paperlist.ai

AI conference papers

AI conference paper library

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

BEV-GS: Feed-forward Gaussian Splatting in Bird’s-Eye-View for Road Reconstruction

Wenhua Wu, Tong Zhao, Chensheng Peng, Lei Yang, Zhe Liu, Hesheng Wang

Road surfaces serve as the only contact medium between ve-hicle wheels or robot feet and the physical world. Reconstructing the roadsurface is crucial for unmanned vehicles and mobile robots. Recent stud-ies on Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) haveachieved remarkable results in scene reconstruction. However, they typi-cally rely on multi-view image inputs and require prolonged optimizationtimes. In this paper, we propose BEV-GS, a real-time single-frame roadsurface reconstruction method based on feed-forward Gaussian splatting.BEV-GS consists of a prediction module and a rendering module. Theprediction module introduces separate geometry and texture networksfollowing the Bird’s-Eye-View (BEV) paradigm. Geometric and textureparameters are directly estimated from a single frame, avoiding per-sceneoptimization. In the rendering module, we utilize grid Gaussian for roadsurface representation and novel view synthesis, which better aligns withroad surface characteristics. Our method achieves state-of-the-art per-formance on the real-world dataset RSRD. The road elevation error isreduced to 1.73 cm, and the PSNR of novel view synthesis reaches 28.36dB. The prediction and rendering FPS are 26 and 2061, respectively,enabling high-accuracy and real-time applications. Our code is availableat https://github.com/IRMVLab/BEV-GS.

ECCV 2026

Bayesian Uncertainty Attribution-Guided Fine-Tuning for Open-Set Action Recognition

Shehan Senavirathna, Hongji Guo, Qiang Ji

Open-set action recognition (OSAR) requires a model toclassify known actions while rejecting unseen ones. Existing methodstypically use predictive uncertainty only at inference time, treating it asa rejection score after representation learning is complete. This leaves theunderlying representation unchanged and allows spurious background orcontext cues to remain embedded in the model, which weakens known–unknown separation. We propose a staged uncertainty-guided fine-tuningframework that uses ensemble-derived epistemic uncertainty not only forrejection, but also as a training signal for representation refinement. Theframework proceeds in three stages: motion-guided ensemble training tobias learning toward action-relevant evidence, in-distribution epistemicregularization to improve uncertainty reliability on known samples, anduncertainty attribution mask-based input attention (UAM-IA), whichconverts uncertainty attribution maps into input attention masks for re-finement. To improve deployment efficiency, we further distill the refinedensemble into a single student model trained to approximate both theensemble predictive distribution and the ensemble-derived epistemic un-certainty. Under a controlled OSAR protocol with UCF101 as the knownset and HMDB51 and MiT-v2 as unknown sources after removing over-lapping classes, the proposed method improves known–unknown separa-tion across multiple backbones. The distilled student preserves much ofthis benefit while enabling efficient single-model inference.

ECCV 2026

AutoV: Loss-Oriented Ranking for Visual Prompt Retrieval in LVLMs

Yuan Zhang, Chun-Kai Fan, Sicheng Yu, Junwen Pan, Tao Huang, Ming Lu, Kuan Cheng, Qi She, Shanghang Zhang

Inspired by text prompts in large language models, visualprompts have been explored to enhance the perceptual capabilities oflarge vision-language models (LVLMs). However, performance tends tosaturate under single visual prompt designs, making further prompt engi-neering increasingly ineffective. To address this limitation, we shift fromprompt engineering to prompt retrieval and propose AutoV, a lightweightframework for instance-adaptive visual prompt identification. Given aninput image and a textual query, AutoV automatically locates the mostsuitable visual prompt from a diverse candidate pool. Training such a re-trieval framework requires prompt-level supervision, yet prompt qualityis inherently ambiguous and difficult to assess reliably, even for humans.To enable automatic supervision, we evaluate visual prompts using a pre-trained LVLM and label them according to their prediction losses. Usingthe loss-oriented ranking as a robust training signal, AutoV learns to re-trieve the query-aware optimal prompt for each instance without manualannotation. Experiments indicate that AutoV enhances the performanceof various LVLMs on image understanding, captioning, grounding, andclassification tasks. For example, AutoV improves LLaVA-OV by 10.2%on VizWiz and boosts Qwen2.5-VL by 3.8% on MMMU, respectively.

