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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

SDUM: A Scalable Deep Unrolled Model for Universal Cardiac MRI Reconstruction

Puyang Wang, Pengfei Guo, Keyi Chai, Jinyuan Zhou, Daguang Xu, Shanshan Jiang

Clinical cardiac MRI spans diverse contrasts, sampling tra-jectories, accelerations, scanners, and patient populations, yet many de-terministic deep-learning reconstruction methods remain protocol spe-cific. We present the Scalable Deep Unrolled Model (SDUM), which in-tegrates a Restormer-based unrolled reconstructor, per-cascade coil sen-sitivity estimation, sampling aware weighted data consistency, and uni-versal conditioning on cascade index and acquisition metadata. A sin-gle SDUM model achieves state-of-the-art performance across all CM-RxRecon2025 tracks without task-specific fine-tuning and outperformsPromptMR+ on CMRxRecon2024 by +0.55 dB. Scaling experimentsshow near-logarithmic gains with depth up to 18 cascades (r=0.986,R2 =0.973) and continued but diminishing gains from data scaling (32.72 dBat 40% to 33.18 dB at 100%). SDUM also generalizes in a zero-shot set-ting to unseen in-house chemical exchange saturation transfer (CEST)MRI (43.57 dB PSNR, 0.9769 SSIM). When trained separately on fastMRIbrain, SDUM surpasses PC-RNN by +1.8 dB. These results supportSDUM as a scalable framework for robust MRI reconstruction acrossheterogeneous acquisition settings beyond cardiac MRI.Code: https://github.com/NVIDIA-Medtech/NV-Raw2insights-MRIModel: https://huggingface.co/nvidia/NV-Raw2insights-MRI

ECCV 2026

SciIR: A Large-scale Training Dataset and Benchmark for Scientific Image Reasoning Generation

Zhiyuan Ma, Zhengfeng Shi, Yuning An, Peize Li, Jiabao Wei, Ruijie Li, Junhao Xiao, Jianjun Li, Bowen Zhou

While Text-to-Image (T2I) models have shown remarkablesuccess in generating photorealistic visual content, they still strugglewith the rigorous semantic alignment and logical reasoning requiredfor scientific imagery. Inspired by Peirce’s Semiotic Triad, we introduceScientific Image Reasoning (SciIR), a comprehensive resource for trainingand evaluation of scientific image generation. We formalize scientificreasoning into three core dimensions: Entity Structure (Icon), ScientificProcess (Index ), and Scientific Law (Symbol ). Specifically, to overcomethe scarcity of training data in scientific image generation, we elaboratelycreate SciIR-82k, a large-scale dataset containing over 80,000 high-qualityscientific image-text pairs from cutting-edge publications. The datasetis hierarchically organized according to the semiotic dimensions andincorporates a Scientific Reasoning Chain-of-Thought (Sci-RCoT) toexplicitly model underlying visual logic. For evaluation, we propose SciIR-Bench, which aligns with these three semiotic levels and employs anAtomic Checklist to convert the outcome-oriented scientific accuracyinto process-oriented, verifiable, fine-grained questions. Our extensiveexperiments reveal significant deficiencies in current models’ scientificreasoning capabilities. Furthermore, by fine-tuning on the SciIR-82kdataset, we developed the Qwen-Image-SciIR model, which achieves asubstantial improvement on the SciIR-Bench, increasing the final scorefrom 35% to 43%, laying a solid foundation for future advances in scientificimage generation.

ECCV 2026

Rethink Backdoor Robustness in Vision Transformers

Yichuan Mo, Dongxian Wu, Yifei Wang, Yisen Wang

Backdoor attacks, which induce Convolutional Neural Net-works (CNNs) to behave maliciously when a predefined trigger is present,pose serious security risks. While such threats also extend to VisionTransformers (ViTs), previous studies have suggested that existing back-door attacks remain highly effective on ViTs and can evade commondefense mechanisms—often without significantly compromising accuracy.In this paper, we revisit this claim and demonstrate that such con-clusions are overly optimistic, largely due to inadequate adaptation ofCNN-based defenses to ViTs. We show that, with proper adjustments,existing backdoor attacks can in fact be effectively mitigated. More-over, we propose a more robust attack strategy: by introducing slightperturbations to the trigger, existing attacks can be made significantlymore resistant to various defenses. We hope that our findings—bothon the defensibility of current attacks with correct adaptations and theproposed enhanced attack—will inspire deeper investigation into thebackdoor robustness of Vision Transformers. Our code is available athttps://github.com/PKU-ML/ViT_backdoor.

