ECCV 2026 论文列表

Conventional RGB cameras suffer from lighting artifacts suchas flare, glare, flicker, and overexposure, leading to irrecoverable informa-tion loss that necessitates computational restoration. However, existingapproaches treat these problems in isolation, failing to recover structuraldetails completely obscured by complex spatially discrete image degrada-tions. In this paper, we propose a novel cross-modal restoration paradigmand present DeLux, a modular proof-of-concept pipeline that leveragesneuromorphic event streams as a structural prior to guide the targeteddetection and inpainting of lighting artifacts in RGB video. Validation onsynthetic benchmarks and real-world automotive footage demonstratesthat DeLux effectively suppresses local artifacts and restores affected re-gions. The proposed approach outperforms existing RGB-only baselinesand event-guided HDR models, achieving an average MS-SSIM of over0.99 across all artifact types and demonstrating up to an 88% reductionin artifact severity in real-world automotive footage. The synthetic arti-fact generation tools and curated real-world evaluation datasets are madepublicly available to foster future research on cross-modal restoration.

In autonomous driving perception, the fusion of LiDAR andcamera modalities has become the dominant paradigm for 3D object de-tection. However, current multi-modal frameworks heavily rely on mas-sive visual backbones pretrained on 2D semantic tasks. This relianceintroduces substantial parameter redundancy and a structural misalign-ment, as 2D priors are ill-equipped to handle the extreme sparsity of Li-DAR projections required for Bird’s-Eye-View geometry. To address this,we present DeGuNet, an ultra-compact and plug-and-play image back-bone explicitly designed for depth-guided representation learning. By in-corporating sparsity-aware feature extraction mechanisms, DeGuNet ef-fectively aligns multi-view images with unstructured LiDAR depth whilestrictly preventing invalid-region contamination. Extensive experimentson the nuScenes dataset demonstrate DeGuNet’s broad plug-and-playapplicability and superior efficiency. When integrated into establishedbaselines, it fundamentally eliminates architectural redundancy, reduc-ing GPU memory consumption by up to 66.5% and achieving a 1.16×inference speedup. Concurrently, DeGuNet delivers up to a 6.20 absolutemAP gain, establishing a new paradigm for parameter-efficient multi-modal 3D perception.

Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make inference prohibitively expensive. Existing compression methods typically rely on fixed window partitioning and attention-based pruning, which overlook the piecewise semantic structure of audio-visual signals and become fragile under aggressive token reduction. We propose Dynamic Audio-driven Semantic cHunking (DASH), a training-free framework that aligns token compression with semantic structure. DASH treats audio embeddings as a semantic anchor and detects boundary candidates via cosine-similarity discontinuities, inducing dynamic, variablelength segments that approximate the underlying piecewise-coherent organization of the sequence. These boundaries are projected onto video tokens as a soft temporally co-registered segmentation prior. Within each segment, token retention is determined by a tri-signal importance estimator that fuses structural boundary cues, representational distinctiveness, and attention-based salience, mitigating the sparsity bias of attentiononly selection. This structure-aware allocation preserves transition-critical tokens while reducing redundant regions. Extensive experiments on AVUT, VideoMME, and WorldSense demonstrate that DASH maintains competitive or superior accuracy while achieving higher compression ratios compared to prior methods. Code is available at: https://github.com/ laychou666/DASH.

Although highly effective in vision and language domains, applying in-context learning to robotics remains challenging. Existing autoregressive in-context imitation methods discretize continuous actions and exacerbate the accumulation of early prediction errors through next-token prediction, limiting their generalization on unseen task configurations. Meanwhile, flow-matching policies have been explored for continuous robot control and can help mitigate compounding errors; however, in-context imitation learning within a flow-matching framework remains underexplored. To address these limitations, we introduce ContextFlow, a conditional flow-matching model that learns continuous action distributions for in-context imitation learning. ContextFlow conditions flow-based action prediction on demonstrations and observations, enabling robust generation from noisy action distributions. To better encode multimodal in-context demonstrations, we adapt perceiver-style multimodal context compressors that distill visual, proprioceptive, and action sequences into compact, task-relevant latent representations. On LIBERO, ContextFlow outperforms ICRT by 35 percentage points in average success rate on unseen task configurations, while matching the performance of the task-specific fine-tuned VLA model π with- 0 out any fine-tuning on unseen tasks. On real robots, it generalizes to unseen configurations of both single-arm and bimanual tasks, achieving 40% success on a new pen-uncapping configuration. Project Page: https://dingjiansw101.github.io/contextflow-page/.

