ECCV 2026 论文列表

DNNs are highly susceptible to noisy labels, often leadingto unstable training and severe performance degradation. Graph-basedmethods have emerged as a promising solution, utilizing sample rela-tionships to refine labels through neighborhood aggregation. However,most existing approaches rely on dense connectivity (e.g., KNN graphs),which tends to aggregate noisy cues and cause oversmoothing. Whilesparse graphs offer a potential remedy, current sparse topologies oftensuffer from blind error propagation between samples, particularly forhard samples near class boundaries. To address these challenges, we pro-pose Maximum Spanning Tree (MST) Guided Confidence and SparseGraph Network (MST-GCSN). We introduce the MST as a robust sparsebackbone to filter out redundant local noise while preserving a globalmanifold skeleton. To rectify the indiscriminate mutual influence betweennodes, a Confidence-Gated Propagation (CGP) mechanism is designedto adaptively regulate information flow based on node reliability, ensur-ing that only high-confidence semantic signals are propagated. Buildingon this optimized structure, a Progressive Label Prediction (PLP) mod-ule integrates local information and global anchor-guided cues to itera-tively correct labels. Extensive experiments on synthetic and real-worldnoisy datasets demonstrate that MST-GCSN significantly enhances ro-bustness.

Supervised learning for image segmentation typically requires spatially aligned image and label sets. When images and labels originate from different sources, the pairing may be misaligned, which can significantly deteriorate the performance of the learned models. This is especially common in remote sensing, when aerial or satellite images are co-registered with labels from another source (e.g., OpenStreetMap). In this work, we propose a novel approach for training on misaligned labels, where we simultaneously learn the label alignment. Our align and segment (AnS) approach builds on the spatial transformer module to transform the misaligned labels using an affine transformation to provide a better learning target for a canonical semantic segmentation network. We prevent shortcut learning of misaligned labels in these semantic segmentation networks through a self-supervised regularization loss and show that it is complementary to data augmentation, especially for systematically misaligned training data. A decisive characteristic of our AnS approach is that it learns without requiring any “golden” labels. We experimentally show on both synthetic and real-world data from different cities that our approach enables high-quality building segmentation and precise label-image alignment at the same time. Code and derived datasets are available at https://github.com/venkanna37/align-and-segment.

Visual fall detection models are usually trained on small,staged datasets. Their real-world utility remains unclear; such data lacksdiversity and evaluation protocols differ from paper to paper. We proposeOmniFall, a unified benchmark of 15k videos (80 hours) with frame-levelannotations in a single 16-class taxonomy. It spans three domains: OF-Staged unifies eight staged datasets with cross-subject and cross-viewsplits; OF-Synthetic adds 12k videos (17 h) with controlled demographicand environmental diversity; and OF-In-the-Wild provides a test-onlyset of genuine accident videos. We evaluate fine-tuned models as wellas much larger zero-shot multimodal LLMs. On in-the-wild fall events,both do comparably well. The clinically critical fallen state is wherethey part: zero-shot models keep confusing fallen with lying, whereasmodels fine-tuned on synthetic data with explicit fallen-state scenes dosubstantially better. We release the unified annotations, the syntheticdata, and the in-the-wild test set to foster the development of fall andfallen-state detectors for uncontrolled environments.Dataset: https://hf.co/datasets/simplexsigil2/omnifall

We investigate the problem of identifying objects that havebeen added, removed, or moved between a pair of captures (imagesor videos) of the same scene at different times. Accurately identifyingverifiable changes is extremely challenging – some objects may appear tobe missing because they are occluded or out of frame, while others mayappear different due to large viewpoint changes. To study this problem, weintroduce the SceneDiff Benchmark, the first multiview change detectiondataset for scenes captured along different camera trajectories, comprising350 diverse video pairs with dense object instance-level annotations. Wealso introduce the SceneDiff algorithm, a training-free approach thatsolves for image poses, segments images into objects, and compares themusing semantic and geometric features. By building on pretrained models,SceneDiff generalizes across domains without retraining and naturallyimproves as the underlying models advance. Experiments on multiviewand two-view benchmarks demonstrate that our method outperformsexisting approaches by large margins (51.6% and 30.6% relative APimprovements). Project page: https://yuqunw.github.io/SceneDiff

