ECCV 2026 论文列表

We present HUGE-Bench, a benchmark for High-LevelUAV Vision–Language–Action (HL-VLA) tasks that tests whether anagent can interpret concise language and execute complex, process-orientedtrajectories with safety awareness. HUGE-Bench comprises 4 real-worlddigital twin scenes, 8 high-level tasks, and 2.56M meters of trajecto-ries, and is built on an aligned 3D Gaussian Splatting (3DGS)–Mesh hy-brid representation that combines photorealistic rendering with collision-capable geometry for scalable generation and collision-aware evaluation.We introduce process-oriented and collision-aware metrics to assess pro-cess fidelity and flight safety. Experiments on representative state-of-the-art VLA models reveal significant gaps in high-level semantic completionand safe execution, highlighting HUGE-Bench as a diagnostic testbed forhigh-level UAV autonomy.

While Large Vision-Language Models (LVLMs) have signifi-cantly advanced GUI agents’ capabilities in parsing textual instructions,interpreting screen content, and executing tasks, a critical challenge per-sists: the irreversibility of agent operations—where a single erroneous ac-tion can trigger catastrophic deviations. To address this, we propose theGUI Action Critic’s Data Flywheel System (GAIA), a training frame-work that enables the models to have iterative critic capabilities, whichare used to improve the Test-Time Scaling (TTS) of basic GUI agents’performance. Specifically, we train an Intuitive Critic Model (ICM)using positive and negative action examples from a base agent first. Thiscritic evaluates the immediate correctness of the agent’s intended actions,thereby selecting operations with higher success probability. Then, theinitial critic guides agent actions to collect refined positive/negative sam-ples, initiating the self-improving cycle. The augmented data then trainsa second-round critic with enhanced discernment capability. We conductexperiments on various datasets and demonstrate that the proposed ICMcan improve the test-time performance of various closed-source and open-source models, and the performance can be gradually improved as thedata is recycled. The code, dataset, and accompanying datasheet will bepublicly released at https://github.com/SeerRay-Lab/GAIA.

Unsupervised Anomaly Detection (UAD) excels in real-worldapplications especially with rare anomalies due to the elimination of la-beled defective samples. Most existing UAD algorithms adopt a compar-ison process where the features of the test image are compared againsttheir normal “prototypes”. Such a paradigm relies primarily on raw fea-tures from pre-trained models and is inefficient due to the excessive num-ber of prototypes. To enhance UAD performance, recent algorithms aimto establish a mapping from the test features to their respective proto-types . However, the proposed mapping models, typically based on distil-lation or attention mechanisms, perform the “reconstruction” implicitly,making the process unnecessarily complex and difficult to explain. In thispaper, we propose a simple yet effective scheme, termed Fast DynamicPrototype (FDP), to rapidly generate high-quality reconstructed proto-types for Multi-class UAD. First, a raw prototype bank is obtained viasimply clustering the deep features of normal samples. Secondly, withinthe proposed Dynamic Bank Adapter module, the raw bank is dynam-ically condensed and adapted based on the information of the currenttest image. The reconstructed prototype of a given test feature is thendefined as the weighted average of its nearest bank members. Finally,an auxiliary FDP utilizing a different backbone model is introduced tosuccessfully mitigate the impact of artifacts present in the primary FDP.The study’s extensive experiments demonstrate the superiority of theproposed algorithm, which outperforms all compared SOTA methods onthree well-known UAD datasets (MVTec-AD, MVTec-3D, and ViSA),while attaining a speed up to 477 fps. Our implementation is publiclyavailable at https://github.com/lmlpy/FDP.git.

