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ECCV 2026

Learning to Deny: Action Denial in Multimodal Large Language Models

Raiyaan Abdullah, Shehreen Azad, Yogesh Rawat

Multimodal large language models (MLLMs) have rapidlyadvanced video understanding, achieving strong zero-shot and few-shotrecognition across standard benchmarks. Yet their ability to deny anaction by recognizing when an activity is not happening despite strongcontextual cues remains largely unexplored. We introduce UCF101-AD, a large-scale benchmark consisting of paired Action-Presence andAction-Denial clips, designed to evaluate this capacity for denial. Eachnegative video in UCF101-AD preserves the same contextual and mo-tion cues (persons, objects, locations) as its positive counterpart, butthe defining action itself is explicitly absent. Evaluating 20 state-of-the-art MLLMs reveals a consistent failure: models that exceed 85% accu-racy on the positive action classes collapse below 50% on its action-denial counterpart, indicating a strong inclination to affirm plausibleactions rather than verify that they truly occur. This exposes a criti-cal blind spot in modern video understanding: the inability to reasoncausally about whether a motion actually happens. To probe this is-sue, we explore a causal graph formulation, CausalAct, which expressesscene structure through natural-language prompts linking context, in-teraction, and motion. Incorporating such causal cues substantially re-duces false positives, demonstrating that denial is a learnable reason-ing skill. UCF101-AD provides a new lens for diagnosing and improv-ing causal reasoning in multimodal models. Dataset and relevant code:https://github.com/raiyaan-abdullah/Learn-to-Deny.

ECCV 2026

Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation

Yeonkyeong Lee, Hyunsung Go, Jongmin Kim, Sewoong Lim, Donghoon Lee

Latent diffusion models have emerged as a dominant frame-work for high-fidelity image and video synthesis, operating in compactlatent spaces with variational autoencoders (VAEs) to enhance compu-tational efficiency without compromising visual quality. However, con-ventional VAEs are suboptimal for video data as they employ fixed com-pression ratios that cannot adapt to the varying complexity of spatio-temporal content. We present KATok (Keep-or-Drop? Adaptive Tok-enizer for Compact Video Representation), a transformer-based VAEthat incorporates an adaptive token selector which is jointly learned withlatent tokens. By evaluating each token’s content-richness as keep-or-dropprobability, the token selector effectively discards uninformative tokens,naturally allowing data-dependent compression. Applying adaptive tok-enization to diffusion models may cause spatial misalignment, as tokendropping can disturb the original spatio-temporal structure. To allevi-ate this issue, we propose two position-prediction strategies: cascadedand joint generation, to ensure spatial consistency. We empirically showthat our model achieves strong reconstruction and generation quality at astate-of-the-art compression ratio. Further analysis on video data revealsthat this improvement is primarily achieved by reducing spatio-temporalredundancy and removing uninformative tokens, as supported by bothquantitative and qualitative results.

ECCV 2026

Hi-DiT: Hybrid Latent-Pixel Diffusion Transformer for Image Generation

Yedong Shen, Yehao Li, Yingwei Pan, Yanyong Zhang, Ting Yao

Current diffusion models face a fundamental tension betweenthe computational efficiency of latent-space generation and the fine-detailfidelity of pixel-space generation. Latent Diffusion Models (LDMs) relyon VAE compression, which can discard high-frequency visual cues andlimit pixel-level quality, while pixel-space diffusion often suffers from diffi-cult optimization when global structure and high-frequency details mustbe learned jointly. In this paper, we propose the Hybrid Latent-PixelDiffusion Transformer (Hi-DiT), a unified Diffusion Transformer thatbridges these two regimes within a single architecture. Motivated by thetemporal heterogeneity of denoising—early timesteps primarily establishcoarse global structure, whereas late timesteps increasingly emphasizehigh-frequency detail—we introduce a dual-stream design with temporalspecialization. Hi-DiT performs semantic planning in a compact latentpathway at early stages, and activates a pixel pathway at later stagesto synthesize high-frequency details using time-consistent noisy pixelembeddings. A Time-Gated Injection mechanism schedules the partic-ipation of pixel tokens only in the low-noise regime, and a lightweightsub-pixel prediction head enables efficient dense detail generation. Ex-tensive experiments demonstrate that Hi-DiT achieves state-of-the-artperformance, obtaining an FID of 1.06 on ImageNet 256 × 256, 1.26 onImageNet 512 × 512, and 5.15 on MS-COCO text-to-image generation.Code is available at: https://github.com/HiDream-ai/Hi-DiT.

