structural supervision and reward decomposition.At the modeling level, by decomposing the parsing process into human-inspired subtasks, such as row-column counting and merged-cell analy-sis, StrucTab progressively unifies them through a sequential reasoningstrategy. At the optimization level, we introduce Uni-TabRL, a unifiedRL framework that leverages decomposed rewards (validity, structure,and content) to provide stable and informative optimization signals. Fi-nally, at the evaluation level, we present TableVerse-5K, a large-scale,challenging benchmark encompassing diverse, real-world table scenarios.Extensive experiments demonstrate the state-of-the-art performance ofStrucTab across all evaluated public benchmarks and significant improve-ments on TableVerse-5K, validating the effectiveness of explicit structuralmodeling and decomposed reward optimization. Code and benchmark arepublicly available at https://github.com/VirtualLUOUCAS/StrucTab.

ECCV 2026

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models

Weiyang Jin, Yuwei Niu, Jiaqi Liao, Chengqi Duan, Aoxue Li, Shenghua Gao, Xihui Liu

Recently, remarkable progress has been made in Uni_x001C_ed Mul-timodal Models (UMMs), which integrate vision-language generation andunderstanding capabilities within a single framework. However, a model'sstrong visual understanding often fails to transfer to visual generation:it may correctly judge prompt-image alignment while failing to gener-ate a faithful image from the same prompt. This raises a compellingquestion: Can a model improve itself by using its understanding moduleto reward its generation module? We introduce SRUM, a self-rewardingpost-training framework directly applicable to existing UMMs of vari-ous designs. SRUM creates a feedback loop where the model's own un-derstanding module acts as an internal _x0010_evaluator_x0011_, providing correctivesignals to improve generation without additional human-labeled data orexternal reward models. To provide comprehensive feedback, SRUM usesa global-local dual reward system: a global reward ensures overall vi-sual semantics and layout, while a local reward re_x001C_nes _x001C_ne-grained,object-level _x001C_delity. SRUM shows strong generalization, boosting perfor-mance on T2I-CompBench from 82.18 to 88.37 and on T2I-ReasonBenchfrom 43.82 to 46.75. Overall, our work establishes a powerful paradigmfor enabling a UMM's understanding module to guide and enhance itsown generation via self-rewarding.

ECCV 2026

SIMSplat: Language-Aligned 4D Gaussian Splatting for Driving Scenario Generation

Sung-Yeon Park, Adam Lee, Juanwu Lu, Can Cui, Luyang Jiang, Rohit Gupta, Kyungtae Han, Ahmadreza Moradipari, Ziran Wang

Driving scene manipulation using real-world sensor data hasemerged as a promising alternative to traditional driving simulators. De-spite advances in language control and neural scene representations, ex-isting methods treat grounding, editing, and simulation as loosely con-nected stages, relying on heuristic object localization, manual guidance,and single-agent validation—thereby constraining semantic expressive-ness and hindering scalable, reactive scenario generation. We introduceSIMSplat, a driving scene editor built on scene-graph-based 4D Gaus-sian Splatting augmented with language-aligned features. By embed-ding appearance, motion, and location semantics directly into Gaus-sian scene-graph nodes, SIMSplat makes reconstructed scenes queryablethrough free-form natural language, bridging language understanding toobject-level editing and multi-agent simulation within a single frame-work. Building on this language-grounded scene graph, SIMSplat sup-ports diverse edits including fine-grained pedestrian manipulation, whilea multi-agent path refinement module propagates changes across allagents to ensure reactive, physically plausible simulations. The pipelinefurther integrates with Vision-Language Models for automated scenariomining. Experiments show that SIMSplat more than doubles baselinegrounding accuracy, achieves the highest task completion rate, and pro-duces the lowest failure rates across diverse driving scenarios.