Online 3D reconstruction models perform poorly on longvideos. This happens because regressing poses relative to a fixed first-frameanchor forces extrapolation far beyond the training distribution. Smalldrifts accumulate and amplify into significant geometric collapse. However,we observe that per-frame depth remains stable throughout this failure.The backbone’s local geometry remains intact; only the global pose headbreaks down. Motivated by this decoupling, we introduce Scal3R. Thisapproach reformulates online reconstruction as multi-reference relativepose querying. We use lightweight learnable tokens, which make upabout ∼1% of the parameters, and inject them into a completely frozenbackbone via asymmetric attention. This setup queries poses relative tomultiple past keyframes. An online pose-graph optimization system withloop closure suppresses long-range drift. Scal3R reaches convergence in8 hours on a single GPU. It reduces the average ATE by over 60% onKITTI compared to the online baseline. It also achieves state-of-the-artperformance across Virtual KITTI, Sintel, TUM-Dynamic, ScanNet, and7-Scenes. Project page: https://linjohnss.github.io/scal3r/

Image-to-image (I2I) translation has achieved strong resultsin tasks like human relighting and driving scene translation using la-tent diffusion models (LDMs). However, compact LDMs often strug-gle to preserve fine-grained structures because the encoder compresseshigh-resolution inputs into a spatially downsampled latent space. Toaddress this issue, we propose a simple saliency-guided warp–unwarpframework that reallocates spatial representation toward salient regionsbefore encoding, enabling better preservation of structural details with-out increasing latent resolution. The warped image is processed by theoriginal diffusion model and then mapped back via an inverse warp.In addition, we propose a simple and efficient outpainting-based syn-thetic data generation pipeline to produce high-quality paired data forimage relighting. Our method is model-agnostic, requires no architec-tural modification, and introduces negligible computational overhead.Experiments on human relighting, driving scene relighting, and transla-tion demonstrate improved structural preservation, lighting faithfulness,and image quality, with our framework extending naturally to video viaframe-by-frame application with good temporal stability. Project Web-page: https://shenzheng2000.github.io/WarpI2I.github.io/

Visual object tracking relies on modeling cross-frame dynam-ics, and recent approaches employ learnable temporal tokens to encodehistorical information. However, our analysis reveals that these tokensare often marginalized by dominant spatial features during joint opti-mization. This imbalance leads to shortcut learning where the networkover-relies on current-frame appearance, causing temporal representationcollapse. To address this, we propose DASTrack, a framework featur-ing Decoupled Auxiliary Supervision (DAS). By masking the templatesduring an auxiliary forward pass, DAS compels the network to localizetargets using only temporal tokens and thus encode robust cross-framepriors. This mechanism is applied strictly during training and discardedat inference to preserve the original architecture and computational ef-ficiency. DASTrack yields consistent and interpretable average perfor-mance gains across multiple backbones. Our findings demonstrate thatthe primary bottleneck in temporal tracking stems from biased learn-ing dynamics rather than architectural design, offering a novel trainingparadigm for eliciting latent temporal capabilities. The code and modelswill be released at: https://github.com/ZhangDailing8/DASTrack.

In-context learning (ICL) enables large vision-language models (LVLMs) to perform tasks by following patterns in context examples, yet its potential to amplify societal biases remains underexplored. We systematically investigate how ICL influences gender bias in LVLMs through VL-BICLE, an evaluation framework comprising six ICL settings, three tasks, and four datasets. Our experiments on six LVLMs reveal that gendered ICL demonstrations act as a directional force, shifting model bias toward the demonstrated gender through a cross-gender mechanism that disproportionately degrades performance on the opposite gender. This effect appears in image captioning and pronoun prediction but not in visual question answering, indicating that gendered ICL influences bias only when the task output involves gendered language. Similarity-based retrieval methods inherit the training pool’s gender imbalance and offer no debiasing advantage, while standard quality metrics remain blind to these bias shifts. To mitigate this bias, we replace real in-context images with synthetic ones from stable diffusion models while keeping captions unchanged. This simple intervention reduces gender bias without degrading caption quality. The code can be found at: https://github.com/mathfather/Gender-Bias-in-VL-ICL.

