ECCV 2026 论文列表

Vision-Language-Action (VLA) models often fail to performthe same learned tasks under environmental shifts, such as changes incamera pose and shifts to a different but similar robot (e.g., from Pandato UR5e). Adapting these models to the shifted environment (i.e., targetdomain) often requires training on multiple demonstrations for each task,which are costly to collect. To reduce the burden of data curation andtraining, we propose an analogy-based method that adapts VLA modelsunder environmental shifts through weight vector arithmetic with domain-specific information addition, named Domain ARiThmetic (DART). Un-like prior approaches, DART requires collecting only a single demonstra-tion, enabling efficient adaptation. To accurately isolate domain-specificinformation for addition, DART performs subspace alignment between sin-gular components in weight vectors to filter out noisy components. In bothsimulated and real-world experiments, DART outperforms existing VLAadaptation methods in one-shot scenarios across diverse visual and embod-iment shifts. Code is available at https://github.com/snumprlab/dart.

Zero-Shot Industrial Anomaly Detection (IAD) is criticalfor rapid deployment in agile manufacturing. We investigate a highlypractical Batch Zero-Shot IAD setting, strictly constrained by smallintra-batch references and common pose variations. Directly applyingexisting mutual-scoring methods fails because training-free features lackgeneralized anomaly semantics and spatial robustness, resulting in am-biguous scoring distances and an “anomaly-to-anomaly shortcut” duringmatching, which can lead to missed detections. To address this, we pro-pose DeMuS (Decoupled Mutual Scoring), a novel two-stage learningframework. By training two lightweight adapters via a two-stage aux-iliary process, our method extracts highly discriminative anomaly se-mantics for accurate scoring (how-to-score), while explicitly decouplingrobust part-level correspondence learning (where-to-match) to guaranteereliable physical alignment. This synergistic mechanism effectively pre-vents semantic confusion under unaligned inputs. Extensive experimentson MVTec AD, VisA, and Real-IAD demonstrate DeMuS achieves state-of-the-art performance and superior robustness against pose shifts, main-taining efficient inference for online inspection. Our code is available athttps://github.com/evoLonation/DeMuS.

In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abnormalities, predicting likelihoods of abnormality, or, with downstream adaptation, generating radiological reports. While the methodology holds promise, three challenges limit the capabilities of current 3D VLEs: data scarcity due to privacy concerns, high computational costs resulting from the volumetric nature of the images, and a domain shift between the long reports used for training and the short prompts used during inference for, e.g., zeroshot classification. As a consequence, natural-image VLE recipes do not directly transfer to 3D medical imaging. In this paper, we overcome these challenges by injecting additional supervision via a report generation objective and combining vision-language with vision-only pre-training, allowing us to leverage both image-only and paired image-text 3D datasets. Further, we propose a novel loss that addresses the domain shift between long reports and short textual prompts. Through these additional objectives, paired with best practices of the 3D medical imaging domain, we develop the Comprehensive Language–Image Pre-training (COLIPRI) encoder family. Our COLIPRI encoders achieve state-of-the-art performance in report generation, semantic segmentation, classification probing, and zero-shot classification. The model weights and inference code are freely available at https: //huggingface.co/microsoft/colipri.

The connoisseurship of antique Chinese porcelain demands extensive historical expertise, material understanding, and aesthetic sensitivity, making it difficult for non-specialists to engage. To democratize cultural-heritage understanding and assist expert connoisseurship, we introduce CiQi-Agent—a domain-specific Porcelain Connoisseurship Agent for intelligent analysis of antique Chinese porcelain. CiQi-Agent supports multi-image porcelain inputs and enables vision tool invocation and multimodal retrieval-augmented generation, performing finegrained connoisseurship analysis across six attributes: dynasty, reign period, kiln site, glaze color, decorative motif, and vessel shape. Beyond attribute classification, it captures subtle visual details, retrieves relevant domain knowledge, and integrates visual and textual evidence to produce coherent, explainable connoisseurship descriptions. To achieve this capability, we construct a large-scale, expert-annotated dataset CiQi-VQA, comprising 29,596 porcelain specimens, 51,553 images, and 557,940 visual question–answering pairs, and further establish a comprehensive benchmark CiQi-Bench aligned with the previously mentioned six attributes. CiQi-Agent is trained through supervised fine-tuning, reinforcement learning, and a tool-augmented reasoning framework that integrates two categories of tools: a vision tool and multimodal retrieval tools. Experimental results show that CiQi-Agent (7B) outperforms all competitive openand closed-source models across all six attributes on CiQi-Bench, achieving on average 12.2% higher accuracy than GPT-5.