ECCV 2026

SimFlow: Simplified and End-to-End Training of Latent Normalizing Flows

Qinyu Zhao, Guangting Zheng, Tao Yang, Rui Zhu, Xingjian Leng, Stephen Gould, Liang Zheng

Normalizing Flows (NFs) learn invertible mappings betweenthe data and a Gaussian distribution. Prior works usually suffer fromtwo limitations. First, they add random noise to training samples orVAE latents as data augmentation, introducing complex pipelines in-cluding extra noising and denoising steps. Second, they use a pretrainedand frozen VAE encoder, resulting in suboptimal reconstruction and gen-eration quality. In this paper, we find that the two issues can be solvedin a very simple way: just fixing the variance (which would otherwise bepredicted by the VAE encoder) to a constant (e.g., 0.5). On the one hand,this method allows the encoder to output a broader distribution of tokensand the decoder to learn to reconstruct clean images from the augmentedtoken distribution, avoiding additional noise or denoising design. On theother hand, fixed variance simplifies the VAE evidence lower bound, mak-ing it stable to train an NF with a VAE jointly. On the ImageNet 256×256generation task, our model SimFlow obtains a gFID score of 2.15, outper-forming the state-of-the-art method STARFlow (gFID 2.40). Moreover,SimFlow can be seamlessly integrated with the end-to-end representa-tion alignment (REPA-E) method and achieves an improved gFID of1.91, setting a new state of the art among NFs.

ECCV 2026

Reinforcement Learning for Multimodal Diffusion Language Models via Bidimensional Trajectory and Thought Optimization

Bowen Li, Yinjie Wang, Yunzhi Zhang, Junhong Liu, Yingqing Guo, Jiajun Wu, Mengdi Wang, Ling Yang

Diffusion Large Language Models (dLLMs) have achievedremarkable success in pure language tasks and recently shown promis-ing results in multimodal understanding. However, existing post-trainingmethods for multimodal dLLMs have not yet fully exploited the uniquestructure and inference paradigm of diffusion models, leaving room formore tailored optimization strategies. We present Bi-VRL, a reinforce-ment learning framework specifically designed for multimodal dLLMs,featuring a bidimensional optimization strategy that enables com-prehensive optimization across both reasoning and diffusion timestepdimensions. For the diffusion timestep dimension, our trajectory-leveloptimization learns a value function over the entire diffusion path, en-abling holistic credit assignment across denoising timesteps. For the rea-soning dimension, our thought-level optimization decomposes outputsinto reasoning steps and delivers targeted rewards to enhance logicalsoundness. This core strategy is augmented by a fine-grained multimodalalignment mechanism that systematically evaluates both the logical cor-rectness of reasoning steps and their grounding in visual evidence. Ourframework is architecture-agnostic, successfully applied to models withboth discrete vision tokens (MMaDA) and continuous vision embed-dings (LLaDA-V). Extensive experiments across challenging multimodalreasoning benchmarks demonstrate that Bi-VRL significantly outper-forms strong supervised fine-tuning baselines, establishing effective post-training methodology for multimodal dLLMs. We will release the codeat https://github.com/Gen-Verse/dLLM-RL.

ECCV 2026

Reference-Free Quality Assessment for Virtual Try-On via Human Feedback

Yuki Hirakawa, Takashi Wada, Ryotaro Shimizu, Takuya Furusawa, Yuki Saito, Ryosuke Araki, Tianwei Chen, Fan Mo, Yoshimitsu Aoki

As virtual try-on (VTON) systems become increasingly im-portant in fashion e-commerce, there is a growing need for reliable reference-free evaluation methods, since ground-truth images of the same personwearing the target garment are typically unavailable in real-world scenar-ios. To address this challenge, we propose VTON-IQA, a reference-freeframework for human-aligned image quality assessment without requir-ing ground-truth images. To model human perceptual judgments, we con-struct VTON-QBench, a large-scale human-annotated benchmark com-prising 62,688 try-on images generated by 14 representative VTON mod-els and 431,800 quality annotations collected from 13,838 qualified anno-tators. To the best of our knowledge, this is the largest dataset to date forhuman subjective evaluation in VTON. Extensive experiments show thatVTON-IQA achieves reliable human-aligned image quality assessment.Moreover, we conduct a comprehensive benchmark evaluation of 14 rep-resentative VTON models using VTON-IQA. The dataset and sourcecode are released at https://github.com/litelightlite/VTON-IQA.