ECCV 2026

PrintAnything: Learning Geometric Plan Map for 3D Printing G-code Generation from Unoriented Point Clouds

Sangmin Hong, Daniel Sungho Jung, Heewon Kim, Kyoung Mu Lee

Point clouds are one of the most fundamental and widelyused 3D representations, serving as the most basic geometric represen-tation of 3D shapes. Nevertheless, most existing 3D printing pipelinesrequire a watertight mesh as input, preventing the direct use of pointclouds for fabrication. A common workaround is to reconstruct meshesfrom point clouds; however, the resulting meshes often contain geomet-ric artifacts, such as incorrect faces or topological inconsistencies, thatare difficult to repair and may lead to printing failures. To overcomethese limitations, we propose PrintAnything, a novel framework thatlearns to produce executable 3D printing G-code directly from 3D pointclouds without requiring mesh reconstruction. To enable point cloudsto serve as direct input for slice-wise toolpath generation, we introducea slice-wise point projection strategy that transforms unstructured 3Dpoint clouds into slice-aligned 2D representations consistent with layer-by-layer nature of fused deposition modeling in 3D printing. To eliminatemesh dependency and provide a unified representation that bridges pointclouds and G-code, we propose Geometric plan (G-plan) map, a com-pact 2D representation composed of occupancy, region, and flow mapsthat encode the geometric and extrusion properties required for toolpathsynthesis in 3D printing. As a result, our proposed method accuratelygenerates printable G-code directly from point clouds, enabling a prac-tical and fully mesh-free pipeline for 3D printing. The code is publiclyavailable at https://github.com/Sangminhong/PrintAnything.

ECCV 2026

Physics Meets Perception: A Reinforcement Learning Framework for Unpaired Real-World Image Dehazing

Yunwei Lan, Zhigao Cui, Chang Liu, Menglin Zhang, Nian Wang, Cong Zhang, Dong Liu

Real-world image dehazing remains a challenging problem due to the significant domain gap between synthetic training data and natural haze, often leading to poor generalization. While unsupervised Generative Adversarial Networks (GANs) attempt to bridge this gap, they frequently suffer from training instability and hallucinatory artifacts. Recently, Reinforcement Learning (RL) has emerged as a promising alternative; however, current RL-based restoration paradigms predominantly rely on diffusion models, leading to prohibitive computational costs and ineffective exploration. To address these bottlenecks, we propose Dehaze-RL, an efficient framework tailored for unpaired real-world dehazing. Bypassing the expensive iterative sampling of diffusion models, we design an efficient policy network to predict hybrid actions in a Physics Hybrid Action Space. By explicitly estimating physical parameters, enhancement factors, and a gating policy map, we anchor the agent’s exploration in reliable physical priors, effectively resolving the exploration dilemma. These components are dynamically integrated via a gating fusion mechanism. Furthermore, we introduce a Multi-Granularity Reward Mechanism to provide comprehensive feedback, seamlessly aligning the outputs with human perception without structural distortions. Extensive experiments demonstrate that Dehaze-RL outperforms stateof-the-art methods in both visual fidelity and quantitative metrics.

ECCV 2026

NavWM: A Unified Navigation World Model for Foresight-Driven Planning

Yanghong Mei, Longteng Guo, MingMing Yu, Guiyu Zhao, Xingjian He, Jing Liu

Conventional visual navigation policies often struggle withmyopic decision-making and mode collapse in complex environments.While world models offer a promising alternative, existing paradigms typ-ically isolate perception, generation, and control, failing to capture theirshared spatio-temporal dynamics. In this paper, we propose NavWM, aunified navigation world model that seamlessly integrates latent worldreasoning, multimodal action prediction, and controllable visual gener-ation. At its core, NavWM leverages latent world tokens to distill geo-metric and semantic priors, endowing the agent with robust structuralunderstanding. To overcome the limitations of deterministic policies, weintroduce an anchor-based multimodal trajectory forecasting frameworkthat generates a diverse action space. This inherent diversity explicitlyempowers the generative world model to act as a robust closed-loop plan-ner, utilizing visual foresight to evaluate and select the optimal path.Extensive experiments across diverse robotics datasets demonstrate thatNavWM significantly advances the state-of-the-art, delivering remark-able improvements in both high-fidelity future state generation and zero-shot navigation success.