ECCV 2026

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM

Amol Harsh, Zongyan Han, Jean Lahoud, Ye Liu, Rao M Anwer, Hisham Cholakkal, Salman Khan, Fahad Shahbaz Khan

Natural-language queries about 3D environments become actionable when responses are verifiable and metric. Verifiability requires explicit grounding to the referred 3D region, while metric answers report physical measurements in real-world units (e.g., size, thickness, clearance, and distance). Existing 3D large multimodal models (LMMs) approaches remain limited: conversational systems typically respond without explicit 3D grounding, while 3D grounding models are not designed for interactive, metric-aware dialogue. In this paper, we present Ground3DLMM, a unified model that takes a point cloud and an optional RGB image as input and supports 3D spatial conversation with (i) pointgrounded responses and (ii) metric numeric outputs at both object and part granularity, including multi-object queries. To evaluate this intersection of grounding and measurement, we define the 3D Grounded Measurement task, which requires predicting the referred 3D region and the corresponding metric quantities in real-world units. We introduce a large-scale dataset built on ScanNet and ScanNet++ datasets with dense object and part annotations and roughly 2.5M question-answer pairs spanning eight tasks, along with a manually verified test set. Extensive experiments on multiple datasets and tasks show that our proposed Ground3D-LMM model provides a strong baseline for grounded, metric-aware 3D conversational understanding. Our dataset and model are publicly available.

ECCV 2026

Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video Editing

Sen Liang, Cong Wang, Zhentao Yu, Fengbin Guan, Zhengguang Zhou, Teng Hu, Youliang Zhang, Yuan Zhou, Xin Li, Qinglin Lu, Zhibo Chen

Existing instruction-based video editing datasets commonlyfocus on single-task appearance editing, failing to meet the complex cre-ative demands of real-world scenarios. To bridge this gap, we presentGoku, a large-scale dataset featuring 2 million high-quality, instruction-aligned video editing pairs, which is the first to extend task boundariesfrom basic appearance editing to multi-task and structural manipulations(e.g., precise control of subject movement). To tackle the data synthesischallenges inherent in these complex tasks, we design an efficient datasynthesis pipeline that decomposes complex edits into controllable sub-problems and introduce a progressive filtering system for data reliabil-ity throughout the whole process. Furthermore, we explore the optimalnetwork structures on Goku, and propose Goku-Edit. To deeply com-prehend complex editing instructions, Goku-Edit leverages an MLLMas its text encoder and adopts a decoupled dual-branch design: a dedi-cated mask branch handles structural control, freeing the main branchfor appearance rendering. A comprehensive video editing benchmark,Goku-Bench, is also proposed with 1,000 human-verified test cases and7 novel editing-specific metrics. Evaluated on Goku-Bench, Goku-Editobtains up to +8% improvement on other open-source models in termsof instruction following.

ECCV 2026

GimbalDiffusion: Gravity-Aware Camera Control for Video Generation

Frédéric Fortier-Chouinard, Yannick Hold-Geoffroy, Valentin Deschaintre, Matheus Gadelha, Jean-Francois Lalonde

