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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

When the Teacher Has More Bits: Self-Teacher Latent Distillation for Learned Image Compression

Abdellah El Mennaoui, Joseph Meehan, Jean-Luc Dugelay, Ghalia Hemrit

Learned image compression (LIC) operates under a rate–distortion (RD) trade-off, where representation quality is constrained bythe target bitrate. We revisit knowledge distillation in LIC through thelens of bitrate asymmetry and introduce a self-teacher distillation frame-work, where a high-rate instance of a codec supervises multiple lower-rateencoders of identical architecture. Because allocating more bits naturallyleads to richer latent representations, the high-rate model provides infor-mative supervision across rate levels. Direct latent matching, however,is problematic under tight rate budgets. We therefore propose variance-normalized latent distillation (VNLD), a rate-aware alignment strategythat scales channel-wise supervision by the teacher’s variance, selectivelytransferring stable, informative structure while suppressing componentsthat cannot be reliably reproduced at lower rates. Across different dis-tillation objectives, networks, and bitrate levels, self-teacher distillation,particularly with VNLD, improves RD performance and yields consistentBD-rate gains over RD-only training. Our method remains compatiblewith fixed-decoder deployments, such as those targeted by JPEG AIstandards.

ECCV 2026

Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously

Yiran Guan, Liang Yin, Dingkang Liang, Jianzhong Ju, Zhenbo Luo, Jian Luan, Yuliang Liu, Xiang Bai

Online Video Large Language Models (VideoLLMs) playa critical role in supporting responsive, real-time interaction. Existingmethods focus on streaming perception, lacking a synchronized logicalreasoning stream. However, directly applying test-time scaling methodsincurs unacceptable response latency. To address this trade-off, we pro-pose Video Streaming Thinking (VST), a novel paradigm for streamingvideo understanding. It supports a thinking-while-watching mechanism,which activates reasoning over incoming video clips during streaming.This design improves timely comprehension and coherent cognition whilepreserving real-time responsiveness by amortizing LLM reasoning latencyover video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts theoffline VideoLLM to causal streaming reasoning, and VST-RL, whichprovides end-to-end improvement through self-exploration in a multi-turn video interaction environment. Additionally, we devise an auto-mated training-data synthesis pipeline that uses video knowledge graphsto generate high-quality streaming QA pairs, with an entity–relationgrounded streaming Chain-of-Thought to enforce multi-evidence reason-ing and sustained attention to the video stream. Extensive evaluationsshow that VST-7B performs strongly on online benchmarks, e.g. 79.5%on StreamingBench and 59.3% on OVO-Bench. Meanwhile, VST remainscompetitive on offline long-form or reasoning benchmarks. Comparedwith Video-R1, VST responds 15.7× faster and achieves +5.4% improve-ment on VideoHolmes, demonstrating higher efficiency and strong gen-eralization across diverse video understanding tasks. Code, data, andmodels have been released at https://github.com/1ranGuan/VST.

ECCV 2026

Video Generation Models Are Inherent Lighting Estimators

Ziqi Cai, Shuchen Weng, Kaiqi Liu, Zifeng Wang, Zhiquan Zhang, Minggui Teng, Han Jiang, Boxin Shi

Recovering dynamic environment maps from a single in-the-wild video is crucial for photorealistic rendering, yet remains a challenge.Recent video generation models can produce photorealistic scenes withcomplex lighting, possessing an inherent understanding of lighting. Inthis paper, we introduce V-LITE (Video generation models are inherentlighting estimators), a framework that unlocks this internal knowledge byreframing lighting estimation as a guided video inpainting task. Inspiredby visual effects (VFX) industry practices, we insert a synthetic chromeball into the scene to compel the model to generate physically plausiblereflections from the surrounding spatio-temporal context. To bridge thegap from LDR-native models to the HDR domain, we design an HDR-aware VAE and employ an efficient LoRA-based fine-tuning strategy. Wethen construct a mixed dataset comprising high-fidelity HDR images toprovide realistic HDR priors, and in-the-wild HDR videos to provide dy-namic spatio-temporal context. Extensive experiments demonstrate thatV-LITE produces temporally coherent HDR environment maps, reveal-ing that modern video diffusion models are not merely synthesizers butalso powerful, inherently capable estimators of physical scene lighting.