Multimodal Large Language Models (MLLMs) have demon-strated remarkable potential in medical image analysis. However, theirapplication in gastrointestinal endoscopy is currently hindered by twocritical limitations: the misalignment between general model reasoningand standardized clinical cognitive pathways, and the lack of causal as-sociation between visual features and diagnostic outcomes. In this paper,we propose a novel Clinical-Cognitive-Aligned (CogAlign) framework toaddress these challenges. First, we endow the model with rigorous clinicalanalytical capabilities by constructing the hierarchical clinical cognitiondataset and employing Supervised Fine-Tuning (SFT). Unlike conven-tional approaches, this strategy internalizes the hierarchical diagnosticlogic of experts, ranging from anatomical localization and morphologicalevaluation to microvascular analysis, directly into the model. Second, toeliminate visual bias, we provide a theoretical analysis demonstratingthat standard supervised tuning inevitably converges to spurious back-ground correlations. Guided by this insight, we propose a counterfactual-driven reinforcement learning strategy to enforce causal rectification. Bygenerating counterfactual normal samples via lesion masking and opti-mizing through clinical-cognition-centric rewards, we constrain the modelto strictly ground its diagnosis in causal lesion features. Extensive exper-iments demonstrate that our approach achieves State-of-the-Art (SoTA)performance across multiple benchmarks, significantly enhancing diag-nostic accuracy in complex clinical scenarios.

Traditional Visual Grounding (VG) predominantly relies ontextual descriptions to localize objects, a paradigm that inherently strug-gles with linguistic ambiguity and often ignores non-verbal deictic cuesprevalent in real-world interactions. In natural egocentric engagements,hand-pointing combined with speech forms the most intuitive referringmechanism. To bridge this gap, we introduce EgoPoint-Ground, thefirst large-scale multimodal dataset dedicated to egocentric deictic visualgrounding. Comprising over 15k interactive samples in complex scenes,the dataset provides rich, multi-grained annotations including hand-targetbounding box pairs and dense semantic captions. We establish a com-prehensive benchmark for hand-pointing referring expression resolution,evaluating a wide spectrum of mainstream Multimodal Large LanguageModels (MLLMs) and state-of-the-art VG architectures. Furthermore, wepropose SV-CoT, a novel baseline framework that reformulates groundingas a structured inference process, synergizing gestural and linguistic cuesthrough a Visual Chain-of-Thought paradigm. Extensive experimentsdemonstrate that SV-CoT achieves an 11.7% absolute improvement overexisting methods, effectively mitigating semantic ambiguity and advanc-ing the capability of agents to comprehend multimodal physical intents.Code: https://github.com/lingli1724/Egopoint

Diffusion models have achieved state-of-the-art performanceacross 2D, video, and 3D generation, but their sampling quality remainsheavily dependent on guidance methods. Existing training-free guidancetechniques construct a weakened auxiliary branch through static per-turbations, yet these methods fundamentally disregard the stage-wisestructure of the denoising process: early timesteps govern magnitude-driven global formation, whereas later timesteps refine directional ge-ometry. We propose a principled training-free diffusion guidance withStage-Aware Noise Decomposition (SAND) that considers this temporalasymmetry by decomposing Gaussian noise into orthogonal magnitude-and direction-oriented components and modulating them with a time-adaptive weighting schedule aligned to diffusion dynamics. The decom-posed perturbations are injected directly into the self-attention queries,keys, and values, forming a dynamically degraded branch that requires noretraining while faithfully mirroring the evolving geometry of the sample.This representation-level perturbation provides a temporally coherentguidance signal that better balances global structure and fine detail. Ex-tensive experiments on Stable Diffusion XL (SDXL) and Stable Diffusion3 (SD3) demonstrate that our method consistently improves global co-herence, sharpens fine-grained texture, and yields superior sample qual-ity in both unconditional and conditional image generation. We furthershow that SAND can be extended to the guidance-distilled image gener-ation model FLUX.1 and the text-to-video model Wan2.2, highlightingits broad applicability.