Recent progress in text-to-video generation has achieved remarkable realism, yet fine-grained control over camera motion and orientation remains elusive, especially with extreme trajectories (e.g., a 180° turnaround, or looking directly up or down). Existing approaches typically encode camera trajectories using relative or ambiguous representations, limiting precise geometric control and offering limited support for large rotations. We introduce GimbalDiffusion, a framework that enables camera control grounded in physical-world coordinates, using gravity as a global reference. Instead of describing motion relative to previous frames, our method defines camera trajectories in an absolute coordinate system, allowing accurate, interpretable control over camera parameters. Using panoramic 360◦ videos for training, we cover the full sphere of possible viewpoints, including combinations of extreme pitch and roll that are out-of-distribution of conventional video data. To improve camera control, we introduce null-pitch conditioning, a strategy that prevents the model from overriding camera specifications in the presence of conflicting prompt content (e.g., generating grass while the camera points toward the sky). Finally, we propose new benchmarks to evaluate gravity-aware camera-controlled video generation, assessing models’ ability to generate extreme camera angles and quantify their input prompt entanglement.

ECCV 2026

FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model

Yuhwan Jeong, Hyeonseong Kim, Daehyun We, Seonkyu Song, Jinnyeong Yang, Hyun-Kurl Jang, Youngho Yoon, Kuk-Jin Yoon

Synthetic data for autonomous driving is surging, poweredby diffusion models that promise scalable scene generation. Yet key ob-stacles remain, since multi-view and temporal consistency often requiresbackbone fine-tuning or added layers, which can erode pretrained knowl-edge and weaken text alignment. Models also stay close to the train-ing distribution, struggling under adverse weather and unseen configu-rations, while fidelity favors frequent over rare classes. We address thesegaps with FrozenDrive, a controllable generative framework that pre-serves pretrained diffusion knowledge while achieving strong consistency.FrozenDrive conditions on driving-stack signals and text prompts, andexpands the context of frozen self-attention across views and frames topromote cross-view alignment and temporal coherence in one pass, with-out trainable spatio-temporal modules in the diffusion backbone. Anobject-focused constraint further improves fidelity for rare categories.Without weather- or scene-specific fine-tuning, FrozenDrive synthesizesglobally coherent multi-view driving scenes from text and surpasses priorbaselines under adverse and rare conditions. On nuScenes, FrozenDrive-augmented data improves AD model performance, especially at night andin rain, demonstrating strong robustness with scenario-targeted data.

ECCV 2026

FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

Vladislav Bargatin, Alexander Yakovenko, Khaled Abud, Dmitriy Vatolin

Optical flow methods typically rely on task-specific inductivebiases, such as correlation volumes, feature warping, and iterative refine-ment, among others, to reach high accuracy. While effective, such biasesconstrain the model to predefined heuristics, which can limit its expres-sivity and lead to more complex pipelines and additional computationalcost. We present FreeFlow, a hierarchical transformer built without anyflow-specific components, using instead a single feed-forward encoder–decoder. FreeFlow combines three attention variants: window attentionfor local processing, shifted-window attention for cross-window informa-tion exchange, and a global attention operating at a reduced resolu-tion. The resulting architecture scales naturally with model capacity,enabling a consistent accuracy gain from small to large variants. Despitethe absence of standard inductive biases, FreeFlow achieves state-of-the-art results on major benchmarks, including Sintel (0.68/1.48 EPE onClean/Final), KITTI-2015 (3.23 Fl-all), and Spring (3.192 1px), whileremaining memory efficient at 1080p inference.

ECCV 2026

Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detection

Zijian Zhuang, Yixiong Zou, Yuhua Li, Ruixuan Li

Cross-Domain Few-Shot Object Detection (CDFSOD) aimsto transfer knowledge from data-rich upstream generic domains to down-stream expert domains using scarce training data, where the significantdomain gap and data scarcity make it an unsolved challenge. To ad-dress this problem, we revisit a natural yet underexplored approach inCDFSOD: data augmentation, by directly synthesizing data through dif-fusion models to supplement limited training samples. However, due tolarge domain gaps, we find that current diffusion methods cannot pro-duce good results, leading to performance even lower than using theoriginal images. To address these limitations, we divide the domain gapsinto visual gaps and semantic gaps for separate analysis. For the visualgap, we find that the diffusion model cannot distinguish noise from use-ful information on expert domains, which can be mitigated by addingweakened noise. For the semantic gap, we find that the background se-mantics shows much smaller gaps between domains than foreground se-mantics, and we can bridge this gap by background inpainting. Basedon the above analysis, we propose a method (Selective Inpainting withTailored Noise, SITN) to dynamically take different strategies for down-stream data synthesis based on their different gaps from the generaldomain, including a Generation Module for adding tailored noise and aSelection Module to dynamically select the inpainting regions. Extensiveexperiments on 6 datasets of CDFSOD and 4 datasets of cross-domainfew-shot segmentation (CDFSS) validate that we can synthesize helpfuldata, achieving new state-of-the-art performance. Our codes is availableat https://github.com/zzzzj311-droid/Free-Lunch-SITN.

