paperlist.ai

AI conference papers

AI conference paper library

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

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation

Hyun-Kurl Jang, Jihun Kim, Hyeokjun Kweon, Kuk-Jin Yoon

Continual Test-Time Adaptation (CTTA) aims to maintainmodel performance under evolving target domains by adapting onlinewithout labeled data. However, practical deployments often cannot re-tain the source dataset due to privacy or licensing constraints, and purelysource-free CTTA methods tend to become unstable under long-termdistribution shift, suffering from compounding self-training errors andcatastrophic forgetting. We introduce DO-ALL (Distill Once, AdaptLife-Long), a plug-and-play framework that revisits source informationin a compact and privacy-conscious form via Dataset Distillation (DD).Before deployment, DO-ALL performs DD to produce a small set of syn-thetic distilled anchors that summarize the source distribution. Duringadaptation, each target sample is matched with its most semanticallyaligned anchor, which provides a stable reference for various CTTA viasource replay, representation alignment, and manifold-smoothing regu-larization. DO-ALL can be seamlessly integrated into existing CTTA al-gorithms, consistently improving long-term robustness across CIFAR100-C, ImageNet-C, and the CCC benchmark. This demonstrates the po-tential of leveraging DD to enable stable and continuous adaptationwithout retaining raw source data. The code is available at https://github.com/blue-531/DOALL.

ECCV 2026

Contrastive-Guided Self-Supervised Latent Visual Reasoning for Hallucination Mitigation

Aoxue Dai, Ningning Wang

Recent research on Large Vision-Language Models (LVLMs)has explored visual chain-of-thought (CoT) mechanisms to enhance visual-centric reasoning, effectively mitigating the hallucination issues that plaguecurrent models. However, existing paradigms typically rely on externalvisual tools and multi-turn interactions. While recent latent visual rea-soning approaches achieve tool-free, single-turn inference by supervisingintermediate representations with auxiliary images, they incur signifi-cant annotation costs. To address this, we propose Contrastive-GuidedSelf-Supervised Latent Visual Reasoning (CoLVR), a novel framework forself-supervised training of visual latent tokens in an annotation-free man-ner. Specifically, we leverage visual contrastive principles to identify keyvisual regions and employ Reinforcement Learning (RL) to align the gen-erated latent tokens with these salient regions. Extensive experimentson hallucination benchmarks demonstrate that our method significantlyoutperforms mainstream de-hallucination approaches and even surpassesfully supervised visual reasoning baselines.

ECCV 2026

CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts

Lianyu Hu, Shengqian Qin, Zeqin Liao, Qing Guo, Liang Wan, Wei Feng, Yang Liu

Chain-of-thought (CoT) reasoning has enabled multi-modallarge language models (MLLMs) to tackle complex visual reasoning tasksby generating explicit intermediate reasoning steps in natural language.However, this text-based reasoning paradigm is inherently slow at infer-ence time with even thousands of tokens and fundamentally constrainedby the expressiveness of natural language. In this paper, we proposeCoLT (Chain of Latent Thoughts), a novel framework that teachesmulti-modal models to reason through a chain of latent thought repre-sentations instead of verbose text tokens, which can perform thinkingwith as few as 3 steps. Naively forcing the model to think with latentstates easily produces meaningless semantics and makes training unsta-ble. To effectively regulate the latent reasoning process, we introduce alightweight external decoder that provides step-level supervision for eachlatent reasoning step in two complementary directions: a forward modethat decodes latent thoughts into the textual reasoning of the next step,and a backward mode that aligns decoder hidden states with the model’slatent thoughts given preceding textual context. We further incorporateinternal supervision that encourages coherent step-by-step latent transi-tions. The decoder and internal supervision are removed during inferenceto maintain high efficiency of latent reasoning. Extensive experiments oneight benchmarks demonstrate that CoLT not only outperforms existinglatent reasoning methods such as CODI and SIM-CoT, but also surpasseslatent visual reasoning approaches that rely on auxiliary images withcostly annotation requirements. Compared to text CoT methods, CoLTcan notably reduce the inference time by 10.1× and text decoding timeby 22.6×. Code is released at https://github.com/hulianyuyy/CoLT.