ECCV 2026

ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Reconstruction

Chaojun Ni, Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Wenkang Qin, Chen Xinze, Guanghong Jia, Guan Huang, Wenjun Mei

Reinforcement learning for end-to-end autonomous drivingin closed-loop simulations is gaining increasing attention, yet most sim-ulators differ substantially from real-world conditions, leading to a sig-nificant sim2real gap. Recent methods use scene reconstruction to buildphotorealistic simulators, improving sensor realism but remaining con-strained by the training data distribution, which limits their ability torender novel trajectories and corner cases. To address this, we proposeReconDreamer-RL, a framework that integrates video diffusion priorsinto scene reconstruction for reinforcement learning, enabling more re-alistic and diverse closed-loop autonomous driving training. Specifically,we introduce ReconSimulator, which combines video diffusion priors forappearance modeling with a kinematic model for physical modeling, re-constructing driving scenarios from real-world data and narrowing thesim2real gap. To cover more corner cases, we propose the Dynamic Ad-versary Agent (DAA), which adjusts surrounding vehicles’ trajectoriesrelative to the ego vehicle to generate challenging scenarios such as cut-ins. Finally, we introduce the Cousin Trajectory Generator (CTG) tomitigate the bias of training trajectories toward simple straight-line mo-tion. Experiments show that ReconDreamer-RL improves end-to-end au-tonomous driving training and outperforms imitation learning methodswith a 5× reduction in Collision Ratio.

ECCV 2026

ReAL: Reference-to-Image (R2I) Aware Latent Diffusion for Image Super-Resolution

Byeonghun Lee, Hyunmin Cho, Sunghoon Im, Kyong Hwan Jin

Recent di_x001B_usion-based super-resolution (SR) methods oftenemploy large text-to-image (T2I) backbones as generic priors. While thisyields strong perceptual quality, restoration performance can becomeoverdependent on text-driven semantics. T2I pipelines typically rely onclassi_x001C_er-free guidance (CFG), doubling the number of function evalu-ations at inference time. To address these limitations, we propose theReference-to-Image Aware Latent Di_x001B_usion Model for Image Super-Resolution (ReAL), a purely Reference-to-Image (R2I) model condi-tioned only on the LR input and a retrieved reference. The ReferenceFusion Module encodes the reference once, caches its key_x0015_value tensors,and injects them into the denoising model's self-attention layers at everytimestep, providing strong feature-level guidance for texture and struc-ture recovery. The R2I design substantially reduces dependence on T2Ipriors and improves semantic consistency, achieving state-of-the-art per-formance compared to T2I-based SR baselines.

ECCV 2026

RASA: Disentangled Spatial-Motional Priors for Cross-Identity Character Animation

Zhen Xiao, Zhen Shen, Zhaofan Qiu, Ting Yao, Xueliang Liu, Tao Mei

Cross-identity character animation aims to drive a targetidentity (from a reference image) to follow the motion of a source char-acter (from a driving video). The core challenge lies in the inherent en-tanglement of two essential capabilities: cross-identity spatial mapping-aligning position, scale, and skeletal proportions between the referenceand the driving pose-and subsequent motion control-refining joint articu-lation, volumetric consistency, and view coherence during generation. Inthis paper, we introduce Reference-Aware Structural Alignment(RASA), a novel framework that systematically disentangles spatialmapping from motion control by injecting structured priors into a Diffu-sion Transformer (DiT). Our approach operates in two complementarystages. First, a Spatial Prior Calibrator (SPC) fuses the referenceidentity with the driving pose to generate an initial noise latent thatis spatially grounded—it ensures the target character is correctly po-sitioned, scaled, and proportionally aligned with the driving skeleton.This resolves cross-identity spatial mismatches at the very start of gen-eration. Second, to achieve identity-agnostic motion control, we proposean Inherent Motional Guider (IMG). Moving beyond appearance-biased 2D keypoints, IMG encodes shape-agnostic SMPL articulationparameters into a semantic motion vector. Injected into the interme-diate layers of the DiT, this vector serves as complementary guidancethat works in tandem with the base pose condition, providing anatomi-cally consistent joint articulation and view-aware volumetric refinement.To rigorously evaluate this challenging task, we curate CIM-Bench, ahigh-quality benchmark with rigorous manual curation. Extensive exper-iments demonstrate that RASA significantly outperforms state-of-the-artmethods in both motion fidelity and visual quality. Our work establishesa new paradigm for cross-identity animation, showing that disentan-gled spatial and motional priors are key to achieving robust and con-sistent character animation. Our project page is at https://hidream-ai.github.io/RASA/.

