arXiv_CV
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RoPT: Robust 6DoF Object Pose Tracking in Video
2026-10-20 22:25:47Yongqing Liang, Yuzhong Chen, Sihang Li, Junsuk Kim, Chase Kidder, Hui-ping Wang, Yunzhi Xu, Raihan Haque, Yubiao Zhang, Chen FengAbstract
Accurate 6-DoF object pose tracking is essential for robotic interaction, yet conventional monocular pipelines frequently suffer from catastrophic divergence under rapid object motion. We present RoPT, a robust tracking framework engineered to overcome this limitation by unifying visual perception, temporal kinematics, and global re-localization into a tightly coupled closed loop. Unlike existing open-loop trackers, RoPT delivers three critical capabilities that fundamentally alter system resilience. First, the visual perception component tracks object masks to robustly handle partial and severe occlusions. Second, a kinematic factor graph regularizes frame-to-frame updates, forecasts forward pose predictions, and drives a parallel multi-hypothesis verification scheme to bypass local minima. Finally, an active re-localization module transforms the pipeline into a closed-loop system, making it failure-aware and capable of deterministic self-recovery. We demonstrate the effectiveness of our framework across challenging benchmarks alongside real-world robotic demonstrations, achieving significantly higher tracking survivability under rapid motion.
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URL
https://arxiv.org/abs/2610.28962
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Pair-Free Coarse-to-Real Video Generation via Real-to-Coarse Image Supervision
2026-10-20 22:09:29Gonzalo G\'omez-Nogales, Daohan Lu, Peiye Zhuang, Tinghui Zhou, Yi ZhouAbstract
Coarse-to-real (C2R) video generation converts coarse animation, simulation, or game-rendered videos into photorealistic videos while preserving scene layout, camera motion, and dynamics. Training such models requires aligned (coarse, realistic) video pairs, which are expensive to create and confine prior systems to narrow synthetic domains. We exploit a simple asymmetry: paired videos are hard to obtain, but paired real-to-coarse images are easy to construct with existing editing and 3D reconstruction tools. Using these image pairs, we train the DINO-R2C model to transfer realistic images to coarse images in DINO feature space, and apply it frame-wise to ordinary real videos to synthesize pseudo-coarse videos. The C2R# model then learns to generate each real video from its pseudo-coarse counterpart, free from needing (coarse, realistic) video pairs. At inference, the DINO-R2C model is discarded and actual coarse videos drive the C2R# model directly. Thus, our pair-free training scales with any real-video corpora rather than with paired synthetic videos. We will release the model and project webpage at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28954
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MINER: Modeling Inter- and Intra-Person Variance for Reidentification
2026-10-20 21:34:43Oron Nir, Eliyahu Strugo, Ariel ShamirAbstract
Clothes-Changing Person Re-Identification (CC-ReID) remains challenging due to drastic appearance shifts across outfits. Most prior works devise biometric signal-extraction heuristics to enhance inter-person variance, e.g., using segmentation masks, while leaving intra-person variance uncontrolled. We present MINER, a simple model-agnostic framework that suppresses garment bias by contrasting hard examples and adaptively balancing dataset-dependent intra- and inter-person variance. Extensive experiments show that MINER improves retrieval across CC-ReID benchmarks, achieving state-of-the-art performance on four datasets and ranking among the leaders in all three tracks of the open TVRID RGB-Depth ReID challenge.
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URL
https://arxiv.org/abs/2610.28941
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RAHF-Net: Range-Aware Heterogeneous Fusion for LiDAR-Assisted RGB Semantic Segmentation
2026-10-20 21:00:32R. D. Lin, Pengcheng Weng, Ge Wang, Han Ding, Fei WangAbstract
This paper studies LiDAR-assisted RGB semantic segmentation, where LiDAR point-cloud features improve dense pixel-wise predictions in the RGB image plane rather than 3D point labels or detections. Existing 2D fusion methods often project point clouds into shallow depth/range maps and apply spatially uniform fusion, discarding rich 3D structure and ignoring the range-dependent reliability of outdoor LiDAR. We propose RAHF-Net, a Range-Aware Heterogeneous Fusion Network. Its Cylindrical-to-Perspective (C2P) module projects pretrained cylindrical voxel features into the image plane, preserving structured 3D semantic priors. A Range-Aware Heterogeneous Fusion (RAHF) module then uses a soft depth-derived range mask to perform LiDAR-guided geometric enhancement in reliable near-field regions and visual-context enhancement in sparse far-field regions. On KITTI-360, RAHF-Net achieves 65.76 mIoU, outperforming representative RGB-only and RGB-LiDAR baselines, including CMX by 1.45 points. Experiments on DELIVER further demonstrate improved robustness under adverse weather, illumination degradation, and sensor corruption.