ECCV 2026

Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers

Edwin Kwadwo Tenagyei, Lei Wang, Ugochukwu Akpudo, JUN ZHOU, Yongsheng Gao

Parameter-efficient fine-tuning (PEFT) has become a prac-tical solution for adapting large pretrained vision transformers (ViTs)to downstream tasks while updating only a small subset of parame-ters. However, existing adapter-based methods perform adaptation in-dependently for each token, implicitly assuming that token refinementsshould be learned in isolation. This token-wise formulation overlooksthe structured relationships among tokens that naturally arise in visualscenes, potentially leading to redundant updates and spatially inconsis-tent feature refinement. In this work, we revisit the design of parameter-efficient adapters and propose to perform adaptation in hyperedge spacerather than token space. We introduce HyperAdapter, a hypergraph-based adapter architecture that enables structured, group-aware adap-tation through soft token routing. HyperAdapter constructs a soft hy-pergraph over ViT tokens using prototype-based assignments, aggregatestoken features into latent hyperedge representations, applies lightweightbottleneck adaptation at the hyperedge level, and diffuses the result-ing updates back to tokens via the hypergraph incidence structure. Thisdesign injects an explicit structural inductive bias into PEFT while pre-serving the modularity and efficiency of standard adapters. Extensiveexperiments across diverse visual benchmarks demonstrate that struc-tured hyperedge adaptation consistently outperforms strong PEFT base-lines under comparable parameter budgets, with particularly pronouncedgains on tasks requiring structured reasoning. Our results suggest thatthe choice of adaptation space is a critical yet underexplored dimensionin parameter-efficient transfer for ViTs.

ECCV 2026

SpecV: Specification Verification for Robust Unified Multimodal Evaluation

Weihao Yu, Rongyao Fang, Yuxuan Cai, Linjiang Huang, Yuhuan Yang, Xianwei Zhuang, Junyang Lin, Yixuan Yuan, Shuai Bai

Unified Multimodal Models (UMMs) consolidate visual un-derstanding, image generation, editing, and interleaved image-text in-teraction, yet their evaluation increasingly depends on VLM-as-a-judgescoring that shifts as judges, prompts, or model versions evolve — aproblem we term evaluation drift. We present SpecV, a specification-verification framework for robust unified multimodal evaluation. SpecVreplaces holistic scoring with the Specification Verification Proto-col, which decomposes each prompt into atomic, binary specificationsand verifies them against model outputs, improving cross-judge agree-ment and stability while preserving alignment with human judgments.To produce reliable specifications, we propose Specification Ensem-ble and Refinement, a multi-model pipeline that aggregates candidatespecifications, deduplicates them semantically, and filters for verifiabil-ity and relevance. We also introduce SpecV-Bench, a 1,200-instancebenchmark covering six core UMM tasks with sub-tracks of increasingconstraint complexity, enabling fine-grained analysis of capability trade-offs and failure modes. Across extensive experiments with multiple judgemodels, SVP consistently reduces ranking flips and improves evalua-tion reproducibility. Benchmark is available at https://github.com/yuyouxixi/SpecV.

ECCV 2026

SignNet-1M: Large-Scale Multilingual Sign Language Video Dataset with Downstream Benchmarks

Zhewen He, Junyi Hu, Haomian Huang, Zhenhua Li, Yushen Liu, Yi Fang

Sign language models are typically trained on datasets cap-tured under constrained conditions, with limited viewpoint, background,and signer-identity diversity, leading to poor robustness under real-worlddistribution shifts. We introduce SignNet-1M, a large-scale augmenteddataset spanning ASL, CSL, and German Sign Language (DGS).SignNet-1M synthesizes realistic variations along three axes: (i) novel-view rendering (rotation and zoom) via 3D Gaussian Splatting (3DGS),(ii) scene/ identity editing via diffusion models for background replace-ment and signer substitution while preserving sign motion and linguisticcontent, and (iii) post-rendering augmentations that emulate captureand compression artifacts (e.g., geometric transforms, photometric shifts,mild temporal resampling, and compression) to better match in-the-wildrecordings. Beyond data release, we provide a unified benchmark suiteacross downstream tasks (e.g., translation and recognition) and abla-tions that isolate each augmentation component. Experiments acrossbackbones show that training with SignNet-1M consistently improvesgeneralization under cross-view, cross-background, cross-identity, andpost-rendering shifts, while maintaining strong in-distribution perfor-mance. The dataset, released augmentation components, metadata, andbenchmark resources are available at https://signnet.chatsign.ai/.