ECCV 2026

Q-TriM: Question-Guided Tri-Modal Attention for Audio–Visual Question Answering

Sunghun Kim, Seung Baek

Audio-Visual Question Answering (AVQA) extends classical VQA by requiring joint reasoning over video and synchronized audio. However, many AVQA systems rely on deeply stacked layers of selfand cross attention across text, video, and audio. Such sequential stacking may incur loss of information such as subtle inter-modal cues over the layers, causing errors to accumulate across sequential attention layers during the fusion. We introduce Q-TriM which performs multi-modal fusion in a shallow and parallel manner instead of a deep and sequential manner. For Q-TriM, we propose a novel framework for attention operation incorporating video and audio conditioned on text. As a result, we obtain not only standard cross attention outputs but also Tri-Modal Attention representations in which Query, Key, and Value come from distinct modalities. These attention representations are combined in parallel at a single stage, thus avoiding the multi-modal fusion with deep stacks in order to mitigate error accumulation and depth-induced issues. QTriM achieves state-of-the-art performance on three AVQA benchmarks, including substantial gains on MUSIC-AVQA-R, which demonstrates its robustness and out-of-distribution generalization. Code is available at https://github.com/Sunghun95/Q-TriM

ECCV 2026

PARL-VLA: Pruning-Aware On-Policy Reinforcement Learning for Vision-Language-Action Model

Ruiyan Xu, Jiashu Lv, Sixu Lin, Ruixing Jin, Shuliang He, Guiliang Liu

Token pruning has emerged as an essential technique for enabling efficient and resilient inference for foundation models, showing great potential in accelerating the Reinforcement Learning (RL) optimization of Vision–Language–Action (VLA) Models. However, our indepth analysis indicates that directly transferring prior pruning strategies to RL is non-trivial. Inference-time pruning introduces a train–test discrepancy, whereas dynamically adjusting the pruning configuration leads to inconsistent policy rollout and improvement. Therefore, we propose PARL-VLA, a pruning-aware RL framework that enforces explorationexploitation pruning consistency by recording the rollout pruning configuration and replaying it during policy updates, enabling end-to-end co-adaptation between token pruning and policy optimization. PARLVLA further trains across a spectrum of token budgets to learn a single policy that remains reliable under different information budgets at test time. On LIBERO and LIBERO-Plus, PARL-VLA discards 55% of visual tokens, speeds up rollout policy forward by about 1.3× and policy updates by about 1.7×, and improves LIBERO-Plus robustness by up to 6.6 points (3.2 on average) without sacrificing in-distribution success. On RoboTwin2.0, PARL-VLA achieves 46.9% real-robot success over 405 trials, improving over RLinf at 37.0%.