ECCV 2026

From Reasoning to Pixels: Grounded Medical Multimodal LLMs for VQA and Segmentation

Haowen Gu, Gensheng Pei, Junzhu Mao, Qiong Wang, Mingwu Ren, Yazhou Yao

Although Multimodal Large Language Models (MLLMs) havedemonstrated impressive performance in Medical Visual Question An-swering (Med-VQA), their reliance on global image features often lacksprecise pixel-level grounding, thereby limiting clinical trustworthiness.To bridge the semantic gap between high-level clinical reasoning andspatial localization, we propose MedREAL (Medical REasoning-drivenAnswering and Localization), a unified framework that seamlessly alignslinguistic reasoning with spatial grounding. Specifically, MedREAL in-troduces Seg Anchored Reasoning Pooling (SARP) to distill task-relevantsemantic evidence directly from [SEG] tokens within the MLLM’s hid-den states. Furthermore, a Reasoning-to-Visual (R2V) fusion mecha-nism is proposed to effectively inject these reasoning-aware features intoa segmentation pipeline for accurate mask decoding. To facilitate thisparadigm, we construct MedRAVS-13K, a comprehensive dataset com-prising 13,824 expertly validated samples across four diverse imagingmodalities. Extensive experiments demonstrate that MedREAL signifi-cantly outperforms state-of-the-arts, achieving 68.49% gIoU and 70.47%cIoU on benchmark evaluations. By generating evidence masks that arestrictly consistent with textual diagnoses, MedREAL provides a robust,interpretable framework for reasoning-driven medical image analysis.

ECCV 2026

Mimicking Radiologists: A Coarse-to-Fine Framework with Structural Sparse Tokens for Dual-LLM Computed Tomography Report Generation

Hong Liu, Dong Wei, Yefeng Zheng, Xian Wu, Liansheng Wang

Computed tomography report generation (CTRG) automatesradiology reporting to reduce clinical workload and facilitate patient care.Recent efforts in applying the rapidly developing large language mod-els (LLMs) have advanced the field; yet, they still face a fundamentalchallenge due to the large volume of 3D data: effectively reducing highfeature redundancy and computational burden while simultaneously ex-tracting information-rich representations. To address this challenge, thiswork presents a novel CTRG framework that fully imitates the coarse-to-fine visual search pipeline practiced by radiologists. Our frameworkfirst employs a ViT-based image-text alignment architecture to extracta global token and local patch tokens for each anatomical structure, en-hanced by a mask-guided sparse negative-entropy loss. An abnormality-proposal LLM then processes the global token and clinical metadata topropose a shortlist of candidate abnormalities. This shortlist promptsan abnormality-prompted local token filter (AP-LTF) to select the mostinformative patch tokens, effectively reducing redundancy while preserv-ing critical information. Finally, a report-generation LLM takes in theglobal token, selected local tokens, and clinical metadata to composea full report. During training, ground-truth abnormalities are prefixedto the reference report to enhance awareness of clinic-relevant findings,and group relative policy optimization (GRPO) aligns the abnormality-proposal and report-generation LLMs for collaborative efficacy. Exper-imental results on two public CTRG datasets demonstrate the supe-rior performance of our framework compared to existing state-of-the-artFig. 1: Comparison of token compression strategies for CTRG. (a) 3D spatial pooling[2, 8] achieves high compression rate but incurs significant information loss. (b) Mask-guided volume cropping [12, 22] yields region-specific tokens but omits intra-regionredundancy. (c) [26] selects the top K tokens with the largest attention weights toregion-specific queries, yet may be subject to dominant “sink tokens” [14, 29]. (d) Ourmethod first proposes a shortlist of candidate abnormalities using only a single globaltoken per structure and clinical metadata. Then, an abnormality-prompted local tokenfilter (AP-LTF) combines the shortlist, a learned score, and the attention weight for acomprehensive token selection, thereby preventing sink tokens from dominating.methods in terms of clinical efficacy, RaTEScore, and GREEN scores.Ablation studies further validate the effectiveness of its novel design.