Offline-trained detectors and matchers often degrade in de-ployment when encountering previously unknown targets, since offlinetraining cannot anticipate the appearance and structural variations ofunknown targets. We propose EVEE, a feature adaptation frameworkthat leverages temporally accumulated event evidence as test-time proxysupervision for adapting detection and matching to previously unseentargets. Rather than performing fully asynchronous event-level match-ing or using events merely as auxiliary inputs, EVEE constructs event-derived proxy supervision to drive label-free online updates. EVEE con-tains three components. (i) Training Warehouse (TraW) accelerates con-vergence within a limited observation window via multi-threaded updatesand best-weight broadcasting. (ii) Event Voting Bank (EVB) accumu-lates bidirectional positive/negative votes to separate foreground frombackground, thereby reducing the impact of background noise on themodel. (iii) ReWeight improves correspondence reliability by rescoringcandidates using appearance similarity, geometric consistency, and EVBpriors. Experiments on public datasets and a real RGB–event platform,show that EVEE achieves 1.9% higher average pose ACC/AUC on un-known targets than strong baselines. Meanwhile, it runs at 51.3 FPSfor matching and 14.52 FPS for online adaptation. The code is publiclyavailable at https://github.com/ZeJZhao/EVEE.

Recent advances in text-to-video (T2V) diffusion models have demonstrated remarkable generative capabilities, yet their reliance on loosely curated training data raises pressing safety and copyright concerns. Concept erasure offers a principled remedy by removing unwanted semantics from pretrained models while preserving remaining concepts. However, existing approaches typically operate at a coarse granularity misaligned with the fine-grained, distributed nature of concept representations, leading to incomplete removal or degraded generation quality. We argue that surgical erasure fundamentally requires intervention at the level of monosemantic features, where each unit encodes a single interpretable concept. To this end, we propose EraseSAE, a novel framework that leverages sparse autoencoders to achieve surgical concept erasure in DiT-based T2V diffusion models via a principled decompose-attributeerase pipeline. We first introduce the Partitioned Convolutional Sparse Autoencoder, which decomposes dense spatiotemporal activations into disentangled, interpretable sparse features while preserving spatiotemporal coherence. A contrastive attribution mechanism then contrasts activations from paired prompts to isolate concept-specific feature kernels. At inference, timestep-resolved spatiotemporal masks derived from the identified kernels confine erasure to regions where the target concept is active, leaving unrelated content intact. Extensive experiments across diverse diffusion models and concept erasure tasks demonstrate that EraseSAE achieves precise and robust concept removal with minimal quality degradation, substantially outperforming state-of-the-art methods. The code is available at https://github.com/HiDream-ai/EraseSAE.

Object removal must prevent the masked target from reap-pearing and reconstruct the occluded background with structural andcontextual fidelity, rather than merely filling a hole plausibly. Recentdataset-free approaches manipulate the diffusion model’s internal self-attention to prevent it from referencing the masked region, yet they failin two critical ways: (i) they treat the masked region as the sole fore-ground, misinterpreting non-target objects as background and regener-ating them, and (ii) they apply uniform attention constraints withoutdistinguishing diverse background subtypes, leading to textural blurringand structural misalignment. Both failures stem from the absence of ex-plicit background-aware reasoning. We propose EraseLoRA, a dataset-free framework that replaces attention surgery with background-awarereasoning and test-time adaptation. The first stage, Background-awareForeground Exclusion (BFE), leverages a multimodal large-language modelto separate target foreground, non-target foregrounds, and clean back-ground from a single image-mask pair. The second stage, Background-aware Reconstruction with Subtype Aggregation (BRSA), performs test-time optimization that treats inferred background subtypes as comple-mentary pieces, enforcing their consistent integration through recon-struction and alignment objectives without explicit attention interven-tion. As a model-agnostic plug-in applicable to diverse diffusion back-bones, EraseLoRA reconstructs backgrounds at least 23% more faithfulto the original scene than previous dataset-free methods while nearlyhalving unwanted foreground re-generation, and surpasses all dataset-driven approaches in both aspects despite requiring no training data.Code is available at https://shjo-april.github.io/EraseLoRA.