Recently, visual token pruning has been studied to handlethe vast number of visual tokens in Multimodal Large Language Models.However, we observe that while existing pruning methods perform reliablyon simple visual understanding, they struggle to effectively generalizeto complex visual reasoning tasks, a critical gap underexplored in previ-ous studies. Through a systematic analysis, we identify Relevant VisualInformation Shift (RVIS) during decoding as the primary failure driver.To address this, we propose Decoding-stage Shift-aware Token Pruning(DSTP), a training-free add-on framework that enables existing pruningmethods to align visual tokens with shifting reasoning requirements duringthe decoding stage. Extensive experiments demonstrate that DSTP signifi-cantly mitigates performance degradation of pruning methods in complexreasoning tasks, while consistently yielding performance gains even acrossvisual understanding benchmarks. Furthermore, DSTP demonstrates ef-fectiveness across diverse state-of-the-art architectures, highlighting itsgeneralizability and efficiency with minimal computational overhead. Oursource code is available here.

While Vision-Language-Action (VLA) models have emergedas powerful generalist policies, their severe vulnerability to adversarialpatches significantly hinders their deployment in safety-critical domains.Moreover, existing patch attacks primarily focus on white-box settings,heavily overfitting to the specific action output space of the target model,which results in poor cross-architecture transferability. To overcome thislimitation, we propose VLA-Hijack, a unified adversarial framework thatbreaks the transferability bottleneck by exploiting a fundamental vulner-ability identified in this work: before planning any motion, a VLA modelmust first use visual information to locate its own robotic arm withinthe environment. Targeting this shared visual self-localization process,our approach concurrently optimizes Attention-Guided ProprioceptiveSuppression to inhibit the real robotic arm’s features, and MultimodalProprioceptive Injection to establish the patch as a surrogate "phantomembodiment". By alternating between semantic concept anchoring andvisual prototype projection, VLA-Hijack effectively severs the semanticrelationship between the agent’s true embodiment and its control pol-icy. Extensive experiments across diverse architectures (OpenVLA, Uni-VLA, and CronusVLA) demonstrate that VLA-Hijack achieves superioroptimization efficiency in white-box settings and sets a new SOTA forcross-architecture and cross-domain black-box transferability.

Fine-grained robotic manipulation depends on understand-ing parts, not only whole objects. Existing 3D foundation models tendto be either generalized but object-aware, or part-aware but limited toclosed-set taxonomies, which weakens zero-shot transfer. We study text-conditioned 3D part segmentation, where a free-form phrase selects afunctional part on point cloud. We introduce UniPart, a feed-forwardcross-modal 3D Transformer that conditions CLIP text embedding. Toscale supervision, we build LangPart-1M with 160K+ Objaverse assetsand 8M text to part pairs using multi-view consistent part generation.We further manually label a high-quality subset, LangPart-4K, for fine-tuning and evaluation. UniPart achieves strong zero-shot results on open-vocabulary part benchmarks and transfers to language-conditioned partgrasping in real world.

Diffusion models have achieved remarkable success across di-verse domains, with performance closely related to the denoising back-bones that parameterize the score function. In this paper, we presenta systematic, phase-aware analysis of diffusion components and showthat abrupt, early-stage fluctuations in deep latents are strongly asso-ciated with artifacts. Guided by these findings, we introduce DUNE(Diffusion Unified Network refiNEr), a training-free refinement frame-work that detects abrupt deviations in deep low-noise internal latentsusing a shared EMA-based criterion, and applies backbone-specific sup-pression to the detector-selected entries. Although derived from U-Net,the same detect–suppress principle extends naturally to Transformer-based diffusion models by acting on the latents of deep self-attentionblocks. Extensive experiments across multiple backbones indicate thatDUNE improves fidelity while reducing hallucinations, offering new in-sight into where and when diffusion backbones should be controlled.

Though Gaussian splatting has achieved impressive resultsin novel view synthesis, it requires millions of primitives to representhighly textured scenes — even when the underlying geometry is simple.We propose a representation that decouples geometry and appearanceto achieve a compact and computationally efficient representation. Ourmethod uses surfels for geometry and combines a global neural field withper-primitive colors for appearance. While existing approaches that tex-ture primitives incur significant overhead in either memory or renderingspeed, our method strikes a better balance by leveraging the neural fieldto implicitly compress texture information. We bound the computationsrequired in the rendering pass by querying the neural field for a fixednumber of primitives per pixel, enabling real-time rendering speeds. Ourrepresentation matches the perceptual quality of 3D Gaussian splattingwhile using 9.7× fewer primitives and 5.5× less memory on outdoorscenes, and 31× fewer primitives and 3.7× less memory on indoor scenes.