Recent advancements in deep learning have actively addressedcomplex challenges within the Computer-Aided Design (CAD) domain.However, most existing approaches rely on task-specific models requir-ing structural modifications for new tasks, and they predominantly focuson point clouds or images rather than the industry-standard BoundaryRepresentation (B-rep) format. To address these limitations, we proposeBrepCoder, a unified Multimodal Large Language Model (MLLM) thatperforms diverse CAD tasks from B-rep inputs. By leveraging the codegeneration capabilities of Large Language Models (LLMs), we convertCAD modeling sequences into Python-like code and align them withB-rep. We then adopt a two-stage training strategy: First, pre-trainingon reverse engineering to learn geometric features and design logic. Sec-ond, effectively extending the model to various downstream tasks such ascompletion, error correction, and CAD-QA. Consequently, by interpret-ing B-rep as structural code, BrepCoder achieves superior generalizationacross diverse tasks, demonstrating its potential as a general-purposeCAD agent.

Text-driven Referring Video Object Segmentation (RVOS)aims to locate and segment target objects in videos given natural lan-guage. However, existing models are typically trained on 2D image orvideo datasets with naive segmentation losses, which overlooks the geo-metric consistency across frames and leads to weak spatial understand-ing. In this paper, we propose Geometry-enhanced Language-guidedVideo segmentation (GeoLaV), a two-stage framework that distills 3Dgeometric knowledge from images to enhance text-driven video segmenta-tion. In the first stage, we perform monocular geometry pretraining withmonocular novel-view synthesis, enabling the model to acquire geometry-consistent visual representations via spatial alignment on large-scale single-image datasets. In the second stage, we introduce geometry-aware distil-lation and fine-tune the model on video segmentation datasets, transfer-ring 3D structural knowledge from a general 3D prior model. This processreinforces 3D awareness and improves both spatiotemporal coherence andlanguage grounding in segmentation. Extensive experiments show thatour method using only image segmentation data already provides notablezero-shot generalization in RVOS. When combined with geometry-awaredistillation for fine-tuning on videos, our method achieves state-of-the-artperformance across multiple RVOS benchmarks. The code is available athttps://github.com/Tony1882880/GeoLaV.

Self-supervised learning (SSL) has significantly advanced 3Dmedical image analysis. However, existing pretraining methods typicallytreat multi-phase scans as isolated samples, neglecting the rich anatom-ical correspondence inherent in paired acquisitions. Clinically, contrast-enhanced CT (CECT) offers a structure-amplified view of the exactanatomy captured in a paired non-contrast CT (NCCT), yet the drasticintensity shifts caused by contrast agents make direct voxel-wise align-ment challenging. To harness this naturally occurring supervisory signalwhile decoupling structural topology from phase-specific appearance, wepropose CAST (Cross-phase Alignment of Structure and Topology),a novel paired pretraining framework. CAST employs a 3D CNN archi-tecture to explicitly align NCCT representations with CECT targets.Moving beyond conventional reconstruction, we introduce two feature-level constraints: (1) a Spectral Consistency module that utilizes3D wavelet decomposition to align frequency-aware boundaries whilesuppressing contrast-induced noise; (2) a Geometry-Aware Topo-logical Consistency module that preserves local relational graphsamong salient anatomical keypoints via dynamic top-hat sampling. Tosupport this, we construct a large-scale dataset comprising 13,850 pairedvolumetric CT scans. Extensive evaluations on public benchmarks andreal-world clinical cohorts demonstrate that CAST achieves state-of-the-art performance. Notably, our method delivers substantial gains inboundary-sensitive segmentation tasks and low-data regimes, proving itsefficacy in learning robust, phase-invariant anatomical representationsfor both NCCT and CECT downstream applications. Code is availableat https://github.com/alibaba-damo-academy/CAST.

