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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

Controlling Embedding Spaces with Text-Conditioned Transformations

Joseph Fioresi, Fabian Caba, Pankaj Nathani, Shah Mubarak, Kushal Kafle

Multimodal embedding spaces in models like CLIP enablepowerful capabilities such as semantic similarity retrieval and cross-modalzero-shot classification. These embeddings compress high-level seman-tics into a single vector, which comes at the cost of primarily express-ing a dominant semantics like main object while suppressing other im-portant attributes such as camera angle or color tone. We propose atext-conditioned transformation of visual embeddings that makes suchattributes explicitly accessible. Given a natural language description ofan attribute category (e.g., “color” or “art style”), a network generates anaffine transformation that emphasizes the specified attribute. Condition-ing on text enables it to learn many attributes simultaneously, accessingthem at inference time through an intuitive interface. The network istrained to align transformed embeddings with the frozen latent space,enabling retrieval using existing large-scale embeddings without any re-encoding. When applied to a full set, the same mechanism transforms thelatent space for attribute disentanglement tasks such as multi-clustering.By operating directly in latent space, our method provides a unifiedand efficient framework for controlling embedding spaces, demonstratingstate-of-the-art performance across both attribute-based retrieval andmulti-attribute organization tasks with near-zero inference cost.

ECCV 2026

Controllable Generative Reference for Stereo Image Compression via Reliability-Aware Gating

Zhineng Zhao, Mingyao Hong, Fengfan Shi, Zhihai He

Stereo image compression fundamentally relies on exploitingcross-view redundancies. We observe that the performance of conven-tional correspondence-driven methodologies degrades severely in out-of-view (OOV) regions or when reference features are heavily quantized,resulting in blurred textures and prediction failure. In this paper, we pro-pose a novel generative stereo image compression framework driven bya controllable generative reference. Instead of relying on finding explicitcorrespondences from a degraded reference, we leverage the powerful pri-ors of a pre-trained diffusion model to synthesize a high-quality target-view reference. To bridge the gap between stochastic generative processesand strict stereo consistency, we introduce Joint Semantic-Spatial Con-trol for reference generation. This mechanism synergistically steers thesynthesis using a robust geometric anchor from the compressed left viewand an ultra-compact semantic condition extracted from the target view.Furthermore, to minimize the impact of structural inconsistency duringreference generation on the quality of the reconstructed image, we de-sign a Reliability-Aware Gating module. By dynamically evaluating thesynthesized prior in the feature space, this module adaptively regulatesgenerative information flow within the entropy model to improve thereconstructed image quality. Extensive experiments on standard bench-marks demonstrate that our framework achieves state-of-the-art rate-distortion performance, delivering remarkable bitrate savings and highvisual quality at low bitrates.

ECCV 2026

Hierarchical 3D Scene Graph Construction and Belief-based Planning for Semantic Navigation

Bing Wu, Zuyao Chen, Chang Wen Chen

Semantic navigation is a fundamental task for embodiedagents operating in unseen environments, requiring both semantic un-derstanding and long-term decision-making. Recent foundation modelshave empowered agents with rich semantic priors for this task. How-ever, without structured global representations, decision-making oftenfalls back on local observations and greedy strategies, resulting in in-efficient exploration and myopic behaviors, especially in long-distancenavigation. To address these challenges, we propose a zero-shot semanticnavigation framework. Our method incrementally maintains an onlineHierarchical 3D Scene Graph (HSG) to form a multi-granular semantictopology over objects, zones, and regions, serving as a compact stateabstraction for global planning. Building on this memory, we introducea hierarchical belief-based planning framework that fuses semantic pri-ors with exploration evidence on the HSG, and performs finite-horizonrollouts on an HSG-based simulator to explicitly estimate the long-termexpected returns of candidate macro-actions. This enables globally con-sistent decisions and reduces redundant backtracking. Extensive experi-ments in high-fidelity simulation environments across multiple tasks anddatasets demonstrate that our method outperforms existing state-of-the-art methods, particularly in long-distance scenarios, where our approachimproves SR and SPL by an average of 9.4% and 5.0%, respectively.