ECCV 2026

MeGAS: Thermomechanical Dynamic Gaussian Splatting for Thermophysical Scene Editing

Zesong Yang, Yuanhang Lei, Yihang Chen, Jiaer Huang, Liyuan Cui, Boming Zhao, Peter Yichen Chen, Hujun Bao, Zhaopeng Cui

Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene reconstruction and physics-based animation. However, existing approaches focus on mechanically driven dynamics while neglecting temperature, a fundamental yet invisible physical factor underlying phenomena such as melting, solidification, and other thermomechanical processes. In this paper, we propose MeGAS, a novel framework that incorporates thermomechanical phase-change dynamics into 3D Gaussian Splatting (3DGS). Specifically, we propose a new thermomechanical dynamic Gaussian Splatting representation that augments 3DGS with temperature attributes and employs a heat advection-diffusion solver with MPM dynamics incorporating phase transitions, enabling physically plausible and visually realistic synthesis of thermophysical phenomena. Furthermore, a new topology-adaptive Gaussian rendering strategy is proposed to mitigate cracking and floaters under extreme deformation. Extensive experiments demonstrate that MeGAS produces physically consistent thermomechanical behavior while maintaining highfidelity photorealistic rendering, advancing toward physics-integrated world models.

ECCV 2026

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images

Qinyue Tong, Ziqian Lu, Jun Liu, Rui Zuo, Zheming Lu, Yueming Jin

Despite notable progress in text-guided medical image seg-mentation nowadays, these methods are limited to single-round dia-logues and fail to support multi-round reasoning, which is importantfor medical education scenarios. In this work, we introduce Multi-Round Entity-Level Medical Reasoning Segmentation (MEMR-Seg), anew task that requires generating segmentation masks through multi-round queries with entity-level reasoning, helping learners progressivelydevelop their understanding of medical knowledge. To support this task,we construct MR-MedSeg, a large-scale dataset of 177K multi-roundmedical segmentation dialogues, featuring entity-based reasoning acrossrounds. Furthermore, we propose MediRound, an effective baselinemodel designed for multi-round medical reasoning segmentation. To mit-igate the inherent error propagation within the chain-like pipeline ofmulti-round segmentation, we introduce a lightweight yet effective Judg-ment & Correction Mechanism during model inference. Experi-mental results demonstrate that our method effectively addresses theMEMR-Seg task and outperforms conventional medical referring seg-mentation methods. The project is available at https: // github. com/Edisonhimself/ MediRound .

ECCV 2026

ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering

Zhengxian Wu, Hangrui Xu, Kai Shi, Zhuohong Chen, Yunyao Yu, Chuanrui Zhang, Zirui Liao, JunYang JunYang, Zhenyu Yang, Haonan Lu, Haoqian Wang

Knowledge-based Visual Question Answering (KB-VQA) re-quires models to combine image understanding with external knowledge.Most prior methods use a fixed retrieve-then-generate pipeline with a pre-selected retriever and a static top-k setting, which is not adaptive duringreasoning. We propose ProMSA, a progressive multimodal search agentfor KB-VQA. Given an image-question pair, the agent iteratively choosesimage search, text search, or stop, under explicit tool-call budgets andwith deduplication to avoid redundant retrieval. For training, we first userejection-sampling SFT to learn valid tool-use formats, then optimize theagent with TN-GSPO, a sequence-level RL objective that normalizesupdates by both generation length and tool-interaction depth. Experi-ments on E-VQA and InfoSeek show consistent gains over strong RAGand agent baselines, and improved retrieval and end-to-end accuracy.Thecode is available at https://github.com/DingWu1021/Promsa.

ECCV 2026

Learning Consistent Temporal Grounding between Related Tasks in Sports Coaching

Arushi Rai, Adriana Kovashka

Video-LLMs often attend to irrelevant frames, which is espe-cially detrimental for sports coaching tasks requiring precise temporal lo-calization. Yet obtaining frame-level supervision is challenging: expensiveto collect from humans and unreliable from other models. We improvetemporal grounding without additional annotations during training byexploiting the observation that related tasks, such as generation andverification, must attend to the same frames. We enforce this via a self-consistency objective over select visual attention maps of tightly-relatedtasks. Using VidDiffBench, which provides ground-truth keyframe an-notations, we first validate that attention misallocation is a meaningfulbottleneck. We then show that training with our objective yields gains of+3.0%, +14.1% accuracy and +0.9 BERTScore over supervised finetun-ing across three sports coaching tasks: ExAct, FitnessQA, and ExpertAF.