ECCV 2026

CGCC: Towards Generalizable Clothes-Changing Person Re-Identification

Yizhi Wu, Fangyi Liu, Wei Yu, Mang Ye

Clothes-Changing Person Re-Identification (CC-ReID) aimsto match pedestrians across non-overlapping camera views despite vari-ations in clothes. However, current research is severely constrained bytwo main shortcomings: existing datasets lack comprehensive diversityand semantic annotations, and current methods fail to globally modeland eliminate identity-irrelevant interfering factors, severely limiting CC-ReID generalization in real-world scenarios. To address these limitations,we propose a unified framework for generalizable CC-ReID. First, weintroduce CGCC, a Comprehensive dataset for Generalizable CC-ReIDfeaturing extensive diversity and fine-grained semantic annotations, com-prising 4,101 identities and 217,248 images. Additionally, we introduceText-Guided Identity Refinement (TGIR), a novel framework that uti-lizes text descriptions to construct an identity-irrelevant subspace, andemploys Singular Value Decomposition (SVD) to holistically extract andeliminate environmental and clothes interference. Together, CGCC andTGIR form a mutually reinforcing closed loop, where the diverse dataand structured annotations of the former provide the essential founda-tion for the semantic disentanglement of the latter. Extensive exper-iments demonstrate that the comprehensive diversity of CGCC effec-tively improves model generalizability, and TGIR precisely extracts ro-bust and generalizable identity representations. Dataset is available athttps://github.com/zhi-time/CGCC.

ECCV 2026

CascadeProto: Cascaded Cross-Modal Prototype Purification via Entropy-Aware Learning for Few-Shot 3D Point Cloud Segmentation

Changshuo Wang, Weijun Li, Fan Mo, Zhonghang LIU, Shuting He, Prayag Tiwari, Dimitrios Kanoulas

Few-shot 3D point cloud semantic segmentation aims to rec-ognize novel object categories with limited labeled examples. Existingmethods typically rely on point-based prototypes extracted from sup-port sets, but such prototypes inevitably contain background noise andlack semantic richness, leading to suboptimal segmentation quality. In-spired by chemical distillation that iteratively removes impurities, wepropose CascadeProto, which employs multi-step refinement to distillhigh-quality prototypes from noisy initializations. At its core lies theEntropy-aware Prototype Purification Module (EPPM), which lever-ages information-theoretic principles to suppress high-entropy backgroundfeatures while amplifying low-entropy foreground information. To fur-ther enrich prototype representations, we introduce Learnable ModalityAdapters (LMA) that independently align each of three CLIP modal-ities — text, audio, and image — with point cloud features throughforeground-background decoupled distribution matching, enabling flexi-ble single-modality semantic enrichment that bridges the 2D-3D domaingap. Furthermore, we propose an Attention-based Dynamic RoutingMechanism (ADRM) that adaptively aggregates predictions from mul-tiple cascade stages, allowing simple regions to benefit from early-stageoutputs while complex regions leverage deeper purification. Extensiveexperiments on S3DIS and ScanNet benchmarks demonstrate the effec-tiveness and superiority of CascadeProto across all three modalities overstate-of-the-art methods. The code is available at https://github.com/changshuowang/CascadeProto.

ECCV 2026

Boosting 3D Foundation Models with Featureless Pose Optimization

Mattia D&amp, #x27, Urso, Christian Sormann, Mattia Rossi, Friedrich Fraundorfer

We introduce Edge-based Pose Optimization (EPO),a trackless geometric optimization framework specifically designed toboost the Structure-from-Motion reconstructions generated by 3D Foun-dation Models. These models achieve rapid inference by bypassing thetime-consuming feature extraction and matching stages of traditionalpipelines, where explicit correspondences between each 3D point andmultiple images, referred to as tracks, are established. However, their ge-ometric accuracy currently falls short of traditional pipelines. While thiscan be addressed in a post-processing step via Bundle Adjustment-likerefinement, doing so requires extracting feature tracks, thus defeatingthe original speed advantage. Instead, our fully differentiable frameworkuses edge map alignment as a proxy for geometric optimization, avoid-ing feature extraction and track construction entirely. Through exten-sive evaluation across multiple datasets and tasks, we demonstrate thatEPO matches or outperforms Bundle Adjustment-like methods while re-quiring significantly lower runtime and memory. Notably, its reducedmemory footprint makes EPO suitable for consumer-grade hardware,where competing refinement methods cannot run. Code is available athttps://github.com/mattiadurso/EPO.