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URL
https://arxiv.org/abs/2610.25400
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Data-Driven Glyph Positioning for Handwritten Music Synthesis
2026-10-20 20:25:26Elona Shatri, Ji\v{r}\'i Mayer, Jan Haji\v{c} jrAbstract
Synthesised data is a common strategy to address the lack of annotated handwritten music scores in Optical Music Recognition (OMR). Smashcima is a recent synthesis framework that assembles pages of handwritten scores by sampling glyph images from MUSCIMA++ and positioning them with hard-coded rules. In this work we ask whether these rules are grounded empirically and whether a learned model for context-dependent positioning can further improve synthesis quality. We extract inter-glyph distributions from around 285 thousand annotated relationships across MUSCIMA++ and OmniOMR, finding that several key parameters deviate systematically from real handwriting. Replacing these constants with empirical means reduces positioning RMSE by up to 60%. We then train a Graph Neural Network (GNN) on the Music Notation Graph structure to predict context-dependent offsets, achieving an additional 50 to 85% RMSE reduction on stems and beams by modelling how neighbouring glyphs constrain placement. Cross-dataset analysis shows that spacing differences between the two corpora are explained by note density (3.3), not style, and that duration-proportional spacing does not hold in handwriting (R2 = 0.001). In downstream evaluation, a YOLOv8 detector pretrained on GNN-positioned synthetic data achieves +30% mAP50 over the baseline and +11% over empirical means when fine-tuned on 200 real handwritten staves, showing that positioning accuracy matters for downstream OMR.
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URL
https://arxiv.org/abs/2610.28899
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Policy-Mask: Budget-Aware Adaptation of Diffusion Features to Discriminative Tasks
2026-10-20 20:13:31Ivan Listopadov, Maksim Nakhodnov, Aibek AlanovAbstract
Selecting intermediate features from a frozen diffusion model for discriminative tasks is non-trivial because the useful signal is distributed across the denoising trajectory, network depth, and channels. In practice, this requires choosing a timestep, a block, and a channel subset. Existing methods either fix these choices by hand or optimize only a few extraction hyperparameters, most often the timestep, then reuse the same rule across inputs and budgets. We introduce Policy-Mask, a budget-conditioned policy trained on the downstream objective that jointly selects all three under a prescribed channel budget. Without normalization, the channel-score contribution grows with the number of selected channels and dominates the timestep and block updates. We normalize it by subset cardinality, keeping the three policy heads on comparable scales and stabilizing joint training. At matched budgets, Policy-Mask improves over fixed extraction and feature-filter baselines across segmentation, classification, and semantic correspondence, reaching 38.9 mIoU on ADE20K, 75.4% top-1 on CIFAR-100 at 128 dimensions, and 76.3 PCK@0.1 on SPair-71k. Component-wise ablations show that block selection materially affects downstream accuracy, and the highest recorded channel-budget score occurs at 256 retained channels, identifying a practical operating point for compact diffusion representations. We further adapt Policy-Mask to diffusion transformers, including PixArt-$\Sigma$ and SD3.5 MMDiT, by replacing U-Net channels with image-token hidden dimensions.
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URL
https://arxiv.org/abs/2610.28889
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Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration
2026-10-20 19:59:24Matthew Strong, Ankush K. Dhawan, Julien Poffet, Giuse Pham, Jinho So, Monroe Kennedy IIIAbstract
Human video scales easily and does not need robot hardware, but existing methods require depth sensing, overlay the robot onto 2D video, or generate data only for the demonstrated objects. We present Real2Sem2Real (R2Sem2R), a real-to-semantic-to-real pipeline that turns one monocular RGB video of a human demonstration into thousands of robot demonstrations across the semantic object instances of a category. R2Sem2R tracks the SE(3) pose of each object with DINOv3-distilled Gaussian splats, outperforming existing methods and FoundationPose with estimated metric depth. R2Sem2R then aligns digital cousins, each generated from one or a few photographs, to the demonstrated objects with a semantic alignment. This alignment matches whole objects and the functional regions of target objects, such as a coffee machine's tray. R2Sem2R maps the human hand to a feasible robot grasp that extends to the cousins. Robot demonstrations are generated either in IsaacSim, for photorealistic rendering, or MuJoCo, for contact physics, and R2Sem2R can generate over 2,000 episodes in about two hours. On two real robots and four tasks, we train a vision-language-action policy on data from one human video per task, and policies trained on our data match or exceed policies trained on teleoperation across diverse object instances. The project page is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28880
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Arbitrary-Scale Anisotropic 3D MRI Super-Resolution with Gaussian Prototypes
2026-10-20 19:52:14Aditya Rastogi, Ramon Leiser, Prateek Singh, Martha Foltyn-Dumitru, Martin Bendszus, Fabian Bamberg, Klaus Maier-Hein, Kai Schlamp, Alexander Radbruch, Gianluca Brugnara, Philipp VollmuthAbstract