ECCV 2026

Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation

Haoyun Chen, Fenghe Tang, Wenxin Ma, S. Kevin Zhou

Universal medical image segmentation seeks to use a singlefoundational model to handle diverse tasks across multiple imaging modal-ities. However, existing approaches often rely heavily on manual visualprompts or retrieved reference images, which limits their automationand robustness. In addition, naive joint training across modalities oftenfails to address large domain shifts. To address these limitations, wepropose Concept-to-Pixel (C2P), a novel prompt-free universal seg-mentation framework. C2P explicitly separates anatomical knowledge intotwo components: Geometric and Semantic representations. It leveragesMultimodal Large Language Models (MLLMs) to distill abstract, high-level medical concepts into learnable Semantic Tokens and introducesexplicitly supervised Geometric Tokens to enforce universal physical andstructural constraints. These disentangled tokens interact deeply withimage features to generate input-specific dynamic kernels for precisemask prediction. Furthermore, we introduce a Geometry-Aware InferenceConsensus mechanism, which utilizes the model’s predicted geometricconstraints to assess prediction reliability and suppress outliers. Extensiveexperiments and analysis on a unified benchmark comprising eight diversedatasets across seven modalities demonstrate the significant superiorityof our jointly trained approach, compared to universe- or single-modelapproaches. Remarkably, our unified model demonstrates strong general-ization, achieving impressive results not only on zero-shot tasks involvingunseen cases but also in cross-modal transfers across similar tasks. Codeis available at: https://github.com/Yundi218/Concept-to-Pixel.

ECCV 2026

CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization

Chuyan Chen, Peng Sun, Kun Yuan

Diffusion Transformers (DiTs) have achieved state-of-the-art(SOTA) performance in visual generative modeling, yet their training re-mains computationally prohibitive. While the recently proposed Momen-tum Orthogonalization (Muon) optimizer offers a promising alternativeto AdamW, its direct application to DiTs yields suboptimal late-stageconvergence. In this paper, we identify the root cause of this bottleneck:standard DiT architectures fuse functionally distinct weights (e.g., withinAdaLN and QKV layers) into unified tensors for computational efficiency.Applying Muon to these fused tensors inadvertently induces implicit sub-space coupling, which distorts update directions and degrades global op-timization. To address this, we introduce Chunked Muon (CMuon), asimple yet highly effective strategy that partitions these matrices intoindependent sub-components prior to orthogonalization. Extensive ex-periments demonstrate that a 675M-parameter DiT trained with CMuonachieves a FID of 1.18 on ImageNet 256 in just 200 epochs. This rep-resents more than a 2x training speedup over AdamW, while effectivelyovercoming the late-stage convergence plateaus of vanilla Muon.

ECCV 2026

CGCE: Classifier-Guided Concept Erasure in Generative Models

Viet Nguyen, Vishal Patel

Recent advancements in large-scale generative models haveenabled the creation of high-quality images and videos, but have alsoraised significant safety concerns regarding the generation of unsafe con-tent. To mitigate this, concept erasure methods have been developed toremove undesirable concepts from pre-trained models. However, existingmethods remain vulnerable to adversarial attacks that can regeneratethe erased content. Moreover, achieving robust erasure often degradesthe model’s generative quality for safe, unrelated concepts, creating adifficult trade-off between safety and performance. To address this chal-lenge, we introduce Classifier-Guided Concept Erasure (CGCE), an effi-cient plug-and-play framework that provides robust concept erasure fordiverse generative models without altering their original weights. CGCEuses a lightweight classifier operating on text embeddings to first detectand then refine prompts containing undesired concepts. By modifyingonly unsafe embeddings at inference time, our method prevents harm-ful content generation while preserving the model’s original quality onbenign prompts. Extensive experiments show that CGCE achieves state-of-the-art robustness against a wide range of red-teaming attacks. Ourapproach also maintains high generative utility, demonstrating a superiorbalance between safety and performance. We showcase the versatility ofCGCE through its successful application to various modern T2I and T2Vmodels, establishing it as a practical and effective solution for safe gen-erative AI.

