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ECCV 2026

Tackling Misattribution in 3D Intrinsic Decomposition via Proximity Attention Point Rendering

Alireza Moazeni, Shichong Peng, Yanshu Zhang, Chirag Vashist, Ke Li

Recent point-based intrinsic decomposition and inverse ren-dering methods have advanced the modelling of the shading and albedoof 3D scenes. However, we identify a fundamental limitation: these meth-ods suffer from a misattribution issue, where individual primitives learnincorrect appearance features despite producing correct aggregated ren-derings. We show that the root cause lies in volume rendering, whichaggregates translucent primitives along each ray and only supervises thefinal colour, preventing direct supervision of individual primitive fea-tures. To address this, we propose Intrinsic PAPR, a robust intrinsic de-composition framework which leverages Proximity Attention Point Ren-dering (PAPR) to enable direct per-point supervision. Unlike volumerendering approaches, PAPR eliminates translucent primitives and di-rectly predicts appearance at ray-surface intersections, enabling accuratesupervision to the feature of each individual point. Our method incor-porates a 2D albedo prior adapted with conditional Implicit MaximumLikelihood Estimation (cIMLE) to handle monocular ambiguities, andemploys a space carving loss to ensure multi-view consistency. Exten-sive evaluations on synthetic and real-world datasets demonstrate thatIntrinsic PAPR outperforms point-based inverse rendering, NeRF-basedintrinsic decomposition, and diffusion-based PBR methods in novel viewsynthesis and albedo estimation while resolving the misattribution issue.

ECCV 2026

Anti-Prompt: Image Protection against Text-Guided Image-to-Video Generation

Yeonghwan Song, Chanhui Lee, Jinsoo Park, Jeany Son

Recent advances in Image-to-Video (I2V) generation allow asingle image to be animated into a convincing video under text guidance,raising serious copyright and privacy risks. We propose Anti-Prompt,an image protection approach that injects imperceptible perturbationsinto an image, inducing visible inconsistencies and structural failures intext-guided I2V generation. Our method is motivated by a simple em-pirical observation: when text guidance is removed from modern I2Vmodels, generation quality degrades markedly, not only in motion re-alism but also in subject preservation, structural coherence, and tem-poral consistency. Building on this insight, Anti-Prompt exploits themodel’s reliance on textual guidance by attenuating text-conditioned in-teractions during denoising while strengthening visual-only pathways.To further systematically evaluate protection effectiveness, we introducea Video-LLM–assisted evaluation protocol that provides interpretable,frame-grounded analyses of generation artifacts and inconsistencies. Ex-periments on two representative I2V architectures demonstrate that ourmethod achieves strong protection performance while improving effi-ciency and cross-model transferability.

ECCV 2026

Knowledge-Centric Agents for Workflow Generation in ComfyUI

Zhendong Li, Lei Sun, Ruibo Ming, He Zhang, Danda Paudel, Luc Van Gool, Jinjin Gu

Workflow generation in visual creation systems such as Com-fyUI demands not only syntactic accuracy but also expert-level reason-ing over modular compositions. Existing large language model (LLM)approaches often treat this as a direct text-to-JSON generation task,struggling with structural brittleness and lacking the experiential knowl-edge required for effective design. We argue that successful workflowgeneration requires modeling knowledge itself, including its structure,hierarchy, and reasoning dynamics. To this end, we propose a knowledge-centric framework that learns to invert, inject, and infer with knowl-edge across multiple abstraction levels. We first perform knowledge in-version to distill hierarchical representations, ranging from full pseudo-codes and skeletons to high-level strategies, from large collections of real-world workflows. We then conduct knowledge injection through super-vised fine-tuning, teaching the model to reason from task descriptionsto strategies and from strategies to executable structures. During infer-ence, the model performs reversible reasoning to synthesize executableworkflows, augmented by self-refinement for structural coherence. Ex-tensive experiments demonstrate that our method produces workflowswith richer node diversity, more coherent structures, and higher execu-tion success rates than existing systems, establishing a new foundationfor knowledge-driven, agentic workflow generation.