ECCV 2026

SGQA: Semantic-Geometric Quality Alignment for Training-Free Few-Shot Instance Segmentation

Yuhao QING, Liuyan Feng, Haoyuan Li

Compositions of frozen foundation models offer a trainingfree route to instance segmentation, yet a clear gap remains between current systems and their empirical upper bounds. We introduce progressive oracle replacement, a diagnostic procedure that decomposes this gap into stage-level components, and find that the scoring stage accounts for most of it. Across diverse encoders and mask generators, we trace this gap to Quality Misalignment: a mismatch between semantic matching confidence and mask geometric fidelity that yields ranking errors when the two signals are used independently. This pattern persists across all tested configurations, suggesting that it is intrinsic to decoupled scoring. We therefore propose Semantic-Geometric Quality Alignment (SGQA), which replaces decoupled scoring with a single quality score formed by the geometric mean of the two signals and adds no hyperparameters to the scoring formula. Under a fixed backbone of DINOv2 ViT-L paired with SAM2 Hiera-L, SGQA outperforms existing training-free methods and matches or exceeds fine-tuning-based methods with comparable or smaller backbones.

ECCV 2026

SeeClear: Reliable Transparent Object Depth Estimation via Generative Opacification

Xiaoying Wang, Yumeng He, Jingkai Shi, Jiayin Lu, Yin Yang, Ying Jiang, Chenfanfu Jiang

Monocular depth estimation remains challenging for trans-parent objects, where refraction and transmission are difficult to modeland break the appearance assumptions used by depth networks. As a re-sult, state-of-the-art estimators often produce unstable or incorrect depthpredictions for transparent materials. We propose SeeClear, a novelframework that converts transparent objects into diffusion-generated opaqueimages, enabling stable monocular depth estimation for transparent ob-jects. Given an input image, we first localize transparent regions andtransform their refractive appearance into geometrically consistent opaqueappearances using a diffusion-based generative opacification module. Theprocessed image is then fed into an off-the-shelf monocular depth esti-mator without retraining or architectural changes. To train the opaci-fication model, we construct SeeClear-396k, a synthetic dataset con-taining 396k rendered images across 66k paired transparent-opaque con-figurations. Experiments on both synthetic and real-world datasets showthat SeeClear significantly improves depth estimation for transparentobjects.

ECCV 2026

Ranked Activation Shift for Post-hoc Out-of-Distribution Detection

Gianluca Guglielmo, Marc Masana

State-of-the-art post-hoc out-of-distribution detection meth-ods rely on intermediate layer activation editing. However, they exhibitinconsistent performance across datasets and models. We show that thisinstability is driven by differences in the activation distributions, andidentify a failure mode of scaling-based methods that arises when penul-timate layer activations are not rectified. Motivated by this analysis,we propose RAS, a hyperparameter-free post-hoc method that replacessorted activation magnitudes with a fixed in-distribution reference profile.Our simple plug-and-play method shows strong and consistent perfor-mance across datasets and architectures without assumptions on thepenultimate layer activation function, and without requiring any hyperpa-rameter tuning, while empirically preserving in-distribution classificationaccuracy. We further analyze what drives the improvement, showing thatboth inhibiting and exciting activation shifts independently contribute tobetter out-of-distribution discrimination1 .

ECCV 2026

PersonaDrive: Controllable Trajectory Prediction with Multi-Dimensional Driving Personas

Chan Lee, Kimin Yun, Yuseok Bae, Seong Tae Kim, Jung Uk Kim

Although recent trajectory prediction and end-to-end au-tonomous driving methods improve robustness in urban environments,they still lack meaningful controllability. Existing benchmarks either pro-vide no persona-conditioned annotations or support only a single urgencyspectrum (i.e., emergency, normal, relaxed), which cannot distinguishpersonas that share the same urgency level but require different drivingdynamics. To address this, we propose (i) the Persona-Conditioned Tra-jectory (PCT) dataset, which decomposes driving personas along twoaxes—Temporal Urgency and Ride Comfort—and combines three levelsof each to form a grid of nine personas, each paired with natural-languagedescriptions and trajectories, and (ii) PersonaDrive, a framework thatcan learn driving personas from language and can generate persona-specific trajectories. PersonaDrive incorporates Persona-Conditioned An-chor Transform (PCAT), which hierarchically reshapes anchors alongboth axes, and Persona-Conditioned Multi-Modal Fusion (PCMF) forBEV-level persona fusion. Training is supervised by a Hierarchical GuideLoss enforcing axis-aligned physical orderings and an Axis-DecomposedDiversity Loss preventing diagonal mode collapse. Experimental resultsshow that PersonaDrive consistently improves over the compared base-lines across multi-dimensional scenarios. The code and PCT dataset areavailable at https://github.com/VisualAIKHU/PersonaDrive.