ECCV 2026

Calibrate Before Adapt: Training-Free Pseudo-Label Calibration for Semi-Supervised Cross-Domain Few-Shot Detection

Xin Yang, Fei Zhou, Wei Wei, Lei Zhang

Semi-supervised cross-domain few-shot object detection (SemiCDFSOD) is a practical yet underexplored task that requires adapting detectors to novel domains using scarce support examples alongside abundant unlabeled data. While pseudo-labeling via open-set detectors offers a direct pathway to exploit unlabeled data, it is fundamentally impaired by two intrinsic defects: semantic misclassification, where localized objects are assigned incorrect categories or suppressed as background, and low recall, where the majority of objects are missed entirely. To systematically address these limitations, we propose a training-free pseudo-label calibration framework for Semi-CDFSOD. First, we construct robust class prototypes from the scarce labeled supports and high-confidence pseudo-labels to rectify semantic misclassifications, dynamically recalibrating noisy category assignments while preserving welllocalized detections. Second, we introduce a visual self-prompting mechanism that harnesses high-confidence pseudo-labels as prompts for the Segment Anything Model (SAM), generating supplementary proposals to recover undetected objects and dramatically enhance recall. The harmonized pseudo-labels, together with the support samples, are then employed to adapt the final detector. We establish the first comprehensive benchmark for Semi-CDFSOD, spanning six diverse domains. Extensive experiments demonstrate that our method consistently outperforms existing approaches. The code will be released at https://github.com/ xSheep123/Semi-CDFSOD.git.

ECCV 2026

Beyond Sequential Distance: Inter-Modal Distance Invariant Position Encoding

Lin Chen, Bolin Ni, Qi Yang, Zili Wang, Kun Ding, Ying Wang, Houwen Peng, Shiming Xiang

Despite the remarkable capabilities of Multimodal Large Lan-guage Models (MLLMs), they still suffer from visual fading in long-context scenarios. Specifically, the attention to visual tokens diminishesas the text sequence lengthens, leading to text generation detached fromvisual constraints. We attribute this degradation to the inherent induc-tive bias of Multimodal RoPE, which penalizes inter-modal attention asthe distance between visual and text tokens increases. To address this, wepropose inter-modal Distance Invariant Position Encoding (DIPE), asimple but effective mechanism that disentangles position encoding basedon modality interactions. DIPE retains the natural relative positioningfor intra-modal interactions to preserve local structure, while enforc-ing an anchored perceptual proximity for inter-modal interactions. Thisstrategy effectively mitigates the inter-modal distance-based penalty, en-suring that visual signals remain perceptually consistent regardless ofthe context length. Experimental results demonstrate that by integrat-ing DIPE with Multimodal RoPE, the model maintains stable visualgrounding in long-context scenarios, significantly alleviating visual fad-ing while preserving performance on standard short-context benchmarks.Code is available at https://github.com/lchen1019/DIPE.

ECCV 2026

ActionPlan: Future-Aware Streaming Motion Synthesis via Frame-Level Action Planning

Eric Nazarenus, Chuqiao Li, Yannan He, Xianghui Xie, Jan Eric Lenssen, Gerard Pons-Moll

We present ActionPlan, a unified motion diffusion frame-work that bridges real-time streaming with high-quality offline genera-tion within a single model. The core idea is to introduce a per-frameaction plan: the model predicts frame-level text latents that act as densesemantic anchors throughout denoising, and uses them to denoise thefull motion sequence with combined semantic and motion cues. To sup-port this structured workflow, we design latent-specific diffusion steps,allowing each motion latent to be denoised independently and sampledin flexible orders at inference. As a result, ActionPlan can run in ahistory-conditioned, future-aware mode for real-time streaming, whilealso supporting high-quality offline generation. The same mechanism fur-ther enables zero-shot motion editing and in-betweening without addi-tional models. Experiments demonstrate that our real-time streaming is5.25× faster while also achieving 18% motion quality improvement overthe best previous method in terms of FID.∗ †Equal contribution. Corresponding author