ECCV 2026

AGE: Agentic Gaussian Editing in 3D Scenarios

Hao Qin, Tesi Lin, Mingwei Wei, Yukai Sun, Mengxu Lu, Ming Kong, Qiang Zhu

Segmenting thin structures such as infrastructure cracks andanatomical vessels is hampered by topology-sensitive geometry, highannotation cost, and poor cross-domain generalization. We propose FMS2 ,a unified flow-matching framework with two complementary modules.SegFlow is a 2.96M-parameter encoder–decoder segmentation modelthat recasts prediction as continuous image→mask transport: it learnsa time-indexed velocity field with a single flow-matching regression lossand obtains the mask through deterministic ODE integration, ratherthan supervising only end-state logits. This trajectory-level supervisionimproves thin-structure continuity and sharpness over topology-awareloss baselines, without auxiliary topology heads, post-processing, or multi-term loss engineering. SynFlow is a mask-conditioned mask→imagegenerator that produces pixel-aligned image–mask pairs by injectingmask geometry at multiple scales, emphasizing boundary bands withedge-aware gating, and expanding sparsity, width, and branching througha controllable mask generator. On five crack and vessel benchmarks,SegFlow alone outperforms CNN, Transformer, Mamba, and generativebaselines, improving mean IoU from 0.511 to 0.599 (+17.2%) and reducingBetti matching error from 82.145 to 51.524 (−37.3%). With limitedlabels, SynFlow recovers near-full performance using 25% real annotationsand improves cross-domain IoU by 0.11. Unlike classical augmentation,SynFlow provides pixel-aligned supervision with controllable structuralshifts (e.g., sparsity, width, branching), making it effective under domainshift. We release 10k crack and 1k vessel image–mask pairs. The projectpage is available at FMS2 .

ECCV 2026

Whence the Voice? Self-supervised Dual-source Audio-Visual Localisation via Selective Convergence

Han Hu, Dongheng Lin, Yuqi Hou, Haotian Li, Hyung Jin Chang, Jianbo Jiao

Localising multiple sound sources in visual scenes remains afundamental challenge in multimodal perception due to an inherent cir-cular dependency: separating mixed audio requires knowing source loca-tions, while identifying sound-producing regions requires separated audiosignals. In this paper, we focus on the dual-source setting and discover aselective convergence in self-supervised audio-visual learning: when pre-sented with multiple sound sources, contrastive models naturally con-verge to the most salient audio-visual correspondence rather than at-tempting to represent all sources equally. This emergent phenomenon,analogous to human selective auditory attention, enables us to breakthe above circular dependency through a progressive two-stage frame-work: first, leveraging selective convergence to identify dominant sources,and then exploiting these learned priors to uncover remaining sources.Our self-supervised approach achieves the best performance among self-supervised methods on dual-source benchmarks without requiring anymanual annotations, and even surpasses some weakly-supervised ap-proaches on certain metrics. Furthermore, we identify a fundamentalevaluation inconsistency in existing benchmarks: comparing continuouslocalisation heatmaps against bounding-box annotations creates system-atic biases, particularly for non-axis-aligned objects where the boundingbox includes substantial background regions. To address this, we intro-duce pixel-level segmentation masks to the existing benchmark, enablingspatially-aligned evaluation. Together, these results suggest that embrac-ing rather than suppressing selectivity offers a scalable, annotation-freeroute to multi-source localisation.

ECCV 2026

WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching

Yihan Wang, Jia Deng

We introduce WAFT-Stereo, a simple and effective warping-based method for stereo matching. WAFT-Stereo demonstrates that costvolumes, a common design used in many leading methods, are not nec-essary for strong performance and can be replaced by warping with im-proved efficiency. WAFT-Stereo ranks first on ETH3D (BP-0.5), Middle-bury (RMSE), and KITTI (all metrics), reducing the zero-shot error by81% on ETH3D, while being 1.8−6.7× faster than competitive methods.Code and model weights are available at https://github.com/princeton-vl/WAFT-Stereo.