ECCV 2026

Perceiving Better Moments: Cover Frame Reselection and Enhancement for Live Photos with the Live2K Dataset

Junyu Lou, Kai Chen, Weiyi You, Hui Zeng, Lei Zhang, Shuhang Gu

Modern smartphones capture Live Photos—short video bursts sur-rounding a still image—offering a dynamic and engaging photographic experi-ence. However, the cover photo and video components are generated by two dis-tinct imaging pipelines: the photo stream undergoes full computational photog-raphy processing, while the video stream is constrained by real-time efficiencyand heavy compression. This intrinsic separation produces a substantial qual-ity gap in resolution, color fidelity, and dynamic range between the cover photoand video frames. When users reselect an alternative frame from the video toreplace an imperfect cover, the chosen frame often suffers from severe degrada-tion, making direct replacement visually unsatisfactory. Restoring such framesrequires simultaneous enhancement of spatial detail and color appearance, a taskconsiderably more challenging than ordinary super-resolution or color enhance-ment. To address this, we define the Live Photo Cover Frame Reselection andEnhancement (LPRE) task, which leverages the intrinsic cues available withineach Live Photo: the high-quality cover image as a structural and color reference,the user-reselected low-quality frame as the reconstruction target and several ad-jacent video frames providing temporal cues. Building upon this formulation, weconstruct Live2K, a real-world dataset of 2,042 Live Photos, and develop a unifiedone-stage baseline that integrates multi-frame fusion, guided color enhancementand super-resolution—establishing the first benchmark for Live Photo enhance-ment research.

ECCV 2026

PDF-Omni: Poincaré Dual Disk Distortion Field-based Recurrent Update for Omnidirectional Stereo Matching

Yunseok Yang, Eunjin Son, Sang Lee

Omnidirectional stereo matching (OSM) estimates 360◦ depthfrom multi-view fisheye images, where inherent fisheye distortion causessparse matching cues in seam regions. However, existing methods rely onspatially uniform update policies, degrading performance in these chal-lenging regions. To address this limitation, we propose PDF-Omni, ageometry-aware OSM framework built on a Poincaré Dual Disk distor-tion field that models spatially varying geometric uncertainty across seamregions. We introduce a distortion-informed GRU with distortion-awareattention to selectively expand the receptive field in high-uncertaintyregions while preserving efficiency in low-uncertainty ones. We furtherpresent a local entropy cumulative loss to supervise high-entropy costdistributions in ambiguous regions. Experimental results demonstratestate-of-the-art performance with consistent gains especially in seam re-gions. The code is available at https://github.com/ynskyang/PDF-Omni

ECCV 2026

Pathwise Test-Time Correction for Autoregressive Long Video Generation

Xunzhi Xiang, Zixuan Duan, Guiyu Zhang, Haiyu Zhang, Zhe Gao, Junta Wu, Shaofeng Zhang, Tengfei Wang, Qi Fan, Chunchao Guo

Distilled autoregressive di!usion models facilitate real-timeshort video synthesis but su!er from severe error accumulation duringlong-sequence generation. While existing Test-Time Optimization (TTO)methods prove e!ective for images or short clips, we identify that they failto mitigate drift in extended sequences due to unstable reward landscapesand the hypersensitivity of distilled parameters. To overcome these limi-tations, we introduce Test-Time Correction (TTC), a training-free alter-native. Specifically, TTC utilizes the initial frame as a stable referenceanchor to calibrate intermediate stochastic states along the sampling tra-jectory. Extensive experiments demonstrate that our method seamlesslyintegrates with various distilled models, extending generation lengthswith a slight overhead while matching the quality of resource-intensivetraining-based methods on 30-second benchmarks.