ECCV 2026

Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization

Liyao Wang, Ruipu Wu, Haojun Xu, Lei Shi, Linjiang Huang, Si Liu

Cross-view object geo-localization (CVOGL) aims to locatea target object from a query view (e.g., ground or drone) within ageo-tagged reference image (e.g., satellite). Existing approaches heav-ily rely on 2D appearance matching and are constrained by limiteddatasets lacking geometric metadata, diverse prompts, and standardfield-of-view imagery. To address these intertwined challenges, we firstintroduce CMA-Loc, a large-scale, high-fidelity building dataset compris-ing over 220,000 ground-satellite and drone-satellite pairs. It providesmulti-modal prompts (points, boxes, masks) and camera poses to en-able flexible target referring and explicit spatial modeling. Furthermore,we propose a novel single-stage Geometry-Aware Geo-localization frame-work (GAGeo), built upon the permutation-equivariant 3D foundationmodel π 3 . By seamlessly integrating visual features, referring prompts,and learnable task tokens, our model adapts the inherited 3D prior tojointly predict bounding boxes, segmentation masks, and camera posesin a single forward pass. Additionally, we introduce a contrastive lossthat utilizes the satellite view as a universal anchor, implicitly aligningground and drone representations to enable zero-shot ground-to-drone lo-calization without requiring triplet training data. Extensive experimentsdemonstrate that our approach significantly outperforms state-of-the-artmethods, exhibiting exceptional generalization ability in unseen scenesand novel cross-view setups.

ECCV 2026

Audio-Visual Continual Test-Time Adaptation without Forgetting

Sarthak Kumar Maharana, Akshay Mehra, Bhavya Ramakrishna, Yunhui Guo, Guan-Ming Su

Audio-visual continual test-time adaptation involves contin-ually adapting a source audio-visual model at test-time, to unlabelednon-stationary domains, where either or both modalities can be distribu-tionally shifted, which hampers online cross-modal learning and eventuallyleads to poor accuracy. While previous works have tackled this problem,we find that SOTA methods suffer from catastrophic forgetting wherethe model’s performance drops well below even the source model dueto continual parameter updates at test-time. In this work, we first showthat adapting only the modality fusion layer to a target domain not onlyimproves performance on that domain but can also enhance performanceon subsequent domains. Based on this strong cross-task transferability ofthe fusion layer’s parameters, we propose a method, AVReCAP, that im-proves test-time performance of the models without access to any sourcedata. Our approach works by using a selective parameter retrieval mech-anism that dynamically retrieves the best fusion layer parameters from abuffer using only a small batch of test data. These parameters are thenintegrated into the model, adapted to the current test distribution, andsaved back for future use. Extensive experiments on benchmark datasetsinvolving unimodal and bimodal corruptions show our proposed AVReCAPsignificantly outperforms existing methods while minimizing catastrophicforgetting.

ECCV 2026

AffoGato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale

Junha Lee, Eunha Park, Chunghyun Park, Dahyun Kang, Minsu Cho

Affordance grounding aims to localize where to interact withan object, a fundamental capability for embodied agents. Yet progressis bottlenecked by data: manual annotation is prohibitively expensiveand confines existing datasets to a narrow set of predefined objectand affordance categories. We introduce Affogato, a framework foropen-vocabulary affordance grounding centered on Affogato-750K, alarge-scale dataset of 750K 3D affordance heatmaps paired with naturallanguage queries. We build it with a fully automated pipeline that or-chestrates foundation models to generate them at scale without humanlabeling. It covers significantly more diverse categories than any existingdataset. For reliable evaluation, we further provide 5K human-verified testpairs. We also present Espresso-3D and Espresso-2D, simple yet effec-tive models with a unified architecture across both modalities. Pretrainingon Affogato-750K improves both Espresso and prior methods and yieldsthe largest gains on unseen object and affordance categories, showingthat it provides broadly transferable supervision across architectures.