We present E(3)-Pose, a novel fast pose estimation methodthat jointly and explicitly models rotation equivariance and object sym-metry. Our work is motivated by the challenging problem of accountingfor fetal head motion during a diagnostic MRI scan. We aim to enableautomatic adaptive prescription of diagnostic 2D MRI slices with 6-DoFhead pose estimation, supported by rapid low-resolution 3D MRI vol-umes acquired before each 2D slice. Existing pose estimation methodsstruggle to generalize to clinical volumes due to pose ambiguities inducedby inherent anatomical symmetries, as well as low resolution, noise, andartifacts. In contrast, E(3)-Pose captures anatomical symmetries andrigid pose equivariance by construction, and yields robust estimates ofthe fetal head pose. Our experiments on publicly available and represen-tative clinical fetal MRI datasets demonstrate the superior robustnessand generalization of our method across domains. Crucially, E(3)-Poseachieves state-of-the-art accuracy on clinical MRI volumes, supportingfuture clinical translation. Our implementation is publicly available1 .

Scene Change Detection (SCD) evaluates changes using pre-defined query-reference (i.e., present-past) image pairs. However, thisformulation overlooks a critical dependency: the corresponding query-reference pair is assumed to be prepared in advance. In real-world appli-cations, such as mobile robots, future query views cannot be known inadvance, and thus their corresponding reference images cannot be pre-defined. To remove this dependency and push change detection towardmore practical applications, we introduce Environmental Change Detec-tion (ECD). A key aspect of ECD is to avoid unrealistically predefinedand aligned query-reference pairs and instead retrieve environmental cuesfrom an uncurated image database of reference scenes. To tackle thisnew challenging task, we additionally introduce an initial solution thatenables change detection under unknown and imperfect query-referenceconditions. The main idea of our solution is to retrieve multiple referencecandidates and aggregate semantically rich representations for changedetection. We further construct ECD benchmark sets by reformulatingthree standard change detection datasets. Extensive experimental resultsdemonstrate the efficacy of our solution in both ECD and SCD.

Leveraging Multimodal Large Language Models (MLLMs)via contrastive learning has become a mainstream paradigm for improv-ing the performance of Universal Multimodal Retrieval (UMR). However,previous works have ignored the grain blindness when adapting thecontrastive paradigm into retrieval tasks. Grain blindness refers to thetendency of the model to overlook grain-level information contained inthe query, which is crucial for effectively handling complex queries. Thisstems from contrastive learning treating samples as a binary classifica-tion (positive/negative), while ignoring the different information carriedby each negative sample. To address this, we argue that negatives shouldbe treated differently according to their similarity to the positive sample,enabling the model to learn distinct grain information from each nega-tive. In this paper, we introduce a simple but effective framework, calledELVA, a novel rule-based RL framework that mitigates grain blindnessthrough ranking-driven MLLMs. 1) Instead of relying on reward mod-els, we extend Reinforcement Learning with Verifiable Rewards (RLVR)to retrieval tasks, allowing the model to explore new ranking behav-iors without explicit ranking labels. 2) By utilizing rule-based rewards,our approach jointly optimizes the ranking of negative samples whileenlarging the similarity gap between positive and negative. To more pre-cisely measure grain blindness, we further introduce MRBench, a newbenchmark specifically designed for multi-grain query scenarios. ELVAachieves state-of-the-art results across standard retrieval benchmarks,and its notable 13.1% improvement on MRBench further demonstratesits effectiveness in alleviating grain blindness. Our code is available athttps://github.com/SeerRay-Lab/ELVA.