Large Vision-Language Models (LVLMs) remain bottleneckedby massive computational footprints, precluding their deployment onresource-constrained edge devices. While efforts to compress LVLMs fo-cus heavily on vision token reduction or smaller language models, thevision encoder is largely overlooked, typically deployed as a monolithic,computationally heavy feature extractor. Moreover, there is no previouseffort that designs a vision encoder for LVLMs directly optimized foron-device latency. In this paper, we present UltraViT, a vision encoderfor LVLMs, explicitly designed and optimized for on-device performance.Specifically, by taking into account real on-device latencies, we system-atically design a pyramidal architecture that strategically integrates andadapts heterogeneous spatial mixers at the macro-block level. Further-more, to pre-train UltraViT, we propose a novel two-stage generativepre-training strategy: cultivating rich spatial features via dense distil-lation, followed by direct generative supervision from a capacity-mixedfrozen LLM. Compared to standard contrastive and SSL, we show thatour pre-training is much more effective for achieving high-level seman-tic grounding for UltraViT needed for the subsequent generative multi-modal alignment of LVLM training. Extensive experiments demonstratethat our on-device latency-informed design combined with our tailoredtraining strategy establishes a new state-of-the-art for efficient LVLMencoding, significantly outperforming existing encoder-centric baselineswhile operating on-device at nearly 1.7× the speed.

Native 3D generative models have achieved remarkable _x001C_-delity and speed, yet they su_x001B_er from a critical limitation: inability toprescribe precise structural articulations, where precise structural con-trol within the native 3D space remains underexplored. This paper pro-poses SK-Adapter, a simple yet e_x001E_cient and e_x001B_ective framework thatunlocks precise skeletal manipulation for native 3D generation. Mov-ing beyond text or image prompts, which can be ambiguous for precisestructure, we treat the 3D skeleton as a _x001C_rst-class control signal. SK-Adapter is a lightweight structural adapter network that encodes jointcoordinates and topology into learnable tokens, which are injected intothe frozen 3D generation backbone via cross-attention. This design al-lows the model to not only e_x001B_ectively _x0010_attend_x0011_ to speci_x001C_c 3D structuralconstraints but also preserve its original generative priors. To bridgethe data gap, we contribute the Objaverse-TMS dataset, a large-scale⋆ „Equal contribution. Equal advising.dataset of 24k text-mesh-skeleton pairs. Extensive experiments con_x001C_rmthat our method achieves robust structural control while preserving thegeometry and texture quality of the foundation model, signi_x001C_cantly out-performing existing baselines. Furthermore, we extend this capability tolocal 3D editing, enabling region-speci_x001C_c editing of existing assets withskeletal guidance, which is unattainable by previous methods. Projectpage: https://sk-adapter.github.io/

Current sign language translation (SLT) systems attempt tolearn all aspects of signing—manual gestures, high-speed fingerspelling,and asynchronous non-manual facial cues—within a single end-to-endnetwork. Learning multiple tasks without detailed supervision leads topoor recognition of fingerspelled proper nouns and technical terms, andleaves rich disambiguating information from lip movements largely unex-ploited. We introduce SignBind-LLM, a modular framework that ad-dresses these limitations through three dedicated expert streams: one forcontinuous signing, one for fingerspelling, and one for lipreading. Eachexpert is pre-trained independently using CTC on approximately twomillion automatically generated pseudo-gloss sequences, removing theneed for manual gloss annotation. A lightweight transformer with learnedtemporal alignment fuses the expert outputs, and a pre-trained languagemodel translates the resulting pseudo-gloss sequences into fluent spokenEnglish. At matched decoder scale (250M parameters), our architecturealready surpasses all prior methods, confirming that the gains are archi-tectural rather than a consequence of scaling the language model. Scal-ing to a larger decoder sets a new state-of-the-art across How2Sign: 23.1,BOBSL: 7.0, and ChicagoFSWild+: 73.6%, while requiring significantlylower training cost than prior approaches.