ECCV 2026

UniDynamics: Event-RGB Fusion for Unified Future 4D Dynamic Scene Generation

Daikun Liu, Xin Zhan, Teng Wang, Xiaoping Wang, Changyin Sun

We propose UniDynamics, a diffusion-based framework forfuture 4D dynamic scenes (RGB, depth, and optical flow) generationfrom a single event-RGB pair, without requiring long histories or con-trol priors as in existing methods, while explicitly modeling future mo-tion fields. The core idea is to leverage event streams to offer an al-ternative motion prior for single-RGB extrapolation, and to enforce ge-ometric and motion constraints throughout generation via multimodalmodeling. Specifically, we design an Event Latent Enhancement (ELE)module to align and enhance event latents into diffusion-injectable con-ditioning features, providing robust initial motion priors and reliabletexture/structure cues. We further introduce a Perceptual DynamicsSpace (PDS) embedded in the multi-scale U-Net, which decouples andadaptively interacts depth and flow while continuously feeding back con-straints to appearance features, improving geometric-motion consistencyfor physically plausible and spatiotemporally coherent prediction. Exper-iments on VKitti2 and DSEC demonstrate state-of-the-art performance,producing high-quality, temporally coherent, and 4D-consistent futurepredictions, especially under challenging high-speed motion blur.

ECCV 2026

UltraGen: Efficient Ultra-High-Resolution Image Generation with Hierarchical Local Attention

Yuyao Zhang, Yu-wing Tai

Ultra-high-resolution image generation is increasingly vital for applications requiring fine-grained textures and global structural fidelity, yet state-of-the-art text-to-image diffusion models such as FLUX and SD3 remain confined to sub 2MP (< 1K × 2K) resolutions due to the quadratic complexity of attention mechanisms and the scarcity of high-quality high-resolution training data. We present UltraImageGen, a novel framework that introduces hierarchical local attention with low-resolution global guidance, enabling efficient, scalable, and semantically coherent image synthesis at ultra-high resolutions. Specifically, high-resolution latents are divided into hardware aligned fixed-size local windows to reduce attention complexity from quadratic to near-linear, while a low-resolution latent with scaled positional embeddings injects global semantics as an anchor. A lightweight LoRA adaptation bridges global and local pathways during denoising, ensuring consistency across structure and detail. To maximize efficiency and achieve scalable ultrahigh-resolution generation, we repermute token sequence in window-first order, so that the GPU-friendly dense local blocks in attention calculation equals to the fixed-size local window in 2D regardless of resolution. Together UltraImageGen reliably scales pretrained models to resolutions higher than 8K with more than 10× speed up and significantly lower memory usage. Extensive experiments demonstrate that UltraImageGen achieves superior quality while maintaining computational efficiency, establishing a practical paradigm for advancing ultra-high-resolution image generation. Our project page and code are available here.

ECCV 2026

Triangle Splatting SLAM

Nicholas Fry, Eric Dexheimer, Kirill Mazur, Paul Kelly, Andrew Davison

We present a dense RGB-D SLAM system using differen-tiable triangles as the 3D map representation. While 3D Gaussian Splat-ting has emerged as the leading method for novel-view synthesis, trianglesremain the standard primitive for traditional rendering hardware, gameengines, and downstream tasks requiring explicit geometry such as simu-lation, collision, and editing. Recent offline methods have demonstratedthat an unstructured ‘triangle soup’ can be optimised into a photorealis-tic mesh via Delaunay triangulation across a set of posed images. Build-ing upon this insight, we present the first dense SLAM system to employTriangle Splatting to perform both tracking and mapping through on-line differentiable rendering of a triangle soup. The map can be convertedinto a connected mesh on-the-fly via restricted Delaunay triangulation,enabling new online capabilities such as mesh deformation and collisionchecking. On Replica and TUM-RGBD, our system outperforms base-lines on 3D geometry, matches the camera-tracking accuracy, and enablesonline mesh-based scene editing.