We address the inverse problem of blind 3D scene recon-struction from extremely motion-blurred images, a scenario where tradi-tional Structure-from-Motion (SfM) pipelines fail. Existing approachestypically circumvent this bottleneck by relying on impractical sharp-imagesupervision. In this work, we introduce PRISM3D, a unified frameworkenabling robust reconstruction directly from severely degraded inputs.To overcome the lack of a reliable starting point, we propose a RobustInitialization strategy utilizing deep dense tracking method (VGGSfM)to recover global topology where feature matching fails. To the best of ourknowledge, we are the first to effectively leverage this paradigm to boot-strap 3D Gaussian Splatting from extreme motion blur. However, whilerobust, this initialization yields sparse and noisy geometry that causes de-terministic optimization to diverge. To resolve this, we propose a coupledsolution driven by probability and physics: we adopt a probabilisticformulation for geometric densification via Markov Chain Monte Carlo(MCMC) to robustly populate the sparse priors, while simultaneouslymodeling physical image formation via continuous Bézier Trajectories.Furthermore, while PRISM3D establishes a highly robust standalonepipeline, the availability of complementary event streams offers an op-portunity to push the reconstruction fidelity further. To exploit this, weintroduce PRISM3D-E, a multi-modal (RGB + Events) extension thatseamlessly integrates high-temporal-resolution events as structural priorsto maximize geometric recovery. Because existing datasets lack pairedevent streams under such severe degradation, we concurrently contributethe PRISM3D-E Benchmark to facilitate rigorous evaluation. Exten-sive experiments demonstrate that both our standalone RGB frameworkand its multi-modal extension establish new state-of-the-art performance.To facilitate future research in extreme motion deblurring, our projectpage, featuring extensive qualitative comparisons, video results, and theupcoming releases of our codebase and benchmark dataset, is availableat https://gopirajumatta.github.io/PRISM3D/.

Vision–language models (VLMs) achieve strong performanceon general-domain tasks but can degrade when visual distributions, ter-minology, and reasoning requirements diverge from general-domain pre-training assumptions, including in scientific imaging. We study this chal-lenge in materials-science transmission electron microscopy (TEM), adata-scarce setting that requires nanoscale visual grounding, modality-specific interpretation, and specialized materials-science knowledge. Weintroduce ATOMIC (Assistant for TEM Oriented Multimodal Instruc-tion and Conversation), a TEM-oriented vision–language model adaptedusing peer-reviewed, literature-derived data without manual annotation.Our pipeline extracts figure–caption pairs, isolates TEM subfigures, andgenerates multimodal instruction data via GPT-based instruction gen-eration. The training strategy separates two complementary supervisionsignals: VisionGround, which enforces image-only grounding, and Do-mainContext, which injects caption-conditioned scientific context. Theircombination, Blend, yields consistent improvements across perception-and knowledge-intensive queries. ATOMIC-7B-Blend achieves 75.2% ac-curacy on TEM-MCQ and 37.5% recall-oriented Answer Word Cover-age (AWC) on TEM-VQA, narrowing the gap to GPT-4o on AWC(40.4%) while enabling local deployment. On the TEM subset of MatCha,an independent external benchmark, ATOMIC-7B-Blend+FT outper-forms LLaVA-v1.5-7B+FT by 18.1 percentage points under identicalfine-tuning conditions. We release TEM-MCQ/TEM-VQA annotations,article URLs, figure IDs, normalized crop coordinates for sub-figure re-construction, the data-curation pipeline, and model weights to supportreproducibility within copyright constraints. These results show thatstructured literature-derived supervision offers an effective data-centricstrategy for adapting VLMs to materials-science TEM and may provide areproducible foundation for future studies of VLM adaptation in relateddata-scarce scientific-imaging domains.