We present DRS-VPT, a feed-forward transformer archi-tecture for foundational image-to-scan registration. Given query imagesand a reference 3D point cloud, the model predicts the scan pose andpoint map alongside the poses and point maps of each camera, all ex-pressed in the first camera’s frame. It additionally predicts a coarse-to-fine pyramid of per-point and per-pixel features for direct reprojectivealignment of the scan to the first image. This formulation unifies down-stream tasks such as camera–LiDAR calibration in autonomous driv-ing and indoor camera-to-map relocalization. A single DRS-VPT modelachieves state-of-the-art performance for image-to-LiDAR registration inautonomous driving, competitive indoor relocalization without trainingmap-specific weights, and strong zero-shot transfer to unseen environ-ments. We also show qualitatively that the model learns complex scan-to-image projection properties such as occlusion of back-facing points.

Metric 3D object detection is a core capability for embodied agents, yet most reliable systems lean on depth sensors, trading away cost, power, and integration simplicity. This motivates monocular 3D detection, which avoids additional constraints, yet it faces a major obstacle: from a single image, depth, and especially absolute scale, are underconstrained. As a result, the prevailing pattern of detecting in 2D and then predicting 3D attributes is often brittle, since modest range errors can dominate 3D localization, and the learned scale prior can fail when cameras, motion, or environments undergo domain shifts. To address this, we propose Map-Det3D, an online multi-view 3D object detection model that brings detection directly into a 3D space reconstructed from RGB. We map a short temporal window into multiple views and repurpose a feed-forward metric 3D reconstruction model as our geometric backbone while tuning its object-aware capabilities. Building on this representation, Map-Det3D directly predicts boxes in metric 3D space, without the widely used 2Dto-3D lifting. Experiments across different benchmarks show that this design supports strong online performance and robust transfer without adaptation, suggesting that training reconstruction priors for detection is a practical route to stable metric 3D detection from monocular video. Code and models are available at royyang0714.github.io/Map-Det3D.

We propose a feed-forward method for dense Signed DistanceField (SDF) regression from unstructured image collections in less thanthree seconds, without camera calibration or post-hoc fusion. Our keyinsight is that the intermediate feature space of pretrained multi-viewfeed-forward geometry transformers (FFGT) already encodes a powerfuljoint world representation; yet, existing pipelines discard it, routing fea-tures through per-view prediction heads before assembling 3D geometrypost-hoc, which discards valuable completeness information and accu-mulates inaccuracies. We instead perform 3D extraction directly fromFFGT features via learned volumetric extraction: voxelized canonicalembeddings that progressively absorb multi-view geometry informationthrough interleaved cross- and self-attention into a structured volumet-ric latent grid. A simple convolutional decoder then maps this grid to adense SDF. We additionally propose a scalable, validity-aware supervi-sion scheme directly using SDFs derived from depth maps or 3D assets,tackling practical issues like non-watertight meshes. Our approach yieldscomplete and well-defined distance values across sparse- and dense-viewsettings and demonstrates geometrically plausible completions.

Monocular depth estimation foundation models, such as the Depth Anything series, have achieved remarkable performance across diverse domains. However, they still suffer from critical failures under adverse weather conditions, such as fog, rain, snow, or at night. To address this, we present Weather-Conditioned Depth Anything (DA-W), a framework that explicitly disentangles style from content for weatherrobust depth estimation. Specifically, we introduce a Style Filter trained on a curated mix of real and synthetic degradation datasets to extract content-independent, degradation-aware weather embeddings. This style embedding is then injected into the Depth Anything backbone using a parameter-efficient, zero-initialized adapter. Such a lightweight modulation allows a single unified model to robustly adapt to diverse conditions—including fog, rain, snow, and low-light—while avoiding catastrophic forgetting of its core generalization abilities in normal conditions. We train the adapter using a pseudo-label distillation and alignment strategy. Our comprehensive experiments demonstrate that our proposed DA-W achieves state-of-the-art robust depth estimation, improving AbsRel by an average of 3.7% on our curated weather benchmarks, while matching or slightly outperforming performance on standard clean benchmarks.