ECCV 2026

λSplit: Self-Supervised Content-Aware Spectral Unmixing for Fluorescence Microscopy

Federico Carrara, Mehdi Seifi, Florian Jug

In fluorescence microscopy, spectral unmixing aims to re-cover individual fluorophore concentrations from spectral images thatcapture mixed fluorophore emissions. Since classical methods operatepixel-wise and rely on least-squares fitting, their performance degradeswith increasingly overlapping emission spectra and higher levels of noise,suggesting that a data-driven approach that can learn and utilize a struc-tural prior might lead to improved results. Learning-based approachesfor spectral imaging do exist, but they are either not optimized for mi-croscopy data or are developed for very specific cases that are not ap-plicable to fluorescence microscopy settings. To address this, we pro-pose λSplit, a physics-informed deep generative model that learns aconditional distribution over concentration maps using a hierarchicalVariational Autoencoder. A fully differentiable Spectral Mixer enforcesconsistency with the image formation process, while the learned struc-tural priors enable state-of-the-art unmixing and implicit noise removal.We demonstrate λSplit on 3 real-world datasets that we syntheticallycast into a total of 58 challenging spectral unmixing benchmarks. Wecompare our results against a total of 10 baseline methods, includingclassical methods and a range of learning-based methods. Our resultsconsistently show competitive performance and improved robustness inhigh noise regimes, when spectra overlap considerably, or when the spec-tral dimensionality is lowered, making λSplit a new state-of-the-art forspectral unmixing of fluorescence microscopy data. Importantly, λSplitis compatible with spectral data produced by standard confocal micro-scopes, enabling immediate adoption without specialized hardware mod-ifications. The code and all datasets used in the experiments are availableat https://github.com/juglab/lambdaSplit.

ECCV 2026

XDen-1K: A Density Field Dataset of Real-World Objects

Jingxuan Zhang, Tianqi Yu, Yatu Zhang, Jinze Wu, Kaixin Yao, Jingyang Liu, Yuyao Zhang, Jiayuan Gu, Jingyi Yu

A deep understanding of the physical world is essential forrobotic manipulation and physically realistic simulation. While currentmethods, including VLM-based and other learning-based approaches,have shown promise in physical property inference, their evaluation isoften hindered by the lack of physically grounded reference data. Toaddress this gap, we introduce XDen-1K, the first large-scale multi-modal dataset that provides physically grounded density field for real-world objects. XDen-1K comprises 1,000 real-world objects spanning137 categories, with comprehensive data for each object, including ahigh-resolution, carefully curated 3D geometric model with part-levelannotations and paired real-world biplanar X-ray scans. In addition,XDen-1K includes high-fidelity volumetric density field reconstructedfrom sparse biplanar X-ray views via a novel optimization framework.XDen-1K also provides a benchmark for density estimation and enablesX-ray-conditioned volumetric segmentation. Experiments further demon-strate that the center-of-mass prior derived by XDen-1K can improverobotic manipulation performance. By providing real-world X-ray scansand physics-consistent density field, XDen-1K establishes a foundationfor advancing physical property inference and embodied AI. Additionaldetails and dataset resources are available at https://xden-1k.github.io/ and https://huggingface.co/datasets/zhangjxx/XDen-1K.

ECCV 2026

VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning

Mingkang Dong, Hongyi Cai, Jie Li, Sifan Zhou, Bin Ren, Kunyu Peng, Yuqian Fu

The effectiveness of multimodal instruction tuning dependsnot only on dataset scale, but also critically on whether training samplesgenuinely require visual reasoning. However, existing instruction datasetsoften contain a substantial portion of visually redundant samples (solv-able from text alone), as well as multimodally misaligned supervisionthat can degrade learning. To address this, we propose VisNec (VisualNecessity Score), a principled data selection framework that measuresthe marginal contribution of visual input during instruction tuning. Bycomparing predictive loss with and without visual context, VisNec iden-tifies whether a training instance is vision-critical, redundant, or mis-aligned. To preserve task diversity, we combine VisNec with semanticclustering and select high-necessity samples within each cluster. Across10 downstream benchmarks, training on only 15% of the LLaVA-665Kdataset selected by VisNec achieves 100.2% of full-data performance. Onthe smaller Vision-Flan-186K dataset, our selection not only further re-duces data size but surpasses full-data training by 15.8%. These resultsdemonstrate that measuring and leveraging visual necessity provides aneffective solution for both efficient and robust multimodal instructiontuning. Project Page: https://dmk041218.github.io/VisNec/.