Accurate, dense depth estimation is crucial for robotic per-ception, but commodity sensors often yield sparse or incomplete measure-ments due to hardware limitations. Existing RGBD-fused depth comple-tion methods learn priors jointly conditioned on training RGB distri-bution and specific depth patterns, limiting domain generalization androbustness to various depth patterns. Recent efforts leverage monoculardepth estimation (MDE) models to introduce domain-general geomet-ric priors, but current two-stage integration strategies relying on explicitrelative-to-metric alignment incur additional computation and introducestructured distortions. To this end, we present Any2Full, a one-stage,domain-general, and pattern-agnostic framework that reformulates com-pletion as a scale-prompting adaptation of a pretrained MDE model.To address varying depth sparsity levels and irregular spatial distribu-tions, we design a Scale-Aware Prompt Encoder. It distills scale cuesfrom sparse inputs into unified scale prompts, guiding the MDE modeltoward globally scale-consistent predictions while preserving its geomet-ric priors. Extensive experiments demonstrate that Any2Full achievessuperior robustness and efficiency. It outperforms OMNI-DC by 32.2%in average AbsREL and delivers a 1.4× speedup over PriorDA with thesame MDE backbone, establishing a new paradigm for universal depthcompletion. Codes and checkpoints are available at https://github.com/zhiyuandaily/Any2Full.

Post-training via Supervised Fine-Tuning (SFT) and Rein-forcement Learning (RL) is crucial for enhancing reasoning in Multi-modal Large Language Models (MLLMs), yet existing paradigms of-ten reach a performance bottleneck due to the limitations of staticdata. While current methods leverage self-reflection or self-evolution topush these boundaries, they still suffer from cognitive drift and hallu-cinated reasoning paths caused by low-quality synthetic data. To ad-dress these challenges, we propose Anchor E volution (AnE), a newparadigm that integrates truth-anchored data curation and model evo-lution, achieving faithful and steady performance gains at the reason-ing frontier. Specifically, we propose Truth Anchor Expansion, whichpinpoints the model failing frontier via trajectory rollouts and lever-ages ground-truth databases to retrieve high-fidelity anchors for faithfuldata curation. Subsequently, we introduce the Scaffold-Stripping Mech-anism to internalize reasoning capabilities. This mechanism first an-chors reasoning paths via scaffold-augmented supervision to mitigatethe learning complexity and distribution drift of direct SFT on rawdata, then leverages RL to strip the scaffold template, thereby effec-tively transitioning the reasoning paths into intrinsic model capabilities.Experimental results on multimodal reasoning benchmarks show thatour method substantially advances the model performance frontier, im-proving the base model by 10.3% across eight multimodal benchmarksand achieving state-of-the-art results. The code will be publicly availableat https://github.com/wangzehao0704/AnE.

Adversarial attacks pose a challenge to the reliability of deeplearning models, motivating effective detection methods. Existing tech-niques often rely on attack-specific assumptions, access to adversarialsamples, or knowledge of the underlying classifier (white-box). We pro-pose A4 D (Attack- and Architecture-Agnostic Adversarial Detector), acompletely black-box, zero-shot adversarial attack detection frameworkthat utilizes prompt-based similarity scores derived from CLIP. To thebest of our knowledge this is the first attempt to utilize CLIP for sucha task. The method is based on two key observations: (i) CLIP is sen-sitive even to small imperceptible non-semantic perturbations; (ii) Theshift in CLIP embedding space is not arbitrary and can be used as arobust attack indicator. Experiments across multiple attacks, datasetsand classifiers validate that A4 D achieves SOTA detection results in theattack-agnostic and classifier-agnostic setting.