ECCV 2026

SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models

Olaf Dünkel, Basavaraj Sunagad, Haoran Wang, David Hoffmann, Christian Theobalt, Adam Kortylewski

Measuring structured object understanding in vision founda-tion models remains challenging due to inconsistent evaluation protocolsand limited part-level supervision. Semantic correspondence (SC) eval-uates this capability by testing whether object parts can be matchedacross instances and categories under large variations in appearance,viewpoint, and geometry. To enable a systematic SC evaluation, we in-troduce SOCO, a new benchmark for Semantic Object Correspondencethat introduces a taxonomy of correspondence types and provides con-sistent, functionally meaningful keypoint annotations across 100 cate-gories and over 1M correspondence pairs. In addition, SOCO includeskeypoint language descriptions, enabling the evaluation of large vision-language models (LVLMs) and their fine-grained part-level understand-ing. Comprehensive experiments reveal that (i) vision foundation back-bones encode strong semantic structure but transfer correspondencespoorly across related categories and only partially capture object-partposition, (ii) LVLMs are stronger at text-prompted part localizationthan at visual-reference cross-image matching, exposing a gap betweenlanguage-grounded localization and fine-grained visual correspondence,and (iii) correspondence performance predicts dense downstream tasks—segmentation, tracking, 3D pose estimation, and 3D detection—morestrongly than ImageNet classification. Together, these findings positionSOCO as a benchmark for structured, part-level representation qualityin vision and multimodal foundation models.

ECCV 2026

Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis

Yucheng Xing, Ling Huang, Pei Liu, Jingying Ma, Jiaxing Xu, Kai He, Mengling Feng

Whole-slide images (WSIs) are widely used for computa-tional cancer prognosis. However, current research primarily focuses onin-domain performance and fails to generalize across clinical centers. Thislimitation stems from their reliance on pixel-derived representations thatare highly susceptible to domain-specific artifacts caused by stainingprotocols and scanner hardware. We hypothesize that high-level pathol-ogy semantics, such as tumor grade and micro-environmental architec-ture, provide a domain-invariant semantic representation that mirrorsthe robust diagnostic logic of human pathologists. Therefore, we proposea Semantic-Anchored Evidential Fusion Survival (SAEFS) framework,where SAEFS derives semantic anchors from WSIs via Visual QuestionAnswering (VQA), employs a dual-stream WSI evidence extraction archi-tecture, uses Dirichlet-based Subjective Logic to model uncertainty, andfuses semantic and visual evidence through a cautious conjunction rule toavoid overconfident fusion from correlated sources. Trained exclusively onone source domain and evaluated zero-shot across four unseen domains,SAEFS consistently outperforms state-of-the-art models both in predic-tion accuracy and reliability, improving the average C-index by 10.2%.Quantitative analyses further show that VQA-derived semantic featuresexhibit significantly lower cross-center divergence than pixel-derived fea-tures, highlighting their robustness for cross-center clinical applications.The code is available at https://github.com/YuchengXing99/SAEFS.

ECCV 2026

GeoSolver: Scaling Test-Time Reasoning in Remote Sensing with Fine-Grained Process Supervision

Sun Lang, Ronghao Fu, Zhuoran Duan, Haoran Liu, Xueyan Liu, Bo Yang

While Vision-Language Models (VLMs) have significantly advanced remote sensing interpretation, enabling them to perform complex, step-by-step reasoning remains highly challenging. Recent efforts to introduce Chain-of-Thought (CoT) reasoning to this domain have shown promise, yet ensuring the visual faithfulness of these intermediate steps remains a critical bottleneck. To address this, we introduce GeoSolver, a novel framework that transitions remote sensing reasoning toward verifiable, process-supervised reinforcement learning. We first construct GeoPRM-2M, a large-scale, token-level process supervision dataset synthesized via entropy-guided Monte Carlo Tree Search (MCTS) and targeted visual hallucination injection. Building upon this dataset, we train GeoPRM, a token-level process reward model (PRM) that provides granular faithfulness feedback. To effectively leverage these verification signals, we propose Process-Aware Tree-GRPO, a reinforcement learning algorithm that integrates tree-structured exploration with a faithfulness-weighted reward mechanism to precisely assign credit to intermediate steps. Extensive experiments demonstrate that our resulting model, GeoSolver- 9B, achieves state-of-the-art performance across diverse remote sensing benchmarks. Crucially, GeoPRM unlocks robust Test-Time Scaling (TTS). Serving as a universal geospatial verifier, it seamlessly scales the performance of GeoSolver-9B and directly enhances general-purpose VLMs, highlighting its remarkable cross-model generalization. The code will be available at https://github.com/minglangL/GeoSolver.