ECCV 2026

VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus

Rohit Sinha, Kunal Tilaganji, Tanuja Ganu, Nagarajan Natarajan, Amit Sharma, Vineeth N Balasubramanian

Multimodal large language models often generate reasoningchains containing subtle errors that lead to incorrect answers. Currentverification approaches have notable limitations. Existing approaches ei-ther require expensive labelled supervision with inconsistent cross-taskperformance or aggregate scores from multiple sources by simple ag-gregations, missing a key insight: when these scores disagree, that dis-agreement itself carries important information about whether a reasoningstep is truly valid or not. We formalise this as a coupled scoring prob-lem among disparate, frozen verifiers, interpretable as a coordinationgame with a unique closed-form equilibrium where agreement signalsvalid steps while disagreement reveals instability. Towards this end, wepropose a training-free domain-agnostic step-wise verification approachwe call VERDICT: VERification via Disagreement-Informed CoupledThresholding. To our knowledge, VERDICT is the first training-freeverifier that makes the structure of cross-modal disagreement explicit andactionable. It computes consensus scores through a closed-form solution,enabling both disagreement-aware filtering and stability-conscious rank-ing of reasoning steps. Evaluated across six benchmarks, VERDICTconsistently improves over the base model by up to +5.95%, and per-forms competitively with domain-specific critics that demand extensivesupervision, demonstrating that cross-modal agreement provides robustverification signals without task-specific adaptation.

ECCV 2026

UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations

Tingyu Yuan, Biaoliang Guan, Wen Ye, Ziyan Tian, Yi Yang, Weijie Zhou, Zhaowen Li, Yan Huang, Peng Wang, Chaoyang Zhao, Jinqiao Wang

In embodied intelligence, the embodiment gap between roboticand human hands brings significant challenges for learning from humandemonstrations. Although some studies have attempted to bridge thisgap using reinforcement learning, they remain confined to merely re-producing human manipulation, resulting in limited task performance.Moreover, current methods struggle to support diverse robotic hand con-figurations. In this paper, we propose UniBYD, a unified framework thatuses a dynamic reinforcement learning algorithm to discover manipula-tion policies aligned with the robot’s physical characteristics. To enableconsistent modeling across diverse robotic hand morphologies, UniBYDincorporates a unified morphological representation (UMR). Building onUMR, we design a dynamic PPO with an annealed reward schedule, en-abling reinforcement learning to transition from offline-informed imita-tion of human demonstrations to online-adaptive exploration of policiesbetter adapted to diverse robotic morphologies, thereby going beyondmere imitation of human hands. To address the severe state drift causedby the incapacity of early-stage policies, we design a hybrid Markov-based shadow engine that provides fine-grained guidance to anchor theimitation within the expert’s manifold. To evaluate UniBYD, we proposeUniManip, the first benchmark for cross-embodiment manipulation span-ning diverse robotic morphologies. Experiments demonstrate a 44.08%average improvement in success rate over the current state-of-the-art.Our project page is https://zhanheng-creator.github.io/UniBYD/.Mocap Data2-fingered3-fingeredUMRReinforcement learningShadow Engine5-fingeredDynamic PPOUniBYD Our results Imitation-based reinforcement learningFig. 1: Leveraging human demonstrations, UniBYD learns manipulation strategiesthat transcend mere imitation and are tailored to a broad spectrum of robotic handmorphologies.