Sports video analysis is a challenging domain for multimodalunderstanding because it involves complex and dynamic human activi-ties. Despite rapid progress in Multimodal Large Language Models (MLLMs),long-horizon reasoning in sports videos remains difficult, as answeringquestions requires both locating and integrating temporally sparse evi-dence into reasoning. We attribute this limitation to two closely relatedfactors: insufficient supervision over temporally dispersed evidence andthe lack of methods for explicit temporal evidence localization and justi-fication. To address these gaps, we introduce SportsTime, a large-scalebenchmark for long-form sports video understanding, comprising 14K+open-ended QA pairs and 50K+ step-wise temporal evidence annota-tions. Building on SportsTime, we propose Chain-of-Time Reasoning(CoTR), which treats reasoning as a process of temporally groundedevidence composition. Specifically, during training, CoTR introduces atemporal-reward GRPO to encourage temporally grounded reasoning.During inference, it employs an anchor-observe-infer evidence-seekingloop to iteratively localize, verify, and compose temporal evidence beforeproducing the final answer. Experiments show that SportsTime exposessubstantial gaps in current MLLMs, while CoTR yields consistent gainsover strong baselines, improving both temporal compositional reasoningperformance and step-wise grounding quality. The dataset and code areavailable at https://github.com/ustiniansy/SportsTime.

Pruning visual foundation models has attracted considerableattention. However, existing methods focus on rigid point-to-point tokenalignment on a single dataset for pruning, suffering from two limitations:i) robustness degradation, and ii) task-specificity deficiency. To addressthese limitations, we propose a task-specific pruning pipeline, namedCut-ViT. Specifically, we first construct gram anchoring matrices fromboth spatial and semantic perspectives, and perform the subspace de-composition to extract the corresponding subspace bases. Basis-agnosticand residual constraints are then adopted to align the gram subspaces be-tween the native and pruned DINOv3 models along spatial and channeldimensions, enabling subnetworks to inherit robust feature representa-tions of native DINOv3. Furthermore, we design spectral entropy adapta-tion, which quantifies the information density of feature manifolds alongspatial and channel dimensions, thereby adapting the pruning objectiveto specific downstream tasks. Experiments show that Cut-ViT requiresapproximately one minute on a single A100 GPU to obtain subnetworksat various sparsity levels, using only 20.9% of the time and 45.5% of theGPU memory compared with previous methods, while achieving SOTAperformance on six tasks across nine datasets.

Large vision-language models (LVLMs) have recently achievedremarkable progress across multimodal tasks, yet object hallucinationremains a persistent challenge where models generate descriptions in-consistent with the visual input. Recent work mitigates hallucinationsthrough training-free representation editing, typically by constructinghallucination-related directions from teacher-forcing (TF) contrasts be-tween hallucinated and truthful responses. However, LVLMs operatethrough autoregressive (AR) decoding during generation, raising thequestion of whether TF-based analysis fully reflects the generation dy-namics that lead to hallucinated outputs. In this paper, we analyzethe relationship between TF-based editing and AR generation behaviorand find that TF-based editing alone may be insu!cient to captureboth decoding dynamics and multimodal interactions associated withhallucinations. To address this limitation, we propose DARE (Dual-path Auto-Regressive-aware Editing), a hybrid hallucination editingframework that integrates two complementary contrast pathways: textualcontrasts and image contrasts, together with autoregressive-aware rep-resentation signals. Specifically, DARE constructs hallucination editingdirections from (1) TF-based textual contrasts, (2) AR-aware represen-tation transitions during decoding, and (3) controlled visual di"erencesbetween paired images. Extensive experiments on multiple LVLM hal-lucination benchmarks demonstrate that DARE consistently reducesobject hallucinations while preserving multimodal perception capabil-ity and inference e!ciency. Our implementation code is available athttps://github.com/KU-VGI/DARE.