Intraoperative 2D/3D registration, which aligns live X-rayimages with preoperative volumes, is essential for image-guided interven-tions. Previous regression-based methods suffer from limited generaliza-tion, thus requiring time-consuming patient-specific retraining. Inspiredby recent geometry foundation models such as DUSt3R, we proposeXPos3R, a generalizable pose regression method that eliminates preop-erative preparation. Unlike existing geometry models designed for homo-geneous inputs, XPos3R extends this paradigm to multi-modal inputs,namely 2D X-rays and 3D volumes. Specifically, we introduce an asym-metric encoder–decoder architecture that improves cross-modal featurealignment while maintaining computational efficiency. To scale trainingunder limited medical data, we adopt an anatomy-specific data cura-tion strategy and construct million-scale synthetic datasets. Evaluatedon real-world benchmarks, a single pretrained XPos3R surpasses patient-specific methods in both accuracy and robustness. With test-time opti-mization completed in seconds, it further reduces the 3D error to <4 mmand the reprojection error to <1 mm. The strong generalization, accu-racy, and efficiency of XPos3R highlight its clinical potential, while itsasymmetric framework may inspire broader cross-modal vision geometrytasks.

Population-level morphometric measurements are central toecological and evolutionary studies, but traditionally require controlledimaging or physical specimen handling, limiting their scalability. Wepresent WildProp, a framework for estimating wildlife body-proportiondistributions directly from large-scale, unconstrained image repositories.We cast morphometric estimation as a retrieval-driven correspondenceproblem: given a single user-annotated canonical image, WildProp per-forms pose-aware retrieval using foundation model features, transferspart endpoints via dense patch-level matching, filters predictions us-ing geometric consistency, and aggregates measurements across retrievedimages to estimate length-ratio distributions. Unlike supervised keypointpipelines, our approach adapts to arbitrary species and user-defined bodyparts without per-species training. Evaluations on three large morpho-metric datasets spanning birds and amphibians show median relative er-rors of 10–20%. We further demonstrate broad applicability through casestudies measuring proportions across diverse taxa, including birds, frogs,insects, and flowers. Ablations show that pose-aware retrieval is criticalfor stable estimation, while robust aggregation mitigates keypoint andpose noise. Our results suggest that carefully filtered 2D correspondencesover web-scale imagery can provide scalable morphometric proxies forcomparative analyses across taxa, geography, and seasonality.

Accurate 3D tumor segmentation is vital for clinical decision-making, but pixel-wise annotation is expensive. Mixed-supervised learn-ing combines a small set of mask labels with abundant box labels, yetreconstruction-kernel heterogeneity creates large domain gaps. We pro-pose WCC4MS, a jointly optimized lesion-centered framework that mod-els kernel-related frequency shifts via a 3D discrete wavelet transform.The input volume is decomposed into low- and high-frequency com-ponents, and the scaled high-frequency coefficients generate syntheticsmooth and sharp variants. A dual-branch architecture learns from maskand box annotations with task-specific decoders, while a cross-branchKL-divergence constraint enforces semantic consistency. An auxiliarydomain-aware head encourages quality-sensitive features and improvesrobustness to domain shift. At inference, only the segmentation branch isused on candidate-centered patches; the box branch is used only duringtraining to exploit weak annotations. Experiments across three mask-annotated CT datasets and one box-only dataset show consistent gainsin segmentation accuracy and cross-domain generalization compared toprior mixed- and weakly supervised methods.

Video generation models produce visually coherent content but struggle with tasks requiring spatial reasoning and multi-step planning. Reinforcement learning (RL) offers a path to improve generalization, but its effectiveness in video reasoning hinges on reward design—a challenge that has received little systematic study.WeinvestigatethisproblembyadaptingGroupRelativePolicyOptimization (GRPO)toflow-basedvideomodelsandtrainingthemonmaze-solvingandrobotic navigationtasks.Wefirstshowthatmultimodalrewardmodelsfailcatastrophically in this setting. To address this, we design verifiable reward functions grounded in objective task metrics. For structured game environments, we introduce a multicomponent trajectory reward that is verifiable against ground-truth optimal paths. For robotic navigation, where ground truth is given only as a reference rollout, we propose an embedding-level reference-anchored reward. Our experiments show that RL fine-tuning with verifiable rewards improves generalization. For example, on complex 3D mazes, our model improves exact match accuracy by 29.1% over the SFT baseline, and on trap-avoidance tasks by 51.4%. Our systematic reward analysis reveals that verifiable rewards are critical for stable training, while multimodal reward models could lead to degenerate solutions. These findings establish verifiable reward design as a key enabler for robust video reasoning.