ECCV 2026

Kiroshi: An Agentic Perception System for High-Accuracy Image Parsing

Haipeng Zhou, Jinshan Liu, He Zhang, Xuequan Lu, Jun Ma, Lei Zhu

Parsing images with high precision for matting and segmentation is sig-nificantly more challenging than conventional dense prediction, as it requires accurateestimation of fine-grained details. Existing methods either lack semantic awareness orproduce suboptimal predictions; even interactive matting approaches rely on manualverification and repetitive checking, making fully automatic matting still unattainable.In this work, we propose Kiroshi , an agentic perception system for high-accuracyimage parsing. We train an Action Model with iterative refinement and mine pairedtrajectories by sampling grid prompts from residual maps, where each step yields apositive and a negative transition under the same intermediate prediction based onquantitative quality gains. These within-context preference pairs form a reliable su-pervision signal for post-training to align the MLLM policy toward more effectivegrid decisions. We also contribute a new High-Fidelity Referring Matting and Seg-mentation (HiFiRefMS ) benchmark to evaluate the performance of different mod-els. Experimental results demonstrate that our method surpasses state-of-the-art ap-proaches both quantitatively and qualitatively, and extensive ablation studies furthervalidate the effectiveness and superiority of our agentic design. Project will be releasedat https://github.com/haipengzhou856/Kiroshi.

ECCV 2026

Flow Straight to Reality: Perceptually Consistent Flow Matching for Efficient Image Restoration

Sangwoo (Jason) Jo, Donggeun Ko, Jayeon Kang, Youngsang Kwak, Jaehwa Kwak, Sungjoon Choi

Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations. Recent approaches attempt to balance this tradeoff via posterior sampling or multi-stage generative pipelines, yet remain computationally expensive and architecturally complex. To overcome these limitations, we propose PCFlow (Perceptually Consistent Flow Matching), a unified framework that directly parameterizes a continuous transport from degraded observations to clean targets, jointly optimizing distortion and perceptual quality. While its latent consistency flow objective drives stable and efficient few-step inference, a Latent Consistency Perceptual Loss (LCPL) imposes semantic constraints directly on the guiding velocity field, steering the dynamics toward visually sharp data manifolds. Furthermore, recognizing the inherent conflict between structural and perceptual consistencies, we integrate a conflict-free gradient projection strategy to stabilize the multi-objective optimization landscape. Combined with lightweight, convolution-only backbone, PCFlow achieves competitive performance across diverse restoration tasks at a fraction of traditional computational costs.

ECCV 2026

Purify then Guide: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing

Yingjie Ma, Xun Lin, Zitong Yu, Haonan Wang, Ruixin Zhang, Shouhong Ding, Xin Liu, Xiaochen Yuan, Weicheng Xie, Linlin Shen

Face anti-spoofing (FAS) is critical for secure deploymentof face recognition in high-stakes applications. However, existing multi-modal FAS methods still generalize poorly to unseen domains. We arguethat a key reason is that they directly align noisy multimodal featuresin which spoof-relevant cues are entangled with modality- and domain-specific variations, so both useful and nuisance patterns are forced tomatch across domains. Motivated by this, we design Multimodal Denoisingand Alignment (MMDA), a CLIP-based framework that (i) purifiesmultimodal features, (ii) softly guides them into a semantic space, and(iii) preserves the pre-trained geometry during task adaptation. To de-couple spoof cues from these variations before alignment, the Modality-Domain Joint Differential Attention (MD2A) module contrasts same-domain cross-sample features to down-weight patterns shared withineach domain/modality, which helps reduce domain- and modality-relatedbiases while better preserving discriminative spoof information. To avoiddestructive, over-rigid alignment, the Representation Space Soft (RS2)strategy aligns visual features to real/spoof subspaces spanned by mul-tiple prompts in the CLIP space, providing a soft semantic pull insteadof collapsing features onto a single text embedding. To prevent task-specific fine-tuning from destroying CLIP’s generalizable structure, theU-shaped Dual Space Adaptation (U-DSA) module introduces deepyet parameter-efficient adaptation and remaps adapted features backto shallower, more domain-invariant layers. Extensive experiments onWMCA, CeFA, PADISI, and SURF under complete-modality, missing-modality, and limited-source protocols show that MMDA consistentlysurpasses state-of-the-art methods, reducing average HTER by up to9.63% and improving AUC by up to 5.98% in cross-domain evaluation.

ECCV 2026

Do Vision Language Models Recognize Visual Ambiguity?