ECCV 2026

Open-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints

Hojun Choi, Seulbin Hwang, Dae Kim, Kisung Kim, Hyunjung Shim, Jinhan Lee

Bird’s-eye view (BEV) perception fuses multi-camera imagesinto a unified top-down representation for autonomous driving. Despiterecent progress, state-of-the-art methods remain confined to closed-setscenarios, making them vulnerable to unpredictable real-world environ-ments. In this work, we introduce open-vocabulary BEV segmentation(OVBS), which leverages vision-language models (VLMs) to recognizecategories beyond the training set while maintaining precise BEV percep-tion and real-time efficiency. A key challenge in OVBS lies in the 3D geo-metric inconsistency inherent in the ill-posed lifting of 2D VLM semanticsinto BEV. To address this, we propose OVBEVSeg, a geometry-awareOVBS framework that enhances efficient Gaussian splatting (GS)-basedunprojection by leveraging robust 3D geometric constraints across threeprogressive stages: (1) 2D-to-BEV pseudo-labeling via reliable 3D pro-jection for OV generalization; (2) joint 2D–BEV per-scene optimizationwith BEV structural constraints for 3D geometric consistency; and (3)3D geometric distillation for online efficiency. On the nuScenes dataset,OVBEVSeg achieves state-of-the-art performance, outperforming closed-set methods by 15.3 mIoU on unseen categories. Remarkably, even withno novel-class ground-truth labels, it remains competitive with self- andsemi-supervised baselines trained with up to 40% of ground-truth anno-tations. Furthermore, it achieves 2.5× faster inference with only 0.22×the memory consumption of projection-based methods.

ECCV 2026

Novel View Synthesis as Video Completion

Qi Wu, Khiem Vuong, Minsik Jeon, Srinivasa G. Narasimhan, Deva Ramanan

We tackle the problem of sparse novel view synthesis (NVS)using video diffusion models; given K (≈ 5) posed images of a scene,we predict the view from a target camera pose. Many prior approachesleverage generative image priors encoded via diffusion models. However,models trained on single images lack multi-view knowledge, and we in-stead argue that video models contain implicit multi-view knowledge andare therefore easier to adapt for NVS. Our key insight is to formulatesparse NVS as a low-frame-rate video completion task. However, onechallenge is that sparse NVS is defined over an unordered set of inputimages, often too sparse to admit a meaningful order, so the modelsshould be invariant to permutations of that input image set. To thisend, we present FrameCrafter, which adapts video models (naturallytrained with coherent frame orderings) to permutation-invariant NVSthrough simple but effective architectural modifications, including per-frame latent encoding, query-centered camera conditioning and removalof temporal positional embeddings. Our results suggest that video modelscan be easily modified to “forget” about time with minimal supervision,producing state-of-the-art performance on sparse-view NVS benchmarks.

ECCV 2026

MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning

Tianyu Xu, Jiawei Chen, Jiazhao Zhang, Wenyao Zhang, Zekun Qi, Minghan Li, Jiahang Liu, Lu Yue, Zhizheng Zhang, He Wang

Visual navigation policies are widely regarded as a critical re-search direction, as they emulate human navigation behavior by leverag-ing egocentric visual observations. However, unlike LiDAR point cloudsor depth maps, visual observations do not provide explicit geometricinformation for navigation, especially in cluttered or dynamic environ-ments, which motivates the need for learning-based models and large-scale data. To this end, we propose to leverage Vision-Language-Action(VLA) models to learn diverse navigation capabilities from synthetic ex-pert data and to alleviate the sim-to-real gap by co-training on large-scalereal-world Visual Question Answering (VQA) data. Specifically, we de-velop MM-Nav, a 7B multi-view VLA model featuring custom-designedarchitectures and enabling a 7 Hz inference speed with 360◦ observation.For large-scale navigation data, we collect a total of 1.5 million expertdemonstrations from three reinforcement learning (RL) experts, eachtrained with privileged information in a challenging, tailor-made envi-ronment and specialized in one of three navigation capabilities: reaching,squeezing, and avoiding. We then iteratively train MM-Nav on these data,dynamically balancing the training data ratio across the three capabil-ities based on their respective performance. Through extensive exper-iments in synthetic and real-world environments, we demonstrate thatour model achieves strong performance and generalization on differentbenchmarks. MM-Nav obtains a success rate of 88.1% on the InternVLA-N1 System-1 point-goal navigation benchmark. Moreover, we find thatour student VLA model outperforms the RL teachers, demonstrating thesynergistic effect of integrating multiple capabilities. Extensive real-worldexperiments further confirm the effectiveness of our method.

ECCV 2026

Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing

Dohun Lee, Chun-Hao Huang, Xuelin Chen, Jong Chul Ye, Duygu Ceylan, Hyeonho Jeong

Video-to-video diffusion models achieve impressive singleturn editing performance, but practical editing workflows are inherently iterative. When edits are applied sequentially, existing models treat each turn independently, often causing previously generated regions to drift or be overwritten. We identify this failure mode as the problem of cross-turn consistency in multi-turn video editing. We introduce Memory-V2V, a memory-augmented framework that treats prior edits as structured constraints for subsequent generations. Memory-V2V maintains an external memory of previous outputs, retrieves task-relevant edits, and integrates them through relevance-aware tokenization and adaptive compression. These technical ingredients enable scalable conditioning without linear growth in computation. We demonstrate Memory-V2V on iterative video novel view synthesis and text-guided long video editing. Memory-V2V substantially enhances cross-turn consistency while maintaining visual quality, outperforming strong baselines with modest overhead.