ECCV 2026

AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking

Zining Wang, Tongkun Guan, Boming Chen, Zhentao Guo, Jianqiang Liu, Chao Jin, Chen Duan, Kai Zhou, Pengfei Yan, Wei Shen, Xiaokang Yang

Multimodal large language models have demonstrated strongdocument reasoning capabilities by incorporating explicit thinking pro-cesses. While this capability significantly improves performance on chal-lenging tasks, current models apply such deep reasoning uniformly to allquestions, resulting in unnecessary computational overhead for simpletask. This not only degrades user experience but also negatively impactaccuracy on benchmark datasets. We identify the critical need for adap-tive thinking mechanisms that can intelligently determine when to en-gage reasoning based on question complexity. To address this, we proposeAdaThinking-E, a novel reinforcement learning framework that learnsadaptive thinking through one-token entropy regulation. Our key insightis that model confidence in the decision to engage thinking (or not)can be quantified through entropy analysis of the predicted probabilitydistribution at critical decision tokens. This observation motivates ourentropy-governed reward mechanism: the training process naturally tran-sitions from high-entropy exploration, where the model experiments withdifferent thinking strategies, to low-entropy convergence with confident,generalizable decision-making policies. Crucially, this approach enablesmodels to intrinsically discover when to think without requiring manualintervention or external difficulty labels. Extensive experiments demon-strate that our approach enables models to be both accurate on complexproblems and efficient on simple ones across diverse document tasks.Code is available at https://github.com/PriNing/AdaThinking-E.

ECCV 2026

YouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videos

Haoming Chen, Lichen Yuan, TianFang Sun, Jingyu Gong, Zhizhong Zhang, Xin Tan, Yanyun Qu, Yuan Xie

3D semantic occupancy prediction is crucial for fine-grainedscene understanding, yet its advancement in privacy-sensitive indoor en-vironments is fundamentally hindered by the scarcity of large-scale an-notated 3D data. To overcome this limitation, we explore learning indoor3D semantic occupancy prediction from abundant, uncalibrated in-the-wild internet videos while simultaneously bypassing the extensive manualannotation. Specifically, we introduce YouTube-Occ, including an auto-mated data pipeline that leverages 2D and 3D foundation models to pro-cess raw web videos, estimating camera geometry, reconstructing scenepoint clouds, and enriching them with dense semantic pseudo-labels.However, these plausible pseudo-labels fail to yield performance gains un-der naive supervision. To address this impasse, we further propose a pre-training framework driven by feature distillation with a dual-alignmentstrategy. Within it, an intra-frame alignment utilizes a voxel-anchoredGaussianization module to align 3D features with corresponding 2D pri-ors, whereas a cross-scene alignment achieves global semantic consistencyvia class-prototype distillation. Empirically, YouTube-Occ delivers con-sistent gains across three mainstream architectures on the NYUv2 andOcc-ScanNet benchmarks, especially under limited-data conditions. Wewill publicly release our code and data, hoping to inspire future research.

ECCV 2026

CabinSI: Omni-Cabin Spatial Reasoning through Explicit Visual Cognitive Maps

Mengxue Qu, Hengrui Hu, Mingming Ma, Ming Lei, Jie Gao, Henghui Ding, Yao Zhao, Kenn wu, Yunchao Wei

Intelligent cabin is gradually evolving from passive voicebased interfaces into multimodal collaborative systems supported by multi-camera and multi-sensor setups, where spatial reasoning becomes a core capability for enabling proactive perceptual interaction. Unlike traditional single-scene spatial tasks, cabin environments involve both interior and exterior vehicle regions, multiple occupants and traffic participants, and inherently require cross-view observations. To fill this research gap, we introduce CabinSI, a benchmark for evaluating spatial intelligence in cross-cabin environments, consisting of two components: RelCabin for relational reasoning tasks and RefCabin for spatial referring localization tasks. The benchmark is built upon real-world captured multi-view cabin data and systematically covers in-cabin, out-of-cabin, and cross-cabin scenarios under single-view, multi-view, and cross-cabinview settings. Furthermore, we propose a cognitive-map-based framework that projects multi-view observations onto a normalized top-down plane and constructs an explicit spatial graph as model input, allowing multimodal large language models to focus on structured reasoning. Experimental results demonstrate that such explicit spatial representation significantly improves the stability of cross-view reasoning in MLLMs. Data and code is available at CabinSI.