On-device vision-language models (VLMs) deployed on edgedevices often encounter distribution shifts between pre-training data andtest data, which can significantly degrade their performance. To miti-gate this degradation, device-server hybrid inference allows an on-deviceVLM to delegate uncertain test inputs to a more powerful server-sideVLM. However, frequently delegating to the remote server at test timeinflates data-transfer costs and server-call latency, limiting the practi-cality of hybrid inference in real-world applications. Thus, to reduce thedelegation frequency without sacrificing the benefits of server assistance,we present CURE, a cumulative knowledge reuse framework for device-server hybrid inference, which efficiently enhances on-device VLMs byaccumulating and reusing the server-side VLM’s knowledge. As a result,CURE enables the on-device VLM to approximate server-level predic-tions with far fewer delegations, thereby amortizing both data-transfercosts and server-call latency across the test stream. Extensive experi-ments across 13 datasets show that CURE closely matches the accuracyof an always-delegate-to-server baseline, reduces delegation frequency by66.74% on average, and improves end-to-end runtime by up to 3.67×,demonstrating the practicality of CURE for real-world deployment.

Large Multimodal Models (LMMs) show strong few-shotgeneralization, but industrial anomaly detection remains difficult becausedefects are small, input resolution is limited, and textual standardsare not always grounded in visual evidence. Recent optimization-basedmethods improve alignment through fine-tuning, but they often requiremany defective samples, which are unavailable in early deployment. Wepresent Global Logic and Local Search (GLLS), a training-freeframework for reference-guided multimodal in-context verification. GLLSuses a Part-Aware Visual-Logical Atlas to organize normal references andstructured specifications in the inference context. It combines a Global& Logic Stream, where SAM 3 extracts partially checkable visual facts,with a Fine-Grained & Actions Stream, where MCTS selects localevidence crops under a fixed budget. Experiments on MMAD-QA andadditional anomaly detection datasets show consistent gains over matchedand general-purpose baselines, while keeping the final diagnostic decisiontraceable to explicit visual evidence throughout the inspection trace.

Diffusion models generate conditional samples by progres-sively denoising Gaussian noise, yet the denoising trajectory can stall atvisually plausible but low-quality outcomes with conditional misalign-ment or structural artifacts. We interpret this behavior as local optimain a surrogate quality landscape: Once early denoising commits to a sub-optimal global structure, later steps mainly sharpen details and seldomcorrect the underlying mistake. While existing inference-time approachesexplore alternative diffusion states via re-noising with fixed strength ordirection, they exhibit limited capacity to escape steep quality plateaus.We propose Controlled Random Zigzag Sampling (Ctrl-Z Sampling), ascalable sampling strategy that detects plateaus in quality landscapevia a surrogate score, and allocates exploration only when a plateau isdetected. Upon detection, Ctrl-Z Sampling rolls back to noisier states,samples a set of alternative continuations, and updates the trajectorywhen a candidate improves the score, otherwise escalating the explo-ration depth to escape the current plateau. The proposed method ismodel-agnostic and broadly compatible with existing diffusion frame-works. Experiments show that Ctrl-Z Sampling consistently improvesgeneration quality over other inference-time scaling samplers across dif-ferent NFE budgets, offering a scalable compute-quality trade-off. Codeavailable at: https://github.com/ShunqiM/Ctrl-Z-Sampling.

Movie dubbing aims to synthesize speech that preserves thevocal identity of a reference audio while synchronizing with the lip move-ments in a target video. Existing methods fail to achieve precise lip-syncand lack naturalness due to explicit alignment at the duration level.While implicit alignment solutions have emerged, they remain suscep-tible to interference from the reference audio, triggering pronunciationand lip-sync degradation in in-the-wild scenarios. In this paper, we pro-pose a novel flow matching-based movie dubbing framework driven bythe Cognitive Synchronous Diffusion Transformer (CoSyncDiT), in-spired by the cognitive process of professional actors. This architectureprogressively guides the noise-to-speech generative trajectory by execut-ing acoustic style adapting, fine-grained visual calibrating, and time-aware context aligning. Furthermore, we design the Joint Semantic andAlignment Regularization (JSAR) mechanism to simultaneously con-strain frame-level temporal consistency on the contextual outputs andsemantic consistency on the flow hidden states, ensuring robust align-ment. Extensive experiments on both standard benchmarks and chal-lenging in-the-wild dubbing benchmarks demonstrate that our methodachieves the state-of-the-art performance across multiple metrics. Thecode is available at https://github.com/GalaxyCong/CoSyncDiT.