ECCV 2026

Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length

Yubo Huang, Hailong Guo, Fangtai Wu, Weiqiang Wang, Shijie Huang, Qijun Gan, Shifeng Zhang, Lin Liu, Sirui Zhao, Enhong Chen, Jiaming Liu, Steven Hoi

Audio-driven avatar interaction demands real-time, stream-ing, and in_x001C_nite-length generation_x0016_capabilities fundamentally at oddswith the sequential denoising and long-horizon drift of current di_x001B_usionmodels. We present Live Avatar, an algorithm-system co-designed frame-work that addresses both challenges for a 14-billion-parameter di_x001B_usionmodel. On the algorithm side, a two-stage pipeline distills a pre-trained bidirectional model into a causal, few-step streaming one whoseKV cache stores noisy rather than clean history. This lossy condition-ing, counter-intuitively, suppresses error accumulation and enables mod-els trained on short clips to generalize stably beyond 10,000 seconds.On the system side, this noisy conditioning also breaks the sequen-tial sampling bottleneck, allowing Timestep-forcing Pipeline Parallelism(TPP) to assign each GPU a _x001C_xed denoising timestep, converting the se-quential di_x001B_usion chain into an asynchronous spatial pipeline that simul-taneously boosts throughput and improves temporal consistency. LiveAvatar achieves 45 FPS with a TTFF of 1.21 s on 5 H100 GPUs, andto our knowledge is the _x001C_rst to enable practical real-time streaming ofa 14B di_x001B_usion model for in_x001C_nite-length avatar generation. Our projectpage is at https://liveavatar.github.io/.

ECCV 2026

Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation

Niclas Popp, Kevin Alexander Laube, Matthias Hein, Lukas Schott

Large foundation models trained on extensive datasets demon-strate strong zero-shot capabilities in various domains. Knowledge dis-tillation has become an established tool for transferring knowledge fromfoundation models to small student networks when data and model sizeare constrained. However, the efficacy of distillation is often hampered bylimited training data coverage. This can result in a covariate shift betweentraining and test data, which in turn can lead the student to exploitspurious features or even shortcut learning. We address this problemby introducing a novel diffusion-based data augmentation strategy thatgenerates images by maximizing the disagreement between the teacherand the student, effectively creating challenging samples that the studentstruggles with, thus mitigating the problem of covariate shift. Experi-ments demonstrate that, compared to state-of-the-art diffusion-based dataaugmentation baselines, our approach is best or second-best in samplemean accuracy and improves the worst group and mean group accuracyon CelebA-HQ, SpuCo Birds and BAR as well as the spurious score onSpurious ImageNet under covariate shift.

ECCV 2026

FreeSwim: Revisiting Sliding-Window Attention Mechanisms for Training-Free Ultra-High-Resolution Video Generation

Yunfeng Wu, Jiayi Song, Zhenxiong Tan, Zihao He, Songhua Liu

The quadratic time and memory complexity of the attention mechanism in modern Transformer-based video generators makes endto-end training for ultra-high-resolution videos prohibitively expensive. Motivated by this limitation, we introduce a training-free approach that leverages video Diffusion Transformers pre-trained at their native scale to synthesize higher-resolution videos without any additional training or adaptation. At the core of our method lies an inward sliding-window attention mechanism, which originates from a key observation: maintaining each query token’s training-scale receptive field is crucial for preserving visual fidelity and detail. However, naive local window attention, unfortunately, often leads to repetitive content and exhibits a lack of global coherence in the generated results. To overcome this challenge, we devise a dual-path pipeline that backs up window attention with a novel cross-attention override strategy, enabling the semantic content produced by local attention to be guided by another branch with a full receptive field and, therefore, ensuring holistic consistency. Furthermore, to improve efficiency, we incorporate a cross-attention caching strategy for this branch to avoid the frequent computation of full 3D attention. Extensive experiments demonstrate that our method delivers ultra-highresolution videos with fine-grained visual details and high efficiency in a training-free paradigm. Meanwhile, it achieves superior performance on VBench, even compared to training-based alternatives, with competitive or improved efficiency.

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