Cross-View Geo-Localization (CVGL) is crucial for naviga-tion in GPS-restricted environments. However, bridging the severe per-spective gap between ground and aerial views remains challenging. Exist-ing approaches either rely on coarse image-level retrieval, suffer from un-stable optimization in pose regression, or require expensive fine-grainedannotations. These limitations suggest that current formulations of cross-view geo-localization are insufficient for reliable fine-grained localization.To address this limitation, we introduce a new task, Sector-Level Cross-View Geo-Localization (SLCVGL), which aims to perform fine-grainedsector-level geo-localization of a limited Field-of-View (FoV) ground im-age within an omnidirectional aerial image, while simultaneously infer-ring its ground-view orientation without manually annotated pose labels.To operationalize this task, we propose an Azimuthal Scanning frame-work. It decomposes aerial feature maps into multiple azimuthal sectors,instead of relying on holistic matching. By aligning ground features withthese sectors, the model explicitly retrieves a sector-level sub-region andimplicitly estimates orientation, eliminating the need for continuous poseregression supervision. This design also reduces interference from unob-served overhead regions. To benchmark the proposed task, we repur-pose standard panoramic datasets into a limited-FoV sector-level proto-col with random FoV cropping and soft labeling. Extensive experimentson CVUSA and CVACT demonstrate improvements over state-of-the-art methods, with a 30.17% R@1 improvement on CVACT under the 70°FoV setting, while maintaining strong robustness across varying FoVs.

Spatio-Temporal Video Grounding (STVG) aims to retrievethe visual trajectory of a specific object from a video stream as describedby a natural language expression. However, most advanced methodsstruggle to balance global context modeling with precise boundary lo-calization. Due to the prohibitive computational costs of processing longvideos, these approaches typically resort to low-rate temporal downsam-pling and implicit motion modeling. This inevitably suppresses high-frequency boundary cues and neglects the explicit inter-frame depen-dencies required for precise boundary delineation. To address these lim-itations, we present ScanFocus, a novel coarse-to-fine framework thatdecouples the STVG task into a global spatio-temporal scan and a localboundary focus. Specifically, we utilize a unified vision-language fusionencoder combined with a lightweight Deformable Semantic-Motion Fu-sion module to efficiently align multimodal features and generate coarseproposals. To recover the suppressed fine-grained details, we introducethe Semantic-Guided Temporal Aggregator (SGTA) in the refinementstage. By densely sampling around coarse boundaries, SGTA explicitlymodels short-term temporal interactions under semantic guidance, cap-turing rapid motion changes for precise timestamp regression. Extensiveexperiments on three widely used benchmarks demonstrate the perfor-mance superiority of our proposed method over previous approaches.Code will be released at https://github.com/TenMinutes209/ScanFocus.

The global rose cultivation industry has experienced continued expansion in recent years. There has been a set of value evaluation criteria covering multiple dimensions for roses. However, the evaluations are mainly conducted by human experts after harvesting, which is time-consuming and labor-intensive. 3D computer vision technologies are promising to automate the process before harvesting, but the lack of large-scale datasets with localization annotations that capture plant architecture hinders the progress. To bridge this gap, we propose Rosetum3D, which is a large-scale 3D vision dataset for preharvest roses. The dataset is constructed via occlusion-robust multi-view RGB-D capture protocols in commercial greenhouses. We provide fine-grained 2D localization labels using bounding boxes and botanically defined keypoints, and then obtain the 3D structures recovered through depth backprojection. Rosetum3D contains 21,114 images and 46,848 annotated rose objects. Models trained on Rosetum3D have achieved 2D/3D rose localization, which is a crucial step for automated preharvest quality grading and growth monitoring. Beyond localization, Rosetum3D serves as a benchmark for agricultural vision tasks, including 2D rose object detection, local feature matching, depth estimation, and instance reidentification. By enabling data-driven precision agriculture, Rosetum3D paves the way for robotic harvesting systems and AI-driven yield prediction in protected cultivation. The dataset are available at https: //huggingface.co/datasets/WaterMelon2333/Rosetum3D/tree/main.

Recent advances in text-to-image (T2I) models have revolu-tionized the field of image generation and editing. However, identifyingsemantics that a T2I model can successfully edit in an image contin-ues to be a challenging task. Most existing approaches require usersto manually specify semantics to modify a particular image, a time-consuming process that often involves extensive trial and error. In thispaper, we present RankT2I, a novel, training-free, and model-agnosticframework that automates the discovery of editable semantics in diffu-sion and FLUX-based models. Given a visual domain, we first utilizea multimodal vision-language model to gather a broad set of candidatesemantics. We then frame semantic discovery as a set selection problemand use a submodular objective to identify semantics that are relevant,editable, and diverse. Our method helps users efficiently identify a widerange of semantics for text-to-image editing models across several do-mains while outperforming existing methods.