ECCV 2026

EGVLR: Evidence-Grounded Vision–Language Reinforcement for Anomaly Reasoning

SHIH-CHIH LIN, Ying-Heng Lu, DONG-YOU YE, Shang-Hong Lai

005 Multimodal large language models (MLLMs) have recently 005006 shown promise for industrial anomaly understanding, where models are 006007 expected not only to determine whether an object is defective, but also to 007008 localize visual evidence and explain defect-related decisions. However, ex- 008009 isting MLLMs often rely on language priors or shortcut reasoning when 009010 facing fine-grained industrial defects, resulting in inconsistent outputs 010011 such as correct answers with invalid locations, hallucinated defect boxes 011012 on normal samples, or rationales that contradict the final prediction. To 012013 address these issues, we propose EGVLR, an evidence-grounded vision– 013014 language reinforcement framework for anomaly reasoning. EGVLR fol- 014015 lows a unified Evidence-Driven Diagnostic Protocol (EDDP), where each 015016 response explicitly contains visual evidence, diagnostic logic, spatial loca- 016017 tion, and the final answer. The framework consists of four stages: Progres- 017018 sive Visual-Evidential Fine-Tuning (PVE-FT), Knowledge-Grounded In- 018019 struction Tuning (KG-IT), Geometry-Semantic Decoupled Preference 019020 Optimization (GS-DPO), and Box-Guided Segmentation Rendering (BGSR). 020021 PVE-FT first aligns the model with localized visual evidence through 021022 synthetic anomalies, 3 × 3 grid grounding, local verification decoys, and 022023 null-hypothesis calibration. KG-IT then internalizes industrial-domain 023024 QA behavior while preserving the same output schema. GS-DPO fur- 024025 ther optimizes answer correctness, geometric localization, BGE-based 025026 rationale semantics, and answer–location–rationale consistency. Finally, 026027 BGSR converts predicted bounding boxes into dense masks using an off- 027028 the-shelf segmentation backend. Experiments on MMAD demonstrate 028029 that EGVLR improves evidence-grounded industrial anomaly reasoning, 029030 especially on defect localization, reasoning-dependent QA, and spatial 030031 false-positive control. 031

ECCV 2026

E-MOTION: A Dataset for Event-Based Scene Flow Estimation with Independent Moving Objects

Ivan Gutierrez Rodriguez, Julien Moreau, Chiara Bartolozzi, Arren Glover

Scene flow represents the 3D cartesian motion of points inthe world and can be used for applications such as navigation in dy-namic environments, human-robot interaction or non-rigid motion anal-ysis, among others. Despite the potential benefits of event cameras formotion estimation tasks, due to their high temporal resolution and low-latency, not many works have yet addressed their use for scene flowestimation. Progress may be limited by the unconventional data addingcomplexity to established processing pipelines, but also due to the lackof event camera datasets with scene flow ground truth. With the aimof filling this gap, we present E-MOTION: a large and versatile datasetrecorded with high-resolution event cameras suitable for depth, opticalflow and scene flow estimation. E-MOTION features a number of inde-pendent moving objects for which ground truth poses and segmentationmasks are also available. We release a total of 43 sequences with denseground truth maps and poses at 200 Hz. The dataset is available at:https://emotion.hds.utc.fr/