ECCV 2026

Taming Dynamic Clutter: Variance-Driven Adaptive Gain Control for Bio-inspired Small Target Detection

Jiaxiang Li, Shaobing Gao, Qinbing Fu, Meiyi Li, Tiansheng Lu, Minjie Tan

Bio-inspired Small Target Motion Detection (STMD) mod-els often suffer “false alarm collapse” due to non-stationary environ-mental interference, failing to adapt to dynamic background statistics.We propose a bio-inspired architecture integrating macro-motion decou-pling with micro-scale Adaptive Gain Control (AGC). Macroscopically, itleverages lobula plate tangential cells (LPTC)-based population codingto eliminate ego-motion-induced baseline drifts in real-time with mini-mal overhead. Microscopically, we introduce an adaptive shunting inhi-bition operator inspired by Natural Scene Statistics (NSS). By quan-tifying local temporal variance, this mechanism maps non-stationaryclutter into a gain control factor to suppress heavy-tailed noise fromhigh-frequency flickers. This versatile plug-and-play module integratesseamlessly into existing STMD frameworks. Extensive experiments onsynthetic and real-world datasets demonstrate that our model signifi-cantly reduces false alarms while maintaining high sensitivity for dimtargets, achieving state-of-the-art precision and robustness in complexdynamic environments. Our code is publicly available at AGC.

ECCV 2026

SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence

Yulu Pan, Han Yi, Seongsu Ha, Mohaiminul Islam, Benjamin Zhang, Lorenzo Torresani, Gedas Bertasius

True video intelligence demands more than recognizing whatis visible: it requires reasoning about why events unfold, predicting whatwould change under different conditions, and deciding what to do next.We refer to this full progression—from perception through causal reason-ing and simulation to strategic planning—as Strategic Video Intelligence(SVI). No existing benchmark evaluates this capability stack: in-the-wildvideos lack verifiable ground truth for causal and strategic questions,while synthetic environments sacrifice the complexity of real multi-agentsystems. To bridge this gap, we introduce SVI-Bench, a large-scale bench-mark that leverages team sports as a dynamic microworld, a domain thatuniquely combines the complexity of real-world multi-agent interactionwith the verifiability of explicit rules and definitive outcomes. SVI-Benchcomprises ∼35K hours of broadcast video, ∼15M annotated actions,∼15K hours of expert commentary, ∼23K game reports, and ∼103Kstructured statistical records across basketball, soccer, and hockey, allconstructed via a data engine that transforms raw game data into adense, cross-referenced corpus. We organize evaluation into 9 tasks span-ning a progressive four-pillar hierarchy: Dynamic Scene Understanding,Causal Reasoning, Strategic Simulation, and Agentic Synthesis. Evaluat-ing strong multimodal and agentic baselines, we find a capability cliff :models perform competently on perceptual tasks (achieving ∼74% onfine-grained action QA) but degrade sharply at higher levels of the stack.Agentic tasks prove hardest of all: the strongest model achieves only 5%accuracy when required to autonomously gather and integrate evidenceacross a corpus of 1.8M clips. We release the full benchmark to catalyzeprogress toward AI systems capable of strategic intelligence in complex,dynamic multi-agent environments.§ Code github.com/texaser/svi-benchõ Data huggingface.co/mvp-group/svi-bench€ Website svi-bench.github.ioA Extended Paper svi-bench.github.io/svi_bench_extended.pdfPillar 1: Perceptionparsing who is where, doing what and whenPillar 2: Reasoningexplaining why something happenedPillar 3: Simulationgenerating plausible alternative futuresPillar 4: Agencyautonomous analysis over multimodal evidenceThe Strategic Video Intelligence StackFig. 1: Overview of SVI-Bench, illustrated through a single play from the 2022NCAA Final Four. SVI-Bench is the first large-scale video benchmark evaluatingthe full SVI stack: Perception (describing what happens), Reasoning (explaining why),Simulation (generating plausible alternatives), and Agency (autonomous analysis).