Huy Ta, Trang Nguyen, Townim Chowdhury, Ankit Yadav, Minh-Son To, Zhibin Liao, Johan Verjans, Hieu Phan

Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropybased methods, such as Semantic Entropy (SE), rely on output diversity. Yet our analysis shows that overconfident visual embeddings suppress output diversity under stochastic decoding, causing SE to underestimate uncertainty in such cases. Recent methods instead probe output diversity through input perturbations, including textual paraphrasing or joint text-image perturbations, and show improved performance. We study these approaches and reveals that the resulting variability is often dominated by textual changes rather than visual evidence, causing uncertainty estimates to reflect prompt sensitivity rather than visual ambiguity. We therefore propose Visual Semantic Entropy (VSE), which perturbs only the image to probe nearby visual variations while keeping the text query fixed. VSE measures uncertainty by clustering generated answers into semantic prototypes and computing the massweighted dispersion among them. Extensive evaluation across five modern vision-language models and five diverse VQA benchmarks demonstrates that VSE effectively captures visual ambiguity, establishing a new state-of-the-art for VLM uncertainty estimation. Code is available: https://github.com/tadeephuy/visual-semantic-entropy

ECCV 2026

Conversational Human Audio-visual Talking Dialogue Generation

Junhao Song, Lluis Guasch, Xilin He, Zhongyu Yang, Yingfang Yuan, Weicheng Xie, Linlin Shen, Lin Haijun, Shizhe Liu, Wei Pang, Siyang Song

Large-scale dyadic interactive audio-visual dialogue (DIAD)datasets provide fundamental data resources for developing humanoidinteractive virtual agents and digital humans. However, collecting suchdata is time-consuming, expensive, and ethically sensitive. To addressthis, we propose CHAT, a new dyadic interactive audio-visual dialoguegeneration (DIADG) framework that generates diverse, paired, and mu-tually responsive speech-face dialogue clips from a single textual prompt.CHAT unifies large language models and talking face models with inter-active audio and facial behaviour refinement modules, enabling the gen-eration of aligned dyadic dialogue clips with diverse contents and facialidentities. Experiments show that CHAT outperforms existing relatedmethods designed for similar tasks under both objective and subjectiveevaluations. Moreover, our synthesised CHAT-AVD-50k dataset servesas effective pre-training data for downstream interactive head genera-tion, consistently improving PerFRDiff and ReactDiff on REACT 2024.CHAT offers a scalable alternative to the costly and ethically sensitivecollection of real dyadic interaction data.

ECCV 2026

Maximum Spanning Tree Guided Confidence and Sparse Graph for Robust Noisy Label Learning

Gengfeng Chen, Xu Liu, Boyi Peng, Liangqiu Xiao, Weicheng Xie, Siyang Song, Zitong Yu, Laizhong Cui, Linlin Shen

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.

ECCV 2026

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels

Venkanna Babu Guthula, Oswin Krause, Dimitri Gominski*, Hui Zhang, Johan Mottelson, Ankit Kariryaa, Nico Lang, Christian Igel

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.

ECCV 2026

OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection

David Schneider, Zdravko Marinov, Moritz Mistol, Zeyun Zhong, Alexander Jaus, Rodi Düger, Rafael Baur, M. Saquib Sarfraz, Rainer Stiefelhagen

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

ECCV 2026

SceneDiff: A Benchmark and Method for Multiview Object Change Detection

Yuqun Wu, Chih-Hao Lin, Henry Che, Aditi Tiwari, Chuhang Zou, Shenlong Wang, Derek Hoiem

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

ECCV 2026

Scal3R: Learning Efficient Multi-Relative Pose Query for Scalable Online 3D Reconstruction

Chin-Yang Lin, Yang-Che Sun, Cheng Sun, Fu-En Yang, Min-Hung Chen, Yen-Yu Lin, Wei-Chen Chiu, Yu-Lun Liu

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/

ECCV 2026

Image Warping for Image-to-Image Translation

Shen Zheng, Anurag Ghosh, Gaurav Parmar, Srinivasa G. Narasimhan

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/

ECCV 2026

Rethinking Temporal Modeling in Visual Object Tracking via Decoupled Auxiliary Supervision

Dailing Zhang, Shiyu Hu, Honghao Fu, Xiaokun Feng, Yipei Wang, Kang Hao Cheong, Kaiqi Huang

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.

ECCV 2026

Gender Bias in Vision-Language In-Context Learning

Tong Xiang, Yuta Nakashima, Noa Garcia

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.

ECCV 2026

DRS-VPT: Directly Re-localizing in Scenes using a Vision and Point Transformer

Lanke Fu, Maurice Fallon

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.

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