Communities are fundamental spatial units that shape urbanform and social life. Whether a residential compound is spatially openor enclosed affects mobility, access to public services, and equity, yetstudies of Chinese fengbi xiaoqu remain largely qualitative or small-scale,limiting reproducible city-scale analysis. We address this gap by introduc-ing GBA-GCs, a metropolitan-scale multimodal benchmark for locallygrounded gated/open community recognition in China’s Greater BayArea, covering 37,444 residential compounds with aligned boundary poly-gons, high-resolution satellite imagery, Chinese metadata, and structuredattributes, together with expert-verified labels, inter-annotator reliability,and official evaluation splits. Built on this benchmark, we present Multi-modal Classifier for Gated Community (MCGC), a vision-centricmultimodal framework based on DINOv3-SAT that fuses imagery, text,and structured cues via modality-aware cross-attention and adaptivegating to mitigate modality imbalance. MCGC consistently outperformsstrong unimodal and multimodal baselines. Finally, we apply the validatedmodel to metropolitan-scale mapping and report equity-oriented findingsincluding spatial clustering of GCs, privatized green space, and reducedpedestrian connectivity. The benchmark, code, and release documentationare available at https://github.com/MinweiZhao/GBA-GCs.

Simulation is indispensable for autonomous driving, yet thepronounced visual Sim2Real gap severely limits the cross-domain gen-eralization of perception models. To bridge this gap, we propose Cross-Domain Control Transfer (CDCT), a diffusion-based Sim2Real frame-work for translating synthetic images into realistic counterparts withoutpaired supervision. CDCT features a novel cross-domain score composi-tion mechanism that injects domain-agnostic geometry guidance, derivedfrom synthetic-domain score differences, into a real-world appearanceprior. This ensures strict geometric and semantic consistency withoutpaired real-world spatial conditions. However, naively incorporating mul-tiple geometric conditions often incurs a prohibitive parameter overhead.To address this, our Lightweight Multi-Condition Adapter (LMCA) mod-ule processes diverse rendering buffers simultaneously, eliminating thecomputational redundancy of standard multi-branch architectures. Ex-tensive experiments on the CARLA-to-Cityscapes benchmark demon-strate that CDCT yields substantial zero-shot performance gains ondownstream perception tasks. Crucially, joint training with our generateddata surpasses the performance of real-world-only training, showcasingits immense potential for autonomous driving.

We introduce UEval, a benchmark to evaluate unified mod-els, i.e., models capable of generating both images and text. UEval com-prises 1,000 expert-curated questions that require both images and textin the model output, sourced from 8 real-world tasks. Our curated ques-tions cover a wide range of reasoning types, from step-by-step guides totextbook explanations. Evaluating open-ended multimodal generation isnon-trivial, as simple LLM-as-a-judge methods can miss the subtleties.Unlike prior methods relying on multimodal Large Language Models(MLLMs) to rate image quality or text accuracy, we design a rubric-basedscoring system in UEval. For each question, reference images and text an-swers are provided to an MLLM to generate an initial rubric, consisting ofmultiple evaluation criteria, and human experts then refine and validatethese rubrics. In total, UEval contains 10,417 validated rubric criteria,enabling fine-grained automatic scoring. UEval is challenging for currentunified models: GPT-5-Thinking scores only 66.4 out of 100, while thebest open-source model reaches merely 49.1. We observe that reasoningmodels often outperform non-reasoning ones, and transferring reasoningtraces from a reasoning model to a non-reasoning model narrows thegap. This suggests that reasoning may be important for tasks requiringcomplex multimodal understanding and generation. UEval is availableat https://huggingface.co/datasets/zlab-princeton/UEval