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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

Equivariant Symmetry-Aware Head Pose Estimation for Fetal MRI

Ramya Muthukrishnan, Borjan Gagoski, Aryn Lee, Ellen Grant, Elfar Adalsteinsson, Benjamin Billot, Polina Golland

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 .

ECCV 2026

Environmental Change Detection for Real-World Change Analysis

Kyusik Cho, Suhan Woo, Hongje Seong, Euntai Kim

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.

ECCV 2026

ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval

Yuhan Liu, Pei Fu, Hang Li, Yukun Qi, Chao Jiang, Jingwen Fu, Zhen Liu, Bin Qin, Zhenbo Luo, Jian Luan, Jingmin Xin

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.

ECCV 2026

Towards Temporal Compositional Reasoning in Long-Form Sports Videos

Siyu Cao, Lu Zhang, Ruizhe Zeng, Zhi-yong Liu

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.

ECCV 2026

EffiDINO: Task-Specific Model Pruning via Gram Anchoring Subspace Consistency

Jianjian Yin, Liulei Li, Tao Chen, Yi Chen, Yazhou Yao, Wenguan Wang

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.

ECCV 2026

DARE to Mitigate Hallucination: Dual-path Auto-Regressive-aware Editing

Jaeho Lee, Jeongeun Lee, Gyeong-Moon Park

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.

ECCV 2026

CURE: Cumulative Knowledge Reuse for Efficient Device-Server Hybrid Inference in Vision-Language Models

Youngjun Lee, Doyoung Kim, Junhyeok Kang, Hwanjun Song, Jae-Gil Lee

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.

ECCV 2026

Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection

Runzhi Deng, Yundi Hu, Yiming Zhong, Zhao Wang, Xixi Liu, Hongsong Wang, Caifeng Shan, Fang Zhao

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.

ECCV 2026

Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations

Shunqi Mao, Wei Guo, Chaoyi Zhang, Jieting Long, Ke Xie, Weidong Cai

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.

ECCV 2026

CoSyncDiT: Cognitive Synchronous Diffusion Transformer for Movie Dubbing

Gaoxiang Cong, Liang Li, Jiaxin Ye, Zhedong Zhang, Hongming Shan, Yuankai Qi, Qingming Huang

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.

ECCV 2026

Co-evolving Representations in Joint Image-Feature Diffusion

Theodoros Kouzelis, Spyros Gidaris, Nikos Komodakis

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.

ECCV 2026

CLUE-VAD: Structured Semantic Clues for Understanding Explainable Events in Video Anomaly Detection

MYOUNG-CHUL KIM, Junhee Lee, ChaeBeen Bang, MyeongAh Cho

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/.

ECCV 2026

MotionAtlas: A High-Quality Dataset and Benchmark for Dense Motion Captioning

Weisong Liu, Haochen Wang, Gaokuan Gaokuan, Yuhao Wang, Yikang Zhou, Zhongwei Ren, Guangcan Mai, Anran Wang, Yanwei Li, Xiangtai Li, Zhaoxiang Zhang

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.

ECCV 2026

CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection

Wen Dong, Zhao Wang, Shuangqing Zhang, Kai Sun, Ben Li, Guosen Xie, Caifeng Shan, Fang Zhao

Multimodal Large Language Models (MLLMs) excel in di-verse vision tasks, but full-parameter retraining is computationally ex-pensive as real-world knowledge evolves. Existing continual learning meth-ods often suffer from semantic entanglement in parameter spaces acrosstasks, impeding the continuous deployment of models. This challenge isespecially pronounced in Anomaly Detection (AD), which exhibits tripleheterogeneity across modalities, domains, and defect scale variability,significantly complicating multi-task knowledge transfer. In this paper,we propose CL-Anomaly, a parameter-efficient fine-tuning frameworkbased on an isolation-sharing collaboration to enable continual learningfor anomaly detection with MLLMs. We introduce the task-private ex-pert PrivLoRA, which physically isolates task-specific subspaces in theparameter space to prevent semantic entanglement of anomaly knowl-edge in diverse scenarios. The Layer-Adaptive Shared Experts maintaincross-task representations within a unified feature space, enabling knowl-edge sharing between previous and new tasks. Furthermore, we proposea Layer-Adaptive Knowledge Transfer strategy that automatically se-lects and dynamically updates the layer-wise key shared experts of eachtask via a momentum-based mechanism, promoting effective knowledgetransfer across related anomaly detection tasks. Extensive experimentsacross three continual learning scenarios for anomaly detection, includ-ing class-incremental, cross-domain, and cross-modal, demonstrate thatCL-Anomaly outperforms state-of-the-art methods. Code is available athttps://github.com/WenDongyp/CL-Anomaly.