ECCV 2026

AVQ-Attention: Adaptive Vector-Quantized Attention

Winfried van den Dool, Patrick Forré, Amirhossein Habibian, Yuki Asano, Max Welling

The O(N 2 ) complexity of attention over N tokens remainsa computational bottleneck in transformer models. Vector-Quantized(VQ) attention reduces this to O(M N ) by representing keys with Mcodewords, but applies uniform codebook capacity regardless of whereattention mass concentrates: high-attention regions of key space maybe coarsely approximated while low-attention regions waste representa-tional capacity. We propose Adaptive Vector-Quantized (AVQ) Atten-tion, which adaptively allocates codebook capacity based on attentionimportance. Starting from a small set of codewords, our method identifiesthe most important codes during the forward pass and refines them withpre-learned child codewords, achieving fine-grained quantization whereit matters most while maintaining coarse quantization elsewhere. We de-velop an implementation using custom Triton kernels that enables the fulladaptive refinement process, including importance scoring, child code-word insertion, and parent contribution replacement, to be carried outwithin the tiled computation paradigm of Flash Attention with minimaloverhead. Our approach maintains O(M N ) complexity while achievingimproved accuracy-efficiency trade-offs compared to fixed-codebook VQ-attention.

ECCV 2026

Don’t Teach Instability, Teach Robustness: Selective Sensitivity Gating for Adversarial Robust Distillation

Jingqi Ji, Quan Kong, Chaojie Gu, Yuanchao Shu, Cong Wang

Adversarial Robustness Distillation (ARD) is the most viableway for transferring defensive capabilities from over-parameterized teach-ers to lightweight student models. However, existing ARD techniques relyon the assumption that the teacher’s decision boundary is a perfect geo-metric oracle. In this paper, we identify a critical gap from the stabilityperspective that such exact matching forces blind inheritance of teacher’ssensitivity noise, punishing the student’s stability even if they are morerobust. To address this challenge, we propose the AEGIS (AdversarialError Gating and Instability Suppression) framework that replaces sim-ple logit matching with gated supervision. It leverages a pair of logit andsensitivity gates to rectify potential errors/instability on the categoricaland gradient-level, respectively. Extensive evaluations on CIFAR-10/100,and Tiny-ImageNet show that AEGIS significantly outperforms state-of-the-art methods, achieving a 3.12% gain under the strong AutoAttackand preserving nearly 50% accuracy under large perturbation intensities(ϵ = 16/255) where traditional baselines collapse. Code is available athttps://github.com/JingqiJi03/AEGIS.

ECCV 2026

Disentangling Rotation and Translation from SE(3)-Equivariant Features for Shape Assembly

Hee-Jun Jung, Uigeun Ahn, JINHWI PARK, Kangil Kim

3D shape assembly requires predicting 3D pose by estimatingrotation and translation separately to align objects or fractures. For 3Dpose prediction, prior works utilize the entangled features that capture3D pose information well, but this mixed representation impedes gen-eralization. To address this problem, we propose the SOT encoder thatdisentangles 3D pose into an SO(3)-equivariant (rotation) feature and aT(3)-equivariant (translation) feature. The SOT encoder consists of twobranches: 1) a rotation branch that suppresses translation by project-ing features onto the translation null space, and 2) a translation branchthat suppresses rotation by translation loss and directly subtracts theSO(3)-equivariant representation from its SE(3)-equivariant counter-part. Across a variety of assembly models and diverse datasets, the SOTencoder consistently demonstrates improved generalization performancein 3D shape assembly. Furthermore, in-depth analyses indicate that thedisentangled features show improved equivariant behavior with respectto the target factor while exhibiting invariant behavior with respect tothe other. These results indicate that explicitly encouraging factor dis-entanglement is a straightforward and effective approach for 3D shapeassembly. The code is available at https://github.com/maroo-sky/SOT.