ECCV 2026

Molmo-Point: Better Pointing for VLMs with Grounding Tokens

Christopher Clark, Yue Yang, Jae Sung Park, Zixian Ma, Jieyu Zhang, Rohun Tripathi, Mohammadreza Salehi, Sangho Lee, Ranjay Krishna

Grounding has become a fundamental capability of vision-language models (VLMs). Most existing VLMs point by generating coor-dinates as part of their text output, which requires learning a complicatedcoordinate system and results in a high token count. Instead, we pro-pose a more intuitive pointing mechanism that directly selects the visualtokens that contain the target concept. Our model generates a specialpointing token that cross-attends to the input image or video tokens andselects the appropriate one. To make this model more fine-grained, we fol-low these pointing tokens with an additional special token that selects afine-grained subpatch within the initially selected region, and then a thirdtoken that specifies a location within that subpatch. We further showthat performance improves by generating points sequentially in a consis-tent order, encoding the relative position of the previously selected point,and including a special no-more-points class when selecting visual to-kens. Using this method, we set a new state-of-the-art on image pointing(70.7% on PointBench), set a new state-of-the-art for fully open modelson GUI pointing (61.1% on ScreenSpotPro), substantially improve VLMvideo tracking (62.5 on J &F vs 56.7 for Molmo2 on Molmo2Track), andimprove video pointing (59.1% human preference win rate vs. Molmo2).We additionally show that our method significantly improves learningefficiency and discuss the qualitative differences that emerge with thisdesign change. Our model weights, new datasets, and source code areavailable at https://allenai.org/blog/molmopoint.

ECCV 2026

ModuSeg: Decoupling Object Discovery and Semantic Retrieval for Training-Free Weakly Supervised Segmentation

Qingze He, Fagui Liu, Dengke Zhang, Qingmao Wei, Quan Tang

Weakly supervised semantic segmentation aims to achievepixel-level predictions using image-level labels. Existing methods typi-cally entangle semantic recognition and object localization, which oftenleads models to focus exclusively on sparse discriminative regions. Al-though foundation models show immense potential, many approachesstill follow the tightly coupled optimization paradigm, struggling to ef-fectively alleviate pseudo-label noise and often relying on time-consumingmulti-stage retraining or unstable end-to-end joint optimization. To ad-dress the above challenges, we present ModuSeg, a training-free weaklysupervised semantic segmentation framework centered on explicitly de-coupling object discovery and semantic assignment. Specifically, we in-tegrate a general mask proposer to extract geometric proposals withreliable boundaries, while leveraging semantic foundation models to con-struct an offline feature bank, transforming segmentation into a non-parametric feature retrieval process. Furthermore, we propose seman-tic boundary purification and soft-masked feature aggregation strate-gies to effectively mitigate boundary ambiguity and quantization errors,thereby extracting high-quality category prototypes. Extensive experi-ments demonstrate that the proposed decoupled architecture better pre-serves fine boundaries without parameter fine-tuning and achieves highlycompetitive performance on standard benchmark datasets. Code is avail-able at https://github.com/Autumnair007/ModuSeg.

ECCV 2026

MMControl: Unified Multi-Modal Control for Joint Audio-Video Generation

Liyang Li, Wen Wang, Canyu Zhao, Tianjian Feng, Zhiyue Zhao, Hao Chen, Chunhua Shen

Recent advances in Diffusion Transformers (DiTs) have en-abled high-quality joint audio-video generation, producing videos withsynchronized audio within a single model. However, existing control-lable generation frameworks are typically restricted to video-only con-trol. This restricts comprehensive controllability and often leads to sub-optimal cross-modal alignment. To bridge this gap, we present MM-Control, which enables users to perform Multi-Modal Control in jointaudio-video generation. MMControl introduces a dual-stream conditionalinjection mechanism. It incorporates both visual and acoustic controlsignals—including reference images, reference audio, depth maps, andpose sequences—into a joint generation process. These conditions are in-jected through bypass branches into a joint audio-video Diffusion Trans-former, enabling the model to simultaneously generate identity-consistentvideo and timbre-consistent audio under structural constraints. Further-more, we introduce modality-specific guidance scaling, which allows usersto independently and dynamically adjust the influence strength of eachvisual and acoustic condition at inference time. Extensive experimentsdemonstrate that MMControl achieves fine-grained, composable controlover character identity, voice timbre, body pose, and depth-guided scenestructure in joint audio-video generation.