Joint image–feature generative modeling has recently emergedas an effective strategy for improving diffusion training by coupling low-level VAE latents with high-level semantic features extracted from pre-trained visual encoders. However, existing approaches rely on a fixedrepresentation space, constructed independently of the generative objec-tive and kept unchanged during training. We argue that the represen-tation space guiding diffusion should itself adapt to the generative task.To this end, we propose Coevolving Representation Diffusion (CoReDi),a framework in which the semantic representation space evolves duringtraining by learning a lightweight linear projection jointly with the diffu-sion model. While naïvely optimizing this projection leads to degeneratesolutions, we show that stable coevolution can be achieved through acombination of stop-gradient targets, normalization, and targeted reg-ularization that prevents feature collapse. This formulation enables thesemantic space to progressively specialize to the needs of image synthesis,improving its complementarity with image latents. We apply CoReDi toboth VAE latent diffusion and pixel-space diffusion, demonstrating thatadaptive semantic representations improve generative modeling acrossboth settings. Experiments show that CoReDi achieves faster convergenceand higher sample quality compared to joint diffusion models operatingin fixed representation spaces.

Weakly Supervised Video Anomaly Detection aims to detect abnor-mal events in long, untrimmed videos using only video-level supervision, provid-ing a practical solution to the high cost of temporally localized annotations. How-ever, abnormal events in real-world surveillance are often determined by complexcontextual interactions among actions, objects, and environments, making it dif-ficult for existing approaches to identify which factors actually cause an anomaly.Most prior methods rely on holistic representations or explanations that lack ex-plicit attribution to the semantic factors driving abnormal events. In this paper,we present CLUE-VAD, a structured semantic decomposition framework for ex-plainable weakly supervised video anomaly detection. Rather than relying solelyon holistic video representations or free-form explanations, CLUE-VAD decom-poses each video segment into structured semantic clues—Action, Environment,and Object—and estimates their category-aware contributions to anomaly predic-tion. By linking anomaly scores to clue-specific weights and keyword-level evi-dence, we provides factor-level attribution and supports transparent explanationgeneration. Extensive experiments on VAD benchmarks demonstrate that CLUE-VAD achieves strong detection performance among X-VAD methods while pro-viding fine-grained, clue-grounded explanations for anomaly predictions. Theproject page is available at https://visualsciencelab-khu.github.io/CLUE-VAD_project/.

We propose MotionAtlas, a system for detailed captioningof motion-centric videos, comprising (1) a dedicated human-annotatedbenchmark, (2) a scalable, high-quality pipeline to construct trainingsamples, and (3) a family of powerful Video-MLLMs. Unlike conventionalglobal motion captioning datasets, we focus on region-aware motion cap-tioning: given a video and a spatiotemporal mask, the model generatesprecise descriptions of motion within the target region, thereby alleviatingvisual clutter and motion entanglement and enabling reliable, quantifi-able evaluation. Concretely, we first build MotionAtlas-Bench, a compre-hensive benchmark comprising 2,073 multiple-choice questions, meticu-lously annotated for a curated set of high-quality, motion-centric videos,to evaluate fine-grained motion understanding of the objects in question.Second, we design a rigorous and scalable data pipeline that leveragesself-bootstrap refinement to suppress fine-grained hallucinations, yield-ing 159k high-quality motion captioning data. Third, we design a tai-lored training data composition strategy, which achieves consistent andsubstantial performance gains across diverse baseline Video-MLLMs, in-cluding Molmo2 and Qwen3-VL. For instance, MotionAtlas-4B surpassesQwen3-VL-4B by an average of 5.2 percentage points across general mo-tion benchmarks. The benchmark, dataset, and code have been released.