ECCV 2026

Trust-Region Noise Search for Black-Box Alignment of Diffusion and Flow Models

Niklas Schweiger, Karnik Ram, Daniel Cremers

Optimizing the noise samples of diffusion and flow models isan increasingly popular approach to align these models to target rewardsat inference time. However, we observe that these approaches are usuallyrestricted to differentiable or cheap reward models, the formulation ofthe underlying pre-trained generative model, or are memory/computeinefficient. We instead propose a simple trust-region based search algorithm(TRS) which treats the pre-trained generative and reward models as ablack-box and only optimizes the source noise. Our approach achieves agood balance between global exploration and local exploitation, and isversatile and easily adaptable to various generative settings and rewardmodels with minimal hyperparameter tuning. We evaluate TRS acrosstext-to-image, molecule and protein design tasks, and obtain significantlyimproved output samples over the base generative models and otherinference-time alignment approaches which optimize the source noisesample, or even the entire reverse-time sampling noise trajectories in thecase of diffusion models. Our source code is publicly available⋆ .

ECCV 2026

Towards Reliable Multi-Label Classification via Conditional Dependency Modeling

Arkapal Panda, Aditya Shankar Pal, Utpal Garain

Multi-label classification models are commonly trained byassuming conditional independence among labels, typically through theuse of binary cross-entropy objectives. While this assumption simpli-fies optimization, it introduces a structural bias that ignores dependen-cies among labels and can adversely affect the calibration of predictedprobabilities. In this work, we theoretically analyze the impact of thisassumption and show that neglecting label dependencies leads to a mis-calibration effect that is linked to the conditional dependency of thelabels. Motivated by this observation, we propose Pairwise CorrelationDifference (PCD), a novel auxiliary loss designed to incorporate labeldependency information during training. PCD aligns the pairwise cor-relations of model logits with those of the ground-truth labels, therebyencouraging the network to capture conditional dependencies among la-bels. We combine PCD with the standard binary cross-entropy loss toform the Correlated Multi-Label Loss (CMLL), which serves as a objec-tive for dependency-aware training. We provide theoretical justificationfor the functional form of the proposed loss and its connection to thedependency structure of the label distribution. Extensive experimentson three benchmark multi-label datasets demonstrate that CMLL con-sistently produces better-calibrated predictions while maintaining com-petitive classification accuracy compared to existing multi-label learningobjectives. Additionally, we show that the proposed approach exhibitsrobustness to the choice of binning schemes used in calibration evalua-tion.

ECCV 2026

Towards Reliable Medical Large Vision-Language Models via Counterfactual Preference Optimization

Xiaoguang Zhu, NaipengWang NaipengWang, Kartik Patwari, Lianlong Sun, Chen-Nee Chuah, ChengxinPang ChengxinPang

Medical Large Vision-Language Models (Med-LVLMs) haveemerged as powerful tools for medical image understanding and au-tomated reporting. However, existing models often suffer from modal-ity bias, where correlations between background context and diagnos-tic labels cause over-reliance on spurious priors rather than lesion evi-dence, leading to clinically irrelevant or hallucinated outputs. To addressthis, we propose a Counterfactual Medical Preference Optimization(CoMedPO) framework that learns an unbiased policy from a biasedreference model. Unlike conventional direct preference optimization (DPO)methods that inherit dataset biases, CoMedPO mitigates spurious cor-relations while preserving the valuable indirect effect between lesionand background dependencies in medical images. This causal formula-tion yields a new preference-based loss with theoretical guarantees forbias mitigation and robust policy learning. Our framework is model-agnostic and seamlessly integrates with existing Med-LVLM alignmentpipelines. Extensive experiments on Med-VQA and medical report gen-eration demonstrate that CoMedPO consistently improves factual accu-racy and clinical relevance, outperforming state-of-the-art DPO variants.These results suggest that CoMedPO provides a principled route towardtrustworthy and clinically grounded multimodal medical reasoning. Ourcode is available at https://github.com/zxgapollo/CoMedPO.