ECCV 2026

DIGS: Differentiable, Incremental, Global, Scalable Pruning for Language Models

Bingcong Li, Junlin Xian, Tao Jiang, jwlin jwlin, Cheng Zou, Geng-Li Zhang

The rapid scaling of large language models (LLMs) and vision-language models (VLMs) has sharply increased compute and storagecosts, motivating extensive research on model pruning. Yet most prun-ing pipelines underuse data scaling during pruning, i.e., increasing thepruning-phase token budget. We revisit L0-regularized structured chan-nel pruning, valued for differentiability, incremental scheduling, and globaldecision-making. We uncover a long-horizon instability: the Lagrangemultiplier grows during training, amplifies pruning gradients, and causeslate-stage oscillations in both gradients and loss. We derive sufficient L2-norm bounds on pruning-specific gradients and reformulate the loss toenforce these bounds per update, replacing dual ascent with a coefficientdetermined by the gradient constraint. Building on this foundation, weintroduce DIGS (Differentiable, Incremental, Global, Scalable pruning),which enables stable and efficient structured channel pruning withoutauxiliary stabilizers while supporting much larger pruning token bud-gets. Applied to the Qwen2.5VL LLM backbone, DIGS scales pruningbeyond 100B tokens and achieves competitive results on public multi-modal benchmarks. Within this setting, larger pruning token budgetsyield monotonic performance gains at fixed sparsity.

ECCV 2026

Differentiable Polarized Path Tracing

Pramod Rao, Jérémy Riviere, xilong zhou, Abhijeet Ghosh, Abhimitra Meka, Thabo Beeler, Marc Habermann, Christian Theobalt, Delio Vicini

Physically based differentiable rendering has proven to be apowerful tool for inverse rendering problems (e.g., 3D reconstruction, re-flectance estimation, lighting estimation). However, most existing meth-ods operate solely on radiometric intensity, discarding valuable polariza-tion cues that constrain scene geometry and material properties. Whileforward simulation of polarized light is well-defined via Mueller-Stokescalculus, extending reverse-mode differentiation to this domain presentssignificant challenges. The rank-deficient nature of common polarimet-ric operators, such as linear polarizers and diffuse reflections, violatesthe invertibility assumptions of standard gradient estimators like pathreplay backpropagation and results in numerical instability. We addressthis by proposing a robust, polarization-aware differentiable path tracingmethod. Our approach estimates unbiased gradients through a combina-tion of path replay and local caching. This formulation enables efficientand stable optimization of material and lighting parameters in complexscenes, broadening the applicability of physically based inverse rendering.Project page: https://vcai.mpi-inf.mpg.de/projects/DPPT/

ECCV 2026

Delving into Latent Spectral Biasing of Video VAEs for Superior Diffusability

Shizhan Liu, Xinran Deng, Zhuoyi Yang, Jiayan Teng, Xiaotao Gu, Jie Tang

Latent diffusion models pair VAEs with diffusion backbones,and the structure of VAE latents strongly influences the difficulty of dif-fusion training. However, existing video VAEs typically focus on recon-struction fidelity, overlooking latent structure. We present a statisticalanalysis of video VAE latent spaces and identify two spectral propertiesessential for diffusion training: a channel-wise eigenspectrum dominatedby a few modes, and a spatio-temporal frequency spectrum biased towardlow frequencies. To induce these properties, we propose two lightweight,backbone-agnostic regularizers: Latent Masked Reconstruction and Lo-cal Correlation Regularization. Experiments show that our Spectral-Structured VAE (SSVAE) achieves a 3× speedup in text-to-video gen-eration convergence and a 10% gain in video reward, outperformingstrong open-source VAEs. Code is available at: https://github.com/zai-org/SSVAE.