ECCV 2026

XSurfer: Reconstructing surface meshes of cerebral and cerebellar cortex from diverse MRI data using untrained neural networks

Haoxiang Li, Mingxuan Liu, Divya Varadarajan, Zhangxuan Hu, Qiyuan Tian, Jonathan Polimeni

Cortical surface reconstruction (CSR) is widely used in neuroimag-ing and is crucial for quantitative analyses of cerebral cortical thickness andsulcal morphology. While CSR is a mature technology when applied routinelyto adult T1 -weighted brain MRI data of the cerebrum, it remains underexploredfor a broad range of MRI contrasts, resolutions, ages, species, and brain struc-tures such as the cerebellum. To address this challenge, we propose XSurfer,a contrast- and resolution-agnostic CSR framework that performs optimizationon single images using an untrained neural network such that training data arenot needed. Specifically, XSurfer starts from segmentations of 3D MRI data,applies sulcal cerebrospinal fluid (CSF) recovery in sulci to address limitationscaused by partial-volume effects, identifies CSF voxels in deep sulci, and extractstopologically correct initial white matter (WM) surfaces and pseudo-target pialsurfaces. It then employs an untrained neural network to predict velocity fields,which are driven by mesh-based and geometrically-constrained loss functionterms to achieve diffeomorphic surface deformation, thereby reconstructing accu-rate pial surfaces. We evaluated XSurfer across different contrasts, resolutionsand seven datasets spanning adult, fetal, infant, ex vivo human, and non-humanprimate brain; XSurfer provides accurate CSR for all cases, surpassing state-of-the-art learning-based methods and recon-all-clinical recently providedby FreeSurfer as a more general-purpose CSR method. By enabling precise,contrast- and resolution-agnostic CSR without pretraining, XSurfer facilitatescross-domain multi-modal neuroimaging and standardizes morphometric analysisfor neuroscience research and clinical applications. The source code is publiclyavailable at: https://github.com/birthlab/XSurfer.

ECCV 2026

Vulnerability of Privacy-Preserving Visual Localization against Diffusion-based Attacks

Maxime Pietrantoni, Torsten Sattler, Gabriela Csurka

The rapid deployment of visual localization (VL) in aug-mented/virtual reality (AR/VR) and autonomous systems makes privacy-preserving localization a critical societal necessity. Existing VL systemsrely on cloud-based 3D scene representation storage and transmit clientside features to a remote server, exposing users to potential reconstruc-tion attacks from intercepted privacy preserving representations. The aimof this paper is therefore to investigate what sensitive information canan adversary actually recover, by comparing multiple privacy-preservingsolutions within the literature. We define privacy as the inability to re-cover personally identifiable information from these representations, ac-knowledging that general scene details do not inherently represent a pri-vacy breach. To model adversarial behavior and to thoroughly measure amethod’s degree of privacy preservation, we introduce a new privacy at-tack that trains a conditional diffusion model to reconstruct images fromprivacy-preserving representations. This reconstruction quality serves asa direct proxy for the amount of sensitive information each represen-tation contains. Leveraging this unified attack protocol, we present thefirst comprehensive comparison across major families of privacy preserv-ing VL methods. Our analysis reveals substantial differences in privacyleakage between representations and highlights limitations in current de-sign assumptions. Code and trained checkpoints may be found here:https://github.com/MaximePi/benchmark-privacy-inversion.

ECCV 2026

VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors

Atif Belal, Heitor Medeiros, Marco Pedersoli, Eric Granger

Vision-language object detectors (VLODs) such as YOLO-World and Grounding DINO exhibit strong zero-shot generalization, buttheir performance degrades under distribution shift. Test-time adapta-tion (TTA) offers a practical way to adapt models online using onlyunlabeled target data. However, despite substantial progress in TTAfor vision-language classification, TTA for VLODs remains largely unex-plored. The only prior method relies on a mean-teacher framework thatintroduces significant latency and memory overhead. To this end, weintroduce VLOD-TTA, a TTA method that leverages dense proposaloverlap and image-conditioned prompts to adapt VLODs with low addi-tional overhead. VLOD-TTA combines (i) an IoU-weighted entropy ob-jective that emphasizes spatially coherent proposal clusters and mitigatesconfirmation bias from isolated boxes, and (ii) image-conditioned promptselection that ranks prompts by image-level compatibility and aggregatesthe most informative prompt scores for detection. Our experiments acrossdiverse distribution shifts, including artistic domains, adverse drivingconditions, low-light imagery, and common corruptions, indicate thatVLOD-TTA consistently outperforms standard TTA baselines and theprior state-of-the-art method using YOLO-World and Grounding DINO.Code: https://github.com/imatif17/VLOD-TTA