Safety alignment of text-to-image (T2I) diffusion models aimsto suppress harmful generations while preserving utility on benign prompts.Recent methods often appear to deliver high safety with high utility, butthis conclusion rests largely on coarse global utility metrics (e.g., FID,CLIPScore) that are insensitive to fine-grained semantic correctness, cre-ating an illusion of high utility. We show that when utility is measuredwith structured evaluation, this illusion breaks: on TIFA (Text-to-ImageFaithfulness evaluation with Question Answering), safety-aligned modelssuffer substantial drops in semantic fidelity, including failures in objectcounts, attributes, and relationships. To diagnose the source of this gap,we analyze the text-encoder prompt embedding space and uncover se-mantic collapse, a contraction of embedding spread coupled with distor-tion of inter-prompt similarity structure, which strongly correlates withstructured utility loss. Guided by this insight, we propose Structure-Aware Geometric Regularization (SAGE)1 , a safety alignment objec-tive that explicitly preserves embedding spread and inter-prompt rela-tional structure during adaptation. Our method restores structured util-ity (TIFA +5.0% over prior state-of-the-art) while maintaining strongsafety performance and competitive coarse-grained utility scores.

Deep research agents integrate fragmented evidence throughmulti-step tool use. BrowseComp offers a text-only testbed for such agents,but existing multimodal benchmarks rarely require both weak visual cuescomposition and BrowseComp-style multi-hop verification. Geolocationis a natural testbed because answers depend on combining multipleambiguous visual cues and validating them with open-web evidence.Thus, we introduce GeoBrowse, a geolocation benchmark that combinesvisual reasoning with knowledge-intensive multi-hop queries. Level 1 testsextracting and composing fragmented visual cues, and Level 2 increasesquery difficulty by injecting long-tail knowledge and obfuscating keyentities. To support evaluation, we provide an agentic workflow GATEwith five think-with-image tools and four knowledge-intensive tools, andrelease expert-annotated stepwise traces grounded in verifiable evidencefor trajectory-level analysis. Experiments show that GATE outperformsdirect inference and open-source agents, indicating that no-tool, search-only or image-only setups are insufficient. Gains come from coherent,level-specific tool-use plans rather than more tool calls, as they morereliably reach annotated key evidence steps and make fewer errors whenintegrating into the final decision. The GeoBrowse benchmark and codesare provided in https://github.com/ornamentt/GeoBrowse.

In this paper, we approach the machine unlearning (MU)problem from a novel perspective: Can we design a learning strategythat simplifies the later MU problem, i.e., leads to unlearning-compatiblemodels? Motivated by recent findings that the cost to reverse SGD (un-learning error) is proportional to the accumulated loss curvature and thatthe generalization error (a measure of acquired subject-specific knowl-edge) is bounded by loss sharpness, we focus on the loss sharpness as thekey factor to train unlearning-compatible models. By studying the theo-retical implications of defining the sharpness over different perturbationnorms, we propose a variation of the sharpness, quantified by the l1 normof the gradient, which minimizing it leads to a flatter learning trajectoryand, as a result, smaller unlearning and generalization errors. Consider-ing these results, to have unlearning-compatible models, we propose toregularize the training loss with l1 norm of the gradient, which encour-ages gradient sparsity during training. Our experiments show that onlyby using our regularizer in the training stage, the MU performance ofvarious baselines is considerably improved, suggesting the effectivenessof our approach in simplifying the MU problem by training unlearning-compatible models. Moreover, we demonstrate that the benefits of ourapproach become even more significant when dealing with poisoned datain backdoor attack scenarios. Our investigations show that in such ascenario, gradient sparsity not only simplifies model cleaning but alsodamps the gradient spikes, which are typically caused by poisonous dataand encourage the model to automatically learn less from such poisonousdata points.