ECCV 2026

Masked BRep Autoencoder via Hierarchical Graph Transformer

Yifei Li, Kang Wu, Wenming Wu, Xiao-Ming Fu

We introduce a novel self-supervised learning framework thatautomatically learns representations from input computer-aided design(CAD) models for downstream tasks, including part classification, mod-eling segmentation, and machining feature recognition. To train our net-work, we construct a large-scale, unlabeled dataset of boundary repre-sentation (BRep) models. The success of our algorithm relies on two keycomponents. The first is a masked graph autoencoder that reconstructsrandomly masked geometries and attributes of BReps for representa-tion learning to enhance the generalization. The second is a hierarchi-cal graph Transformer architecture that elegantly fuses global and locallearning by a cross-scale mutual attention block to model long-range ge-ometric dependencies and a graph neural network block to aggregatelocal topological information. After training the autoencoder, we replaceits decoder with a task-specific network trained on a small amount oflabeled data for downstream tasks. We conduct experiments on varioustasks and achieve high performance, even with a small amount of labeleddata, demonstrating the practicality and generalizability of our model.Compared to other methods, our model performs significantly better ondownstream tasks with the same amount of training data, particularlywhen the training data is very limited.

ECCV 2026

HybridSim: A Physics–Learning Hybrid Digital Twin for mmWave Human Sensing

Weitao Xiong, Tianyu Liu, Peng Li, Kok Chung Chua, Chean Khim Toa, Pu Wang, Hongfei Xue

High-fidelity simulation of mmWave radar signals for dynamichuman motion is valuable for developing radar-based human sensing mod-els; yet collecting accurately labeled measurements for a specific deploy-ment site remains expensive. We present HybridSim, a physics–learninghybrid simulator that synthesizes mmWave radar signals from dynamichuman meshes under a fixed indoor room configuration, explicitly de-coupling propagation into two components. To parameterize the humansubject, we use a tri-plane representation to extract human features anda Graph Convolutional Network to stabilize optimization and mitigategradient instability. The direct signal path is modeled via an inverse-rendering formulation with a microfacet BRDF to capture primary surfacereflections. In parallel, the indirect path is approximated by combining3D Gaussian Splatting with a virtual-receiver geometry to fit and repro-duce site-specific multipath interference patterns, achieving substantiallylower computational cost than explicit full ray tracing. Experiments ina fixed-room setting show improved agreement with a physically basedreference and consistent gains on downstream radar-based human sensingtasks when using HybridSim for site-specific data augmentation.

ECCV 2026

Long-term Traffic Simulation via Structured Autoregressive Modeling

Lingyu Xiao, Zexin Feng, Xintao Yan

Interactive traffic simulation is a vital world model for au-tonomous driving. A central challenge in long-horizon simulation is mod-eling sustained multi-agent interactions, which is further exacerbatedby dynamic token cardinality as agents continuously enter and exit thescene. In this work, we propose that the solution lies in the synergybetween the architectural inductive biases and statistical priors of large-scale sequence models, e.g., Large Language Models (LLMs). Our prob-ing experiments reveal that the transferability of attention mechanismsand the distributional consistency between motion tokens and naturallanguage enable small-scale, heavily frozen LLMs to rapidly adapt to traf-fic modeling. Building on this insight, we introduce RosettaSim, a unifiedframework that projects scene topology, agent states, and spawning in-tents into a structured autoregressive stream with variable length, achiev-ing both strong short-term accuracy and stable long-horizon simulationfidelity. Furthermore, evaluating extended rollouts presents yet anotherhurdle, as one-to-one agent correspondence inevitably fades over time.To address this, we introduce Retrieval-based Traffic Evaluation (RTE),which retrieves semantically similar real-world scenarios as context-awarereference anchors. Experiments on the Waymo Open Sim Agent Chal-lenge (WOSAC) demonstrate that RosettaSim achieves state-of-the-artperformance in both short- and long-term simulation. Furthermore, RTEexhibits a stronger correlation with standard metrics (r = 0.83) than ex-isting approaches (r = 0.74), indicating improved alignment with long-horizon simulation fidelity.