ECCV 2026

STEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detection

Jakub Micorek, Mateusz Kozinski, Horst Possegger

Skeleton-based Video Anomaly Detection (VAD) offers a ro-bust, privacy-preserving solution for identifying abnormal behaviors. Tomodel the distribution of normal static and moving poses, recent meth-ods train Energy-Based Models (EBMs) via Denoising Score Matching(DSM). However, directly injecting noise, required for training, into rawjoint coordinates creates physically impossible poses, and this structuralcollapse severely worsens as the temporal window expands. To addressthis, we introduce STEP, a simple framework that utilizes PrincipalComponent Analysis (PCA) to project pose sequences into a compact,whitened PC-space. Learning the data density within this well-behavedPC-space ensures that the injected noise translates into physically plau-sible variations, which allows the model to process longer video sequenceswithout the performance collapse of raw coordinate baselines. Addition-ally, to mitigate inherent pose estimation inaccuracies arising from occlu-sions or motion blur, we integrate a sequence-level weighting mechanismbased on the estimator’s confidence scores. Operating at real-time com-putational efficiency, our simple and lightweight framework outperformsthe previous skeleton-based state-of-the-art by 12.2% (90.1% AUROC)on the challenging UBnormal dataset and achieves highly competitiveresults by improving on the ShanghaiTech benchmark.

ECCV 2026

ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling

Yawen Luo, Xiaoyu Shi, Jun-hao Zhuang, Yutian Chen, Quande Liu, Xintao Wang, Pengfei Wan, Tianfan Xue

Multi-shot video generation is crucial for long narrative storytelling, yet current bidirectional architectures suffer from limited interactivity and high latency. We propose ShotStream, a novel causal multi-shot architecture that enables interactive storytelling and efficient on-the-fly frame generation. By reformulating the task as next-shot generation conditioned on historical context, ShotStream allows users to dynamically instruct ongoing narratives via streaming prompts. We achieve this by first fine-tuning a text-to-video model into a bidirectional nextshot generator, which is then distilled into a causal student via Distribution Matching Distillation. To overcome the challenges of inter-shot consistency and error accumulation inherent in autoregressive generation, we introduce two key innovations. First, a dual-cache memory mechanism preserves visual coherence: a global context cache retains conditional frames for inter-shot consistency, while a local context cache holds generated frames within the current shot for intra-shot consistency. And a RoPE discontinuity indicator is employed to explicitly distinguish the two caches to eliminate ambiguity. Second, to mitigate error accumulation, we propose a two-stage distillation strategy. This begins with intra-shot self-forcing conditioned on ground-truth historical shots and progressively extends to inter-shot self-forcing using self-generated histories, effectively bridging the train-test gap. Extensive experiments demonstrate that ShotStream generates coherent multi-shot videos with sub-second latency, achieving 16 FPS on a single GPU. It matches or exceeds the quality of slower bidirectional models, paving the way for real-time interactive storytelling. Code and models will be released.

ECCV 2026

Target-aware Image Editing via Cycle-consistent Constraints

Yanghao Wang, Zhen Wang, Long Chen

Recent pre-trained text-to-image flow models have enabledremarkable progress in text-based image editing. Mainstream approachesadopt a corruption-then-restoration paradigm, where the source im-age is first corrupted into an editable “intermediate state” and then re-stored to the target image under the prompt guidance. However, currentmethods construct this intermediate state in a target-agnostic manner,i.e., they mainly focus on realizing source image reconstruction whileneglecting the semantic gaps towards the specific editing target. Thisdesign inherently results in limited editability or inconsistency when thedesired modifications substantially deviate from the source. In this pa-per, we argue that the intermediate state should be target-aware, i.e.,selectively corrupting editing-relevant contents while preserving editing-irrelevant ones. Thus, we propose FlowCycle, an inversion-free and flow-based editing framework that parameterizes corruption with learnablenoises and optimizes them through a cycle-consistent process. By itera-tively editing the source to the target and recovering back to the sourcewith dual consistency constraints, FlowCycle learns to produce a target-aware intermediate state, enabling faithful modifications while preservingsource consistency. For efficiency, we further accelerate the optimizationby dynamically adjusting the sampling steps. Extensive ablations demon-strated that FlowCycle achieves superior editing performance.