ECCV 2026

Generalize LMMs to Versatile Visual Modalities via Fabricated Modality Synthesis

Shihao Yuan, Yuanze Li, Ruyi Zhang, Ming Liu, Wangmeng Zuo

Despite the advancements of Large Multimodal Models (LMMs)in RGB vision, their ability to generalize to unseen visual modalitiesremains a largely unexplored challenge. We argue that different visualmodalities are merely distinct samplings of the same physical world.Therefore, effective generalization requires models to possess both modality-agnostic perception of scene semantics and the adaptability to modality-specific characteristics. To achieve this, we propose a training frame-work, VVM-Tuning, to equip LMMs with these capabilities throughmodality synthesis and modality contexts. Specifically, we synthesize di-verse appearance-varied images from RGB scenes, training the model todisentangle invariant semantics from varying visual appearances, andalign these appearances with language for visual concepts decoupledfrom modalities. We then introduce modality contexts in the promptand use instruction tuning to assist the model in mapping these appear-ance variations back to modality-related attributes, enabling zero-shotadaptation to unseen modalities during inference. To facilitate researchin this direction, we introduce VVM-Bench, a comprehensive bench-mark featuring 6 real and synthetic modalities to evaluate semantic per-ception and modality understanding. Experiments demonstrate that, viaour training on synthetic modalities, 5 tested models exhibit consistentimprovements on both real-world and novel synthetic modalities withoutin-modality training. Source code and data will be publicly available athttps://github.com/Hunter-Will/VVM-Tuning.

ECCV 2026

SVG-EAR: Parameter-Free Linear Compensation for Sparse Video Generation via Error-aware Routing

Xuanyi Zhou, Qiuyang Mang, Shuo Yang, Haocheng Xi, Jintao Zhang, Huanzhi Mao, Joseph E Gonzalez, Kurt Keutzer, Ion Stoica, Alvin Cheung

Diffusion Transformers (DiTs) have become a leading back-bone for video generation, yet their quadratic attention cost remains amajor bottleneck. Sparse attention reduces this cost by computing only asubset of attention blocks. However, prior methods often either drop theremaining blocks which incurs information loss, or rely on learned predic-tors to approximate them, introducing training overhead and potentialoutput distribution shifting. In this paper, we show that the missingcontributions can be recovered without training: after semantic cluster-ing, keys and values within each block exhibit strong similarity and canbe well summarized by a small set of cluster centroids. Based on thisobservation, we introduce SVG-EAR, a parameter-free linear compensa-tion branch that uses the centroid to approximate skipped blocks andrecover their contributions. While centroid compensation is accurate formost blocks, it can fail on a small subset. Standard sparsification typi-cally selects blocks by attention scores, which indicate where the modelplaces its attention mass, but not where the approximation error wouldbe largest. SVG-EAR therefore performs error-aware routing: a lightweightprobe estimates the compensation error for each block, and we computeexactly the blocks with the highest error-to-cost ratio while compensatingfor skipped blocks. We provide theoretical guarantees that relate atten-tion reconstruction error to clustering quality, and empirically show thatSVG-EAR improves the quality-efficiency trade-off and increases through-put at the same generation fidelity on video diffusion tasks. Overall,SVG-EAR establishes a clear Pareto frontier over prior approaches, achiev-ing up to 1.77× and 1.93× speedups while maintaining PSNRs of up to29.759 and 31.043 on Wan2.2 and HunyuanVideo, respectively.

ECCV 2026

Bridging Vision and Language Concepts through Optimal Transport Semantic Flow

Chenyang Zhang, Anqi Dong, Guangming Zhu, Nuoye Xiong, Siyuan Wang, Lin Mei, Liang Zhang

Concept Bottleneck Models (CBMs) promise transparent rea-soning by predicting through human-interpretable concepts, yet theireffectiveness fundamentally depends on how well visual and textual rep-resentations are aligned or matched. Existing vision–language CBMs of-ten rely on pre-aligned encoders or global cosine similarity, which ob-scures fine-grained concept localization and fails to reflect true seman-tic geometry. In this work, we rethink concept alignment as dynamiccross-modal transport process instead of static projection and proposeOptimal Transport Flow Concept Bottleneck Model (OTF-CBM). Itfirst learns a data-driven semantic cost via Inverse Optimal Transport tomeasure cross-modal distances, and then performs unbalanced optimal-transport-based flow matching to model semantic transitions betweenvisual patches and textual concepts. With velocity-based concept activa-tion, OTF-CBM captures interpretable geometric relations without ODEintegration. Experiments further show that OTF-CBM achieves greatclassification accuracy and concept faithfulness, offering a new geomet-ric and dynamical perspective for interpretable cross-modal reasoning.Our code can be found at github.com/ChenyangZhang00/OTF-CBM.