ECCV 2026

VISOR++ : VISUAL INPUT BASED STEERING FOR LARGE VISION LANGUAGE MODELS

Ravikumar Balakrishnan, Mansi Phute

As Vision Language Models (VLM) are deployed across safety-critical applications, understanding and controlling their behavioral pat-terns has become increasingly important. Existing behavioral controlmethods face significant limitations: system prompting is a popular ap-proach but could easily be overridden by user instructions, while ap-plying activation-based steering vectors requires invasive runtime accessto model internals, precluding deployment with API-based services andclosed-source models. Finding steering methods that transfer across mul-tiple VLMs is still an open area of research. To this end, we introduce vi-sual input based steering for output redirection (VISOR++), a novel ap-proach that achieves behavioral control through optimized visual inputsalone. We demonstrate that a single VISOR++ image can be generatedfor two architecturally diverse VLMs that by itself can emulate each oftheir steering vectors. By crafting universal visual inputs that induce tar-get activation patterns for an ensemble of models, VISOR++ eliminatesthe need for runtime model access while remaining deployment-agnostic.This means that when an underlying model supports multimodal capa-bility, model behaviors can be steered by inserting an image input com-pletely replacing runtime steering vector based interventions. We firstdemonstrate the effectiveness of the VISOR++ images on open-accessmodels such as LLaVA-1.5-7B and IDEFICS2-8B along three alignmentdirections: refusal, sycophancy and survival instinct. Both the model-specific steering images and the jointly optimized images achieve perfor-mance parity closely following that of steering vectors for both positiveand negative steering tasks. We also show early promise of VISOR++images in achieving directional behavioral shifts for unseen models thatinclude both open-access and closed-access models. At the same time,VISOR++ images are able to preserve 99.9% performance on 14,000unrelated MMLU evaluation samples highlighting their specificity to in-ducing only behavioral shifts.

ECCV 2026

VERITAS: A Multi-agent Co-scientist for Verifiable Image-Derived Hypothesis Testing

Lucas Stoffl, Benedikt Wiestler, Johannes Paetzold

Scienti_x001C_c research based on multimodal clinical data (includ-ing medical imaging) requires coordinating clinical, radiological, pro-gramming, and biostatistical expertise, a fragmented process that bottle-necks discovery. We present Veritas (Veri_x001C_able Epistemic Reasoningfor Image-Derived Hypothesis Testing via Agentic Systems), a clinicalco-scientist: a multi-agent system that autonomously tests natural-language hypotheses and produces a fully auditable evidence trail, trac-ing every conclusion through executable outputs from analysis plan tosegmentation masks to statistical code to _x001C_nal verdict. Unlike prior AI-scientist systems, which mainly operate on tabular or text data, Veritasgrounds autonomous discovery directly in medical images. It decomposesthe work_x001D_ow into four phases handled by role-specialized agents, and in-troduces an epistemic evidence label framework that mechanically classi-_x001C_es outcomes as Supported, Refuted, Underpowered, or Invalid byjointly evaluating signi_x001C_cance, e_x001B_ect direction, and study power. This dis-tinction is critical in medical imaging, where non-signi_x001C_cant results oftenre_x001D_ect insu_x001E_cient sample size rather than absent e_x001B_ects. We construct atiered benchmark of 64 hypotheses spanning six complexity levels acrosscardiac and brain glioma MRI datasets. Veritas reaches 81.4% verdictaccuracy with frontier models and 71.2% with locally-hosted open-weightmodels (8_x0015_30B), outperforming all single-model baselines in both classes.It also produces the highest rate of independently veri_x001C_able statisticaloutputs (86.6%), so even its failures remain diagnosable through arti-fact inspection. Structured multi-agent decomposition thus substitutesfor model scale while preserving the veri_x001C_ability that scienti_x001C_c discoverydemands. We release code, hypothesis bank, and evaluation pipeline athttps://github.com/LucZot/veritas.