ECCV 2026

Layering Virtual Try-On

Chun Feng, Bowei Chen, Shan Mengyi, Ira Kemelmacher-Shlizerman

In the real world, fashion is about layering: adding a jacketover a shirt, or a sequence of adding and removing layers, rather thanjust a single-layer swap. This fundamental real-world task remains achallenge in existing Virtual Try-On (VTON) methods, which excel atsingle-layer replacement but are not designed to layer or de-layer anexisting outfit. This paper proposes Layering Virtual Try-On (LVTON),a layering benchmark and method that preserves an existing outfit whileenabling sequential layering. We find that current VTON paradigms arefundamentally ill-equipped for LVTON, as their reliance on cloth-agnosticrepresentations and single-item datasets discards essential layering context.Our key insight is that the LVTON challenge must be disentangled intotwo distinct competencies: (1) General VTON Priors (e.g., deformation,identity preservation) and (2) Specific Layering Knowledge (e.g., layeringorder and occlusion reasoning). First, our model obtains general VTONpriors by being trained on data produced by an automatic data generationpipeline that synthesizes samples from fashion videos via segmentationand inpainting. Second, the model is fine-tuned on a small, dedicatedLVTON dataset to learn the layering logic. Our method achieves state-of-the-art results on our LVTON benchmark and demonstrates superiorgeneralizability on traditional VTON benchmarks, setting new state-of-the-art results when fine-tuned and exhibiting zero-shot capabilities.

ECCV 2026

Hybrid-LUT: Channel-Aware Hybrid Lookup Table and Filtering for Efficient Image Restoration

Zhilin Ai, Boyu Li, Sidi Yang, Wenqing Shi, Wenyong Zhou, Binxiao Huang, Chenchen Ding, Ngai Wong

Lookup table (LUT)-based image denoising methods haveattracted increasing attention due to their high efficiency and hardware-friendly properties. However, existing RGB-LUT approaches require threeidentical LUTs to process RGB channels in parallel, resulting in largeon-chip SRAM consumption. A simple alternative is to apply LUT pro-cessing only to the luminance (Y) channel in the YUV color space toreduce memory usage. However, this naive strategy leads to degradedrestoration quality, since ignoring the chrominance (UV) channels in-troduces color distortion and residual artifacts. In this work, we pro-pose Hybrid-LUT, a YUV-based asymmetric channel-processing frame-work that combines LUT and filtering in a unified design. Specifically, amulti-band LUT branch with pixel-level weight fusion is applied to theY channel to recover fine textures, while lightweight filtering is used forthe UV channels to maintain color consistency. This design reduces LUTstorage by two-thirds compared with RGB-LUT methods while main-taining the same runtime throughput. Extensive experiments show thatHybrid-LUT achieves state-of-the-art (SOTA) performance across mul-tiple benchmarks with only 421 KB of storage. In particular, our methodsurpasses existing LUT-based denoising approaches by at least 0.63 dBCPSNR on real-world datasets, demonstrating its effectiveness for imagedenoising on resource-constrained edge devices.

ECCV 2026

HUGE-Bench: A Benchmark for High-Level UAV Vision-Language-Action Tasks

Jingyu Guo, Ziye Chen, Ziwen Li, Zhengqing Gao, Jiaxin Huang, Hanlue Zhang, Fengming Huang, Yu Yao, Tongliang Liu, Mingming Gong

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.

ECCV 2026

GAIA: A Data Flywheel System for Training GUI Test-Time Scaling Critic Models

Shaokang Wang, Pei Fu, Ruoceng Zhang, Shaojie Zhang, Xiuwen Xi, Jiahui Yang, Bin Qin, Ying Huang, Zhenbo Luo, Jian Luan

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.

ECCV 2026

Fast Dynamic Prototypes for Unsupervised Anomaly Detection and Localization

Mingliang Li, Hanxi Li, Lin Yuanbo Wu, Changhong Liu, Xiaowei Zhao

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.

ECCV 2026

EVEE: Event-Based Online Adaptation for Matching on Unknown Targets

Zejing Zhao, Cheng Ju, Yanwen Zhang, Akio Namiki

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.

ECCV 2026

EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders

Xinghao Wang, Dong Li, Wei Yu, Yingwei Pan, Tao Gong, Qi Chu, Nenghai Yu, Ting Yao

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.

ECCV 2026

EraseLoRA: MLLM-Driven Foreground Exclusion and Background Subtype Aggregation for Dataset-Free Object Removal

Sanghyun Jo, Donghwan Lee, Eunji Jung, Seong Je Oh, Kyungsu Kim

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.

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