ECCV 2026

D-Rex : Diffusion Rendering for Relightable Expressive Avatars

Timo Teufel, xilong zhou, Umar Iqbal, Jan Kautz, Marc Habermann, Vladislav Golyanik, Christian Theobalt

We present D-Rex, a person-specific framework for photore-alistic, relightable, expressive, and animatable full-body human avatarswith free-viewpoint rendering. Existing methods for relightable full-bodyavatars rely on explicit 3D intrinsic decomposition with analytic re-flectance models, which require accurate geometry registration and care-ful optimization to capture realistic light transport effects. This tightcoupling of relighting with avatar modeling has hindered expressiveness:to our knowledge, no existing method demonstrates strong facial anima-tion alongside relighting, limiting applicability in telepresence, gaming,and virtual production. We propose to decouple relighting entirely fromavatar modeling by treating it as an image-space post-process: a learnedtranslation from flat-lit, albedo-like renderings to a target HDR illumina-tion. To this end, we leverage the strong generative prior of a pre-trainedvideo diffusion relighting model, fine-tuned via LoRA on paired flat-litand relit frames captured in a light stage. The flat-lit driving framesare produced by an independent expressive full-body avatar frameworktrained under white-light conditions, requiring no modification to sup-port relighting, making D-Rex directly applicable to any white-lightavatar system. We demonstrate that D-Rex enables view- and tempo-rally consistent relighting while faithfully preserving expressive motionand fine-grained facial detail, outperforming physically-based relightableavatar baselines. Project page: https://vcai.mpi-inf.mpg.de/projects/DRex/

ECCV 2026

CSS-BA: Gate Guided Column Space Search for Bundle Adjustment

Ayano Kaneda, Takafumi Taketomi, Shugo Yamaguchi, Shigeo Morishima

Bundle adjustment (BA) remains a critical refinement mod-ule for image-based 3D reconstruction and continues to improve geomet-ric accuracy even in learning-based pipelines. However, in low-parallaxand near-rotational regimes, classical Schur-based Levenberg–Marquardt(LM) often becomes ill-conditioned and yields unreliable pose and cali-bration estimates. We propose Gate-Guided CSS-BA, a solver-side mod-ification of Schur-LM that preserves the classical BA objective and trust-region framework while constraining each update to a geometrically in-formed low-dimensional subspace. By integrating Column Space Search(CSS) with geometry-aware gating, the method stabilizes the Schur-LMupdate without altering the estimation problem. In contrast to keyframeor state-selection approaches, all camera and point parameters remainin the optimization problem; only the update direction is restricted. Themethod serves as a drop-in replacement for existing BA pipelines. Exper-iments on both generic and challenging weak-geometry scenarios showmore stable optimization, improved relative pose accuracy, and compet-itive calibration behavior while maintaining reprojection quality.

ECCV 2026

CROSS: Cascaded Distillation and Dual-Constraint Grounding for Remote Sensing Referring Segmentation

Tingzhang Luo, Ruizhong Liu, Yichao Liu, Cheng Fan, Yu Liu, Jianyuan Guo

Referring Remote Sensing Image Segmentation (RRSIS) hasachieved significant progress through the integration of VLMs and theSegment Anything Model (SAM). However, this progress largely relieson strong pre-trained capabilities, while leaving two fundamental limi-tations insufficiently addressed: (1) Architectural Weak-Coupling, wherethe unidirectional flow forces reliance on coarse VLM prompts and wastesSAM’s pixel-level structural guidance, causing localization drift; and (2)Object-Centric Semantic Bias, where models overemphasize dominantobject semantics while remaining insensitive to spatial reasoning cru-cial for RRSIS. Motivated by these observations, we propose CROSS, atightly integrated paradigm for RRSIS. First, we introduce Linguistic-Guided Cascaded Distillation (LGCD) to bridge the architectural gap,which distills SAM’s geometric affinities as soft regularizers into VLM in-termediate layers, injecting dense structural priors to refine localization.Second, Perspective-Spatial Contrastive Learning (PSCL) imposes cross-anchored constraints by mining mask-filtered deceptive distractors andspatial-linguistic counterfactuals as hard negatives, explicitly shatter-ing semantic shortcuts to enforce genuine logical consistency. Extensiveexperiments on RRSIS benchmarks demonstrate that CROSS achievesstate-of-the-art performance and maintains precise localization even un-der severe spatial description perturbations, standing as a robust newparadigm for RRSIS. https://clarence-cv.github.io/CROSS/.

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