ECCV 2026

Universal Image Immunization against Diffusion-based Image Editing via Semantic Injection

Chanhui Lee, Donggyu Choi, Seunghyun Shin, Hae-Gon Jeon, Jeany Son

Diffusion model advances have enabled powerful text-guidedimage editing, but also raise ethical and legal risks such as deepfakesand unauthorized use. To prevent these risks, adversarial attack-basedimage immunization has emerged as a promising defense against AI-driven semantic manipulation. Yet, most existing approaches requireimage-specific optimization or additional neural networks at inferencetime, hindering scalability and practicality. In this paper, we proposethe first universal adversarial perturbation-based image immunizationframework that generates a single, image-agnostic adversarial perturbationspecifically designed for diffusion-based editing pipelines. Inspired by UAPused in targeted attacks, our method aims to generate a UAP that inducesdiffusion models to misinterpret the input image as a specific semantictarget. Simultaneously, it suppresses original content to misdirect themodel’s attention during editing, thereby effectively blocking unauthorizededits by overwriting the image’s original semantics via the UAP. Extensiveexperiments show that our method, as the first universal immunizationapproach, significantly outperforms several baselines in the UAP setting.Notably, despite the inherent difficulty of universal perturbations, ourmethod achieves competitive or superior performance compared to image-specific methods under a more restricted perturbation budget, while alsoexhibiting strong black-box transferability across diverse diffusion models.

ECCV 2026

Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering

Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Van Nguyen, Terrence Chen, Ziyan Wu

Photorealistic volumetric rendering of CT scans greatly ben-e_x001C_ts clinical work_x001D_ows, yet neural approaches such as Neural RadianceFields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitiveper-scan optimization (hours for NeRF, about 30 minutes for 3DGS),making them impractical in clinical settings. We propose Render-FM,a feedforward model that eliminates this bottleneck by directly regress-ing 6D Gaussian Splatting (6DGS) parameters from a CT volume ina single 2.8-second forward pass, a 500× speedup over per-scan opti-mization. To bridge the domain gap between natural scene reconstruc-tion and medical volumetric rendering, we introduce Anatomy-GuidedPriming (AGP), which incorporates segmentation masks and transferfunctions as structural and appearance priors, information that exist-ing Gaussian splatting methods overlook. Built on an nnU-Net-inspired3D U-Net trained on diverse CT scans, Render-FM predicts per-voxel6DGS parameters and supports immediate real-time rendering. Unlikeper-scan methods, it generalizes to unseen anatomies, novel transferfunctions, and enables compositional organ visualization with zero ad-ditional preparation time. Optional 89-second _x001C_ne-tuning further im-proves quality, surpassing per-scan optimized baselines. Project page:https://gaozhongpai.github.io/renderfm/.

ECCV 2026

From Local to Global: A Progressive Reconstruction Network for Diffractive Snapshot Spectral Imaging

Zhengyue Zhuge, Shiqi Chen, Chi Zhang, Jiahui Xu, Tianchen Qiu, Dingchuan Yu, Yueting Chen

Diffractive Snapshot Spectral Imaging (DSSI) encodes spec-tral and spatial information into a single 2D measurement via a diffrac-tive optical element (DOE), offering a compact and light-efficient al-ternative to conventional Coded-Aperture Snapshot Spectral Imaging(CASSI). However, the spatially uneven coding of DSSI, which is mainlyconcentrated in texture-rich regions, has led existing reconstruction net-works to rely heavily on global attention modules to propagate spectralfeatures into texture-less regions, making them short-sighted in locallycoded areas and computationally redundant. To address these challenges,we propose Local-to-Global Spectral Reconstruction Network (LGSR-Net), a two-stage progressive framework comprising complementary lo-cal and global subnetworks. The local part extracts information fromtexture-rich regions under direct supervision from effectively coded ar-eas, while the global part models long-range dependencies to facilitateglobal information flow. We introduce a spatially guided channel atten-tion module to emphasize informative regions and an edge-aware gatedfusion mechanism to integrate local features into the global subnetwork’sdecoders. Moreover, a coding-aware sample selection strategy and a tai-lored loss function are designed to improve training efficiency and re-construction fidelity. Extensive experiments on both simulated and realDSSI datasets show that LGSRNet achieves state-of-the-art reconstruc-tion performance with significantly lower computational cost and latency.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.