Current CT report generation frameworks predominantlyrely on global feature representations, often failing to capture region-specific details and potentially missing certain abnormalities. To over-come this limitation, we propose MedRegion-CT, a region-focused mul-timodal large language model framework featuring three key innova-tions. First, we revisit the SlowFast strategy to jointly model globaland fine-grained information and adapt it to the medical domain via aRegion-based SlowFast Tokenizer that extracts tokens guided by clin-ically meaningful regions. Second, generated pseudo-masks guide themodel to attend to diagnostically important anatomical regions, facilitat-ing a systematic understanding of the overall scan context. Third, quanti-tative lesion information, including size, diameter, and spatial location,is encoded as structured textual prompts, enabling context-aware andclinically informed report generation. To enable rigorous evaluation, wevalidate our framework on multi-institutional structured report genera-tion benchmarks. Experimental results demonstrate that MedRegion-CTachieves state-of-the-art performance, outperforming existing approachesin both linguistic quality and clinical accuracy. All code is publicly avail-able at: https://github.com/babbu3682/MedRegion-CT.

Graphical user interface (GUI) grounding is a key capa-bility for computer-use agents, mapping natural-language instructionsto actionable regions on the screen. Existing Multimodal Large Lan-guage Model (MLLM) approaches typically formulate GUI groundingas a text-based coordinate generation task. However, directly gener-ating precise coordinates from visual inputs is challenging and oftendata-intensive. A more intuitive strategy is to first identify instruction-relevant visual patches and then determine the exact click location withinthem. Motivated by recent observations that general MLLMs exhibitnative grounding ability embedded in their attention maps, we pro-pose GUI-AIMA, an attention-based and coordinate-free supervised fine-tuning framework for efficient GUI grounding. GUI-AIMA aligns the in-trinsic multimodal attention of MLLMs with patch-wise grounding sig-nals. These signals are calculated adaptively for diverse user instructionsby multi-head aggregation on simplified query-visual attention matri-ces. Besides, its coordinate-free manner can easily integrate a plug-and-play zoom-in stage. GUI-AIMA-3B was trained with only 509k sam-ples (∼101k screenshots), demonstrating exceptional data efficiency andverifying that light training can trigger the native grounding capabil-ity of MLLMs. It achieves state-of-the-art performance among 3B mod-els, attaining an average accuracy of 61.5% on ScreenSpot-Pro, 92.1%on ScreenSpot-v2, 68.1% on OSWorld-G, 79.1% on MMBench-GUI-L2and 60.0% on UI-Vision. Project page: https://github.com/sjz5202/GUI-AIMA.

Snapshot hyperspectral imaging avoids sequential scanning,but systems that jointly achieve stable reconstruction, low cost, andcompact optics remain limited. We present a snapshot hyperspectralimaging system based on angular-to-spectral diversity conversion. A ta-pered kaleidoscope creates replicated views with distinct incidence di-rections, and a directly attached birefringent filter converts them intoview-channel-dependent spectral transmittances, yielding complemen-tary measurements that better condition the inverse problem for morestable single-shot spectral reconstruction. The system preserves a sim-ple pixel-wise linear model for fast non-learning-based reconstruction anduses only off-the-shelf components without relay optics or cascaded mod-ules. We select the birefringent filter configuration using a condition-number-based criterion and validate the system on both synthetic andreal data.

Automated fine-grained perception of calligraphy styles—a task vital to cultural heritage preservation—remains a critical challenge for Large Vision-Language Models (LVLMs), largely constrained by existing datasets that suffer from modal mixture and flattened labels. To bridge this gap, we introduce HCSU, the first comprehensive dataset tailored for fine-grained Historical Calligraphy Style Understanding. HCSU comprises 39,307 meticulously curated character images from 49 historically prominent calligraphers across 10 dynasties, systematically decoupling authentic ink manuscripts (Tie) from stone rubbings (Bei) to resolve the long-standing modal mixture problem. Moving beyond conventional flattened labels, HCSU provides hierarchical expert-written aesthetic descriptions, enabling two rigorous evaluation protocols: finegrained style discrimination and interpretable aesthetic reasoning. Extensive evaluations reveal a persistent gap between calligraphy-related knowledge and visually grounded style perception: state-of-the-art LVLMs show non-trivial performance but remain sensitive to script-level, textual, and source-specific cues, and often struggle to ground aesthetic judgments in fine-grained brushwork evidence. Ultimately, the HCSU benchmark exposes fundamental limitations in current multimodal architectures, aiming to inspire the evolution of expert-level visual reasoning for cultural heritage preservation. The dataset is available at https://huggingface.co/datasets/Tongji209/HCSU.