ECCV 2026

Natural Image Pretraining Improves Abstract Reasoning

Xiaoman Ding, Keya Hu, Katelyn Gan, Victor Yin, Kaiming He

EgoExo proficiency estimation aims to assess action qual-ity by integrating fine-grained motion cues from egocentric (1st-person)views with spatial context from multiple exocentric (3rd-person) views.Simply adding more exocentric views degrades EgoExo performance, asredundant or noisy perspectives dilute useful motion cues. Our anal-ysis identifies two key causes: (1) Multiview redundancy — Fromthe data perspective, certain views provide limited or noisy informa-tion, diluting discriminative cues; (2) Overfitting — From the featureperspective, conventional fusion increases representational complexity,causing the model to memorise view-specific patterns rather than learngeneralisable representations. To address these issues, we propose twocomplementary modules. AdaMVS adaptively identifies and fuses themost informative view tokens under weak supervision from the data per-spective, while VIB-GB combines Gradient Blending and VariationalInformation Bottleneck regularisation from the feature perspective tocompress redundant signals and suppress overfitting during training.Experiments on EgoExo-4D and EgoExo-Fitness demonstrate that ourmethod learns both which view to look at and how to fuse them,achieving new state-of-the-art results. Our source code is available athttps://github.com/dx199771/AdaMVS

ECCV 2026

Semantic Generative Tuning for Unified Multimodal Models

Songsong Yu, Yuxin Chen, Ying Shan, Yanwei Li

Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training paradigms independently optimize understanding via sparse text signals and generation through dense pixel objectives. Such a decoupled strategy yields misaligned representation spaces, isolating visual understanding from generation and hindering their mutual reinforcement. This work presents the first systematic investigation into generative post-training, where we formulate hierarchical visual tasks as generative proxies to bridge the isolation in UMMs. Our empirical investigation reveals that high-level semantic tasks, particularly image segmentation, serve as optimal proxies. Unlike low-level tasks that distract models with texture details, segmentation provides structural semantics that significantly enhance both vision-centric perception and generative layout fidelity. Building upon these insights, we introduce Semantic Generative Tuning (SGT), a novel paradigm that leverages segmentation as a generative proxy to align and synergize multimodal capabilities. Mechanistic analyses further demonstrate that SGT fundamentally improves feature linear separability and optimizes visualtextual attention allocation pattern. Extensive evaluations show that SGT consistently improves both multimodal comprehension and generative fidelity across mainstream benchmarks. Our code is available on the Project Page.

ECCV 2026

Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation

Yingshu Li, Yunyi Liu, Zhenghao Chen, Tong Chen, Zailong Chen, Lingqiao Liu, Lei Wang, Luping Zhou

Despite rapid advances in chest X-ray (CXR) foundationmodels, most radiology report generation (RRG) systems still rely onheavily downsampled inputs (e.g., 256×256) due to the fixed visual tokenbudgets of pretrained vision encoders, suppressing subtle yet clinicallyimportant cues present in native-resolution images. However, enablinghigh-resolution (high-res) perception remains challenging: naïve tilingcauses prohibitive token inflation, while global compression suppressessubtle lesions and degrades diagnostic fidelity. Inspired by radiologists’workflow, localizing suspicious regions before detailed high-res assess-ment. We propose Lesion-Aware High-Resolution Patch Discovery andFusion for Chest X-ray Reporting (LePaX), the first RRG frameworkthat enables efficient high-res CXR perception (up to 1920×1920) with-out increasing the vision-token count. LePaX formulates high-res percep-tion as a constrained spatial resolution allocation problem under a fixedtoken budget and introduces two key components: Learnable Spatial Res-olution Allocation (LSRA), which learns a spatial utility map that adap-tively allocates limited high-res capacity to diagnostically relevant re-gions, enabling targeted extraction of high-res patches from native CXRs;and Global–Regional Fusion (GRF), which performs token-preservingregion-to-global refinement by projecting high-resolution regional evi-dence back onto the global feature grid through spatially aligned resolu-tion write-back, avoiding token inflation. Experiments on multiple CXRbenchmarks demonstrate that LePaX consistently improves both clinicaland linguistic metrics while enabling native-resolution CXR perceptionwith over 10× fewer visual tokens than naïve high-res tiling.