ECCV 2026

Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning

Zhen Zeng, Leijiang Gu, Zhangling Duan, Feng Li, Zenglin Shi, Cees Snoek, Meng Wang

Multimodal large language models (MLLMs) can inadver-tently memorize privacy-sensitive information during training. Whileexisting unlearning methods can remove such content, they often severelydegrade the model’s foundational capabilities, such as general imageunderstanding. This critical shortfall motivates our investigation into be-nign memory forgetting, the precise removal of targeted, privacy-sensitiveknowledge while rigorously preserving unrelated capabilities. To pioneerand evaluate progress toward this objective, we introduce S-MLLMUnBench, the first benchmark designed to jointly and quantitatively assessan unlearning method’s efficacy in knowledge erasure and the preservationof image understanding. Furthermore, we propose the Sculpted MemoryForgetting Adapter (SMFA), a new framework that enables benign mem-ory forgetting. SMFA confines forgetting to designated memory regions,maintaining overall model performance. By initially fine-tuning the modelto replace sensitive outputs with refusals, SMFA generates a memoryforgetting adapter, followed by a retaining anchor-guided masking mech-anism that safeguards unrelated knowledge. Extensive experiments onS-MLLMUn Bench demonstrate that existing methods fail to achievebenign forgetting, whereas our proposed SMFA serves as an effectivebaseline, successfully achieving targeted knowledge erasure without com-promising the model’s foundational visual capabilities. Code and dataare available at https://github.com/zeng-zhen/S-MLLMUn.

ECCV 2026

SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch‑Level Incoherence and Temporal Roughness

Jongyeop Hyun, Hyounghun Kim

Deploying AI-generated video detectors in real-world ser-vices demands an ultra-low false positive rate (FPR) on real videosto avoid falsely rejecting authentic content, a regime where standardmetrics such as AUROC fail to reflect actual operating behavior. Weintroduce Spatial Patch-Level Incoherence and Temporal Roughness(SPLIT), a training-free detector that operates on patch tokens froma frozen vision encoder to detect both fully generated and partiallyedited videos. SPLIT computes two complementary signals: Two-stepTemporal Roughness (TTR), capturing non-smooth patch trajectoriesvia one-step and two-step feature variation contrast, and Local SpatialMotion Incoherence (LSMI), measuring spatially inconsistent temporalchanges through gradients of a feature-space motion field. The two arefused multiplicatively with gamma correction to sharpen real–fake sepa-ration at strict thresholds. We further propose a service-aligned evalua-tion protocol based on Fake Recall at fixed FPR with real-only thresholdcalibration and cross-real threshold transfer. Across three benchmarks—FakeParts, GenVideo, and ViF-Bench—SPLIT achieves the highest FakeRecall at FPR = 0.1%, substantially outperforming supervised and training-free baselines while remaining robust to post-processing with negligi-ble overhead. The code is publicly available at https://github.com/mldljyh/SPLIT.

ECCV 2026

SEM-ROVER: Semantic Voxel-Guided Diffusion for Large-Scale Driving Scene Generation

Hiba Dahmani*, Nathan Piasco, Moussab Bennehar, Luis G Roldao Jimenez*, Dzmitry Tsishkou, Laurent Caraffa, Jean-Philippe Tarel, Roland Brémond

Scalable generation of outdoor driving scenes requires 3Drepresentations that remain consistent across multiple viewpoints andscale to large areas. Existing solutions either rely on image or video gen-erative models distilled to 3D space, harming the geometric coherenceand restricting the rendering to training views, or are limited to small-scale 3D scene or object-centric generation. In this work, we propose a3D generative framework based on Σ-Voxfield grid, a discrete representa-tion where each occupied voxel stores a fixed number of colorized surfacesamples. To generate this representation, we train a semantic-conditioneddiffusion model that operates on local voxel neighborhoods and uses 3Dpositional encodings to capture spatial structure. We scale to large scenesvia progressive spatial outpainting over overlapping regions. Finally, werender the generated Σ-Voxfield grid with a deferred rendering moduleto obtain photorealistic images, enabling large-scale multiview-consistent3D scene generation without per-scene optimization. Extensive experi-ments show that our approach can generate diverse large-scale urbanoutdoor scenes, renderable into photorealistic images with various sen-sor configurations and camera trajectories while maintaining moderatecomputation cost compared to existing approaches.

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