ECCV 2026

BLOB-Q: Boosting Low Bit ViT Quantization via Global Optimization on Model Distortion

Wang Mark, Kaixin Xu, Xue Geng, Fen Fang, Mohamed Aly, Xulei Yang, Min Wu, Weisi Lin

In this paper, we present BLOB-Q, a novel Mixed-PrecisionPost Training Quantization (MPQ) approach for Vision Transformers(ViTs). MPQ aims to assign different bit-widths across layers under amodel size budget. Most existing MPQ methods either rely on layerwisesensitivity heuristics or surrogate objectives. These approaches simplifyoptimization, but they no longer optimize the same model-level objec-tive that determines accuracy. Others solve the global MPQ problemby non-heuristic and non-analytical algorithms, such as ReinforcementLearning, genetic algorithms or gradient-based learning. However, theyare inefficient for modern large vision transformers. In this work, we revisitthe global MPQ problem and show that this problem can be solved byefficient analytical algorithms while still maintaining its global-optimality.This comes from two key insights. Firstly, through systematic signalanalysis on quantization errors, we discover that under 4„6-bit range,the quantization error on converged ViTs are indeed small perturbations.This encourages that the global MPQ objective is possible to be quadrat-ically decomposed for empirical ViT models. Secondly, motivated by thesmall perturbation observation, we further discover that the global MPQobjective satisfies an additivity property. Utilizing the additivity property,the NP-hard global MPQ optimization problem can be decomposed intosub-problems and solved practically, while still retain the global optimal-ity. Specifically, we solve the decomposed analytical optimization problemusing dynamic programming algorithm, which efficiently find the globallyoptimal solution with only linear time complexity. Extensive experimentson numerous ViT models demonstrate the effectiveness of our approach.Results show that BLOB-Q significantly improves state-of-the-art andcan further reduce the size of ViT models to 4 bits to 6 bits withouthurting ImageNet accuracy. Moreover, BLOB-Q is highly efficient, onlyrequiring on average sub-2 minutes for regular ViT-S to ViT-B models.

ECCV 2026

Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation

Weihao Yan, Yeqiang Qian, Yi Dong, Ming Yang

Precise medical image segmentation is crucial for clinical di-agnosis and treatment planning, yet relies heavily on expensive expertannotations. Semi-supervised medical image segmentation (SSMIS) of-fers a cost-effective solution but typically operates under the assump-tion of independent and identically distributed (i.i.d.) data, defaultingto random sampling. While statistically valid at scale, this strategy suf-fers from severe representation bias in low-data regimes, failing to cap-ture the heterogeneous medical data manifold. To address this, we pro-pose a highly data-efficient framework driven by distribution alignment.First, we introduce an offline Distribution-Aware Sample Selection strat-egy. By leveraging Vision Foundation Models (VFMs) and our designedDensity-K-Center algorithm, we explicitly identify representative struc-tural anchors, establishing a more representative labeled domain. Second,to bridge the remaining distribution gap, we propose the Memory-guidedCopy-Paste (MCP) module. Tailored for the inherent class imbalance inmedical scans, MCP leverages a semantic memory mechanism to retrievehistorically consistent priors for cross-domain alignment, encouragingsemantic consistency. Coupled with an easy-to-hard progressive sched-ule, this framework effectively mitigates early-stage pseudo-label noise.Extensive experiments on six diverse 2D and 3D datasets demonstratestrong segmentation performance, particularly in extremely low-labeledscenarios (e.g., 1/16 ratio).

ECCV 2026

Beyond Prompts: Unconditional 3D Inversion for Out-of-Distribution Shapes

Victoria Chen, Emery Pierson, Léopold Maillard, Maks Ovsjanikov

Text-driven inversion of generative models is a core paradigmfor manipulating 2D or 3D content, unlocking numerous applicationssuch as text-based editing, style transfer, or inverse problems. How-ever, it relies on the assumption that generative models remain sen-sitive to natural language prompts. We demonstrate that for state-of-the-art native text-to-3D generative models, this assumption often col-lapses. We identify a critical failure mode where generation trajectoriesare drawn into latent “sink traps”: regions where the model becomesinsensitive to prompt modifications. In these regimes, changes to theinput text fail to alter internal representations in a way that altersthe output geometry. Crucially, we observe that this is not a limita-tion of the model’s geometric expressivity; the same generative mod-els possess the ability to produce a vast diversity of shapes but, as wedemonstrate, become insensitive to out-of-distribution text guidance. Weinvestigate this behavior by analyzing the sampling trajectories of thegenerative model, and find that complex geometries can still be repre-sented and produced by leveraging the model’s unconditional generativeprior. This leads to a more robust framework for text-based 3D shapeediting that bypasses latent sinks by decoupling a model’s geometricrepresentation power from its linguistic sensitivity. Our approach ad-dresses the limitations of current 3D pipelines and enables high-fidelitysemantic manipulation of out-of-distribution 3D shapes. Project web-page: https://daidedou.sorpi.fr/publication/beyondprompts

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