ECCV 2026

Robust Self-Supervised Cross-Modal Super-Resolution against Real-World Misaligned Observations

Xiaoyu Dong, Jiahuan Li, Ziteng Cui, Naoto Yokoya

Cross-modal super-resolution (SR) on real-world misaligneddata is challenging, as only unlabeled low-resolution (LR) source andhigh-resolution (HR) guide images with complex spatial misalignmentare available. Previous methods either rely on simulated training dataor adopt suboptimal alignment strategies that overlook cross-modal de-pendencies, limiting their practical performance. To address these is-sues, we propose RobSelf, a self-supervised model that jointly optimizesa misalignment-aware feature translator and a content-aware referencefilter online. The translator resolves unsupervised cross-modal and cross-resolution alignment via weakly-supervised, misalignment-aware trans-lation, yielding an aligned guide feature. Guided by this feature, thefilter performs reference-based discriminative self-enhancement on thesource, enabling SR prediction with high resolution and high fidelity.Experiments on synthesized data and collected real-world data demon-strate that RobSelf achieves state-of-the-art performance, outperformingexisting self-supervised and supervised methods. Moreover, it achievessuperior efficiency, being up to 15.3× faster than prior self-supervisedmethods. https://github.com/palmdong/RobSelf

ECCV 2026

GR-GRPO: Graph-Diffused Credit for Autoregressive Image RL Alignment

Zheyu Zhang, Peng-Tao Jiang, Tianyi Zheng, Jian Zhang, Jinwei Chen, Bo Li

Reinforcement-learning alignment for autoregressive imagegeneration is bottlenecked by expensive rollouts: in GRPO-style groupoptimization, sampling G rollouts per prompt improves exploration andwithin-group credit estimation but multiplies sampling cost. This chal-lenge is amplified by vector-quantized (VQ) tokenization, where nearbycodebook IDs in embedding space can be locally substitutable; however,GRPO-style updates provide explicit positive credit only to sampled IDs,leading to insufficient coverage of success-supported alternatives undersmall-G training. We propose GR-GRPO (Graph-Regularized GRPO),which densifies token-level supervision without additional rollouts by dif-fusing positive evidence from positive rollouts over a precomputed code-book K-NN graph to construct graph-diffused soft targets, and regulariz-ing the policy toward these targets via an auxiliary cross-entropy term. Aconfidence-adaptive gate modulates diffusion strength based on the pol-icy’s logit margin, and a difficulty-aware budget allocation scheme fur-ther improves rollout utilization across prompts. Extensive experimentson compositional image generation (GenEval) and preference-style align-ment (DrawBench) validate the effectiveness of GR-GRPO. In particular,GR-GRPO achieves a substantially better compute–performance trade-off, surpassing large-group training (G=64) with ∼ 4.6× less rollout timeon Janus-Pro-1B. Our code and model weights are available athttps://github.com/vivoCameraResearch/GR-GRPO.

ECCV 2026

ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection

Camile Lendering, Erkut Akdag, Joaquin Figueira Chacon, Egor Bondarev

Unified anomaly detection requires modeling highly heteroge-neous normal data without access to anomalous samples. While foundationmodels like DINOv2 provide rich token representations, leveraging thesespaces for explicit density estimation remains challenging. Energy-BasedModels (EBMs) offer a principled formulation, but their training inhigh-dimensional token spaces is unstable due to anisotropy and strongcross-dimensional correlations, which degrades finite-step Markov ChainMonte Carlo (MCMC) sampling. We identify this instability as fundamen-tally geometric and introduce ReFP-AD (Rectified Flow Preconditioningfor Anomaly Detection), which learns a geometric reparameterization thatmaps high-dimensional embeddings into a well-conditioned latent spacevia an optimal transport (OT)-coupled rectified flow. This precondition-ing enables stable persistent contrastive divergence with preconditionedStochastic Gradient Langevin Dynamics (SGLD) in full-dimensional tokenspaces. Anomaly scores are then derived from the learned energy landscapeusing gradient norms. Under a strict unified protocol on the MVTec-ADand VisA datasets, ReFP-AD achieves 98.6%/97.9% Image/Pixel AUROCon MVTec-AD and 97.3%/99.0% on VisA, outperforming prior unifiedEBM baselines by up to +10.8% in Image AUROC. Ablation experimentsdemonstrate that geometric reparameterization is critical for finite-stepMCMC and accurate anomaly localization in high-dimensional tokenspaces. Code is available at https://github.com/CLendering/ReFP-AD

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.