ECCV 2026

LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement

Tongkun Guan, Haocheng Wang, Wei Shen, Xiaokang Yang

Visual document retrieval requires rapidly locating relevantpages from large multi-modal corpora in response to user queries. Whilerecent methods powered by Multi-modal Large Language Models (MLLMs)show competitive accuracy, they suffer from prohibitive computationalcosts by applying intensive MLLM encoding to every single page. Mean-while, we observe that user queries are typically keyword-anchored, con-taining semantically rich words that are expected to appear directly inthe visible text of relevant pages, offering an efficient cue for quickly nar-rowing down candidate pages. Building on this insight, we propose Light-STAR, an efficient framework that decomposes visual document retrievalinto: 1) LLM-free Visual Selection, which utilizes content-grounded queryencoding to focus on informative words and employs LLM-free visual em-beddings to produce a high-recall candidate set; and 2) Vision-adaptiveSemantic Refinement, which further performs fine-grained semantic match-ing exclusively on these top candidates via adaptive region-wise featurefusion to effectively combine textual and layout cues, optimized through ahardness-aware contrastive objective. Experimental results demonstratethat LightSTAR achieves state-of-the-art retrieval accuracy while reduc-ing end-to-end latency by several-fold, offering a highly practical solutionto the accuracy-efficiency trade-off in visual document retrieval. Code isavailable at https://github.com/bokufa/LightSTAR.

ECCV 2026

MOOZY: A Patient-First Foundation Model for Computational Pathology

Yousef Hassan, Vincent Quoc-Huy Trinh, Christopher Pal, Mahdi S. Hosseini

Computational pathology needs whole-slide image (WSI) foun-dation models that transfer across diverse clinical tasks, yet current ap-proaches remain largely slide-centric, often depend on private data andexpensive paired-report supervision, and do not explicitly model relation-ships among multiple slides from the same patient. We present MOOZY,a patient-first pathology foundation model in which the patient case, notthe individual slide, is the core unit of representation. MOOZY explicitlymodels dependencies across all slides from the same patient via a casetransformer during pretraining, combining multi-stage self-supervisionwith scaled low-cost task supervision. In Stage 1, we pretrain a vision-only slide encoder on 77,134 public slide feature grids using masked self-distillation. In Stage 2, we align these representations with clinical se-mantics using a case transformer and multi-task supervision over 333tasks from 56 public datasets, including 205 classification and 128 sur-vival tasks across four endpoints. Across sixteen held-out tasks, MOOZYimproves macro weighted F1, balanced accuracy, and macro weightedROC-AUC relative to PRISM by +4.19%, +7.93%, and +6.95%, re-spectively. MOOZY is also parameter efficient with 85.77M parameters,14× smaller than GigaPath. These results suggest that patient-level pre-training yields transferable embeddings, providing a path toward scalablepatient-first histopathology foundation models.

ECCV 2026

Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks

Yaohua Liu, Yifan guo, Jiaxin Gao

Transfer-based adversarial attacks craft adversarial examplesusing surrogate models to mislead black-box victim models. Beyond per-turbation generation, transferability is fundamentally governed by thecoupling of initialization, surrogate adaptation, and gradient dynam-ics. We revisit this challenge from a Bilevel-Minimax perspective andinstantiate it in BMAT (Bilevel-Minimax Adversarial Transfer). Thebilevel formulation captures the dependency between initialization andperturbation, while the inner minimax problem promotes surrogate ro-bustness for cross-architecture generalization. Algorithmically, we designan integrated bottom-up solver that combines a Soft Weight Modulatorand an Implicit Gradient Approximator for ternary coupling interaction.We further provide theoretical insights into the optimization dynamicsof the proposed bilevel-minimax framework. Extensive experiments onclassification and segmentation benchmarks show that BMAT surpasses10+ strong baselines across 30+ victim models, improving both intra-and cross-architecture transfer, and yielding up to 2× mIoU reduction.Code is available at https://github.com/callous-youth/BMAT.

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