Reducing MRI acquisition time improves patient comfort, reduces motion artefacts, and increases scanner throughput. Clinical protocols often shorten acquisitions by sacrificing spatial resolution, particularly along the slice axis. Super-resolution can recover higher-resolution images, but existing methods are typically specialised to one scale or acquisition geometry, limiting their use across heterogeneous clinical protocols. We present GSMR3D, a continuous volumetric model that reconstructs arbitrary isotropic and anisotropic resolutions with a single checkpoint per MRI contrast. It combines a learned dictionary of anisotropic 3D Gaussian prototypes with scale-conditioned frequency encoding and an exact measurement-consistency projection, keeping reconstructions consistent with the acquired low-resolution data. We train separate models on 3,731 contrast-enhanced T1-weighted and 30,254 T1-weighted brain MRI volumes from clinical and population-based cohorts. We evaluate GSMR3D against geometry-specific specialists for isotropic, in-plane, and through-plane super-resolution using reconstruction quality and downstream tasks: brain tumour segmentation, brain-metastasis detection, and brain parcellation. GSMR3D outperforms specialists at isotropic $2\times$ and achieves better LPIPS, DISTS, and FID across all geometries, although specialists retain higher PSNR in anisotropic settings. It more faithfully preserves glioma segmentations across all geometries, achieves the highest lesion sensitivity on an independent brain-metastasis cohort, and the highest brain-parcellation agreement for isotropic and in-plane reconstruction. Reconstruction takes about 6s per volume, adding little overhead relative to acquisition-time savings. GSMR3D offers a flexible approach to MRI scan-time reduction with one model per contrast across geometries while preserving information relevant to downstream tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2610.24858
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Small Average Error, Wide Individual Error: General-Purpose LLMs Estimating Energy Intake from Meal Photos in a Feeding Trial
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Photo-based estimation of food energy could ease dietary assessment, and general-purpose multimodal large language models (LLMs) make such estimates easy to obtain. Most evaluations use one photo taken before eating and compare with energy served, while dietary assessment needs energy eaten. We evaluated eight models from three vendors (Claude Haiku, Sonnet and Opus 5.5; GPT-6 luna, 6.1 sol and 6 astra; Gemini 3.8 Flash and 4 Argon) on 802 meals from ACETADA, a controlled feeding trial with weighed intake, with the before photo only, before and after photos in separate calls (subtraction), or both photos in one request. Three results ran against intuition. Subtraction under-estimated intake for six of eight models, because the models still report hundreds of kilocalories for a nearly empty tray. Scaling the before-only estimate by the average share eaten (0.87) matched much of the gain from the after photo; beyond that constant, the second photo lowered meal-level error by only 2.2 to 4.4 points, mainly in meals with food left. With one photo, two models had a near-zero intake bias only because they under-estimated the served energy and ignored uneaten food. In ablations, adding the after photo improved accuracy under both prompt formats for seven models, while prompt effects varied across models. Over 148 feeding days, five models kept the 90% confidence interval of their mean error within a pre-set 10% margin; one model also met the pre-set 30% limits of agreement, narrowly (-28% to +27%; limits outside 30% cannot be excluded), and the other seven did not. Protein was under-estimated by every model, most in protein-dense meals; repeated calls changed error by at most 1.4 points; a high reasoning effort helped only the smallest model. A low average bias is not evidence of accurate individual intake estimation; such estimates should support, not replace, assessment by a dietitian.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26538
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DualHeads: Reward-tilted few-step diffusion made simple
2026-10-20 19:31:03Yuanzhi Zhu, Eleftherios Tsonis, Simo Alami Chehboune, Xi Wang, St\'ephane Lathuili\`ere, Vicky KalogeitonAbstract
Distilled diffusion models generate high-quality images in a few sampling steps, but aligning them with task requirements and human preferences is difficult. Methods developed for multi-step diffusion models rely on differentiable rewards, or on score fields and stochastic trajectories that distilled generators may lack. We ask whether a few-step generator can be adapted using only external reward evaluations and reference samples, and show that two binary classifiers suffice. Our method, DualHeads, implements them as lightweight heads on a frozen diffusion backbone. The reward head learns which samples of the same prompt score higher, the reference head separates reference images from generated ones, and the generator maximizes a weighted sum of their logits. When the heads are optimal, this objective is KL-regularized maximization of the learned reward, and the ratio of head weights sets the regularization strength. Empirically, with both rule-based and model-based rewards, a few-step DualHeads generator reaches task rewards close to those of 40-step reinforcement learning methods while preserving image quality, at no extra inference cost. We hope that this simple and elegant dual-head formulation will help advance few-step reward-tilted generation.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28858
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Depth-guided Geometric Adaptation for Few-shot Image Classification
2026-10-20 19:29:42Zhiqi Miao, Estefan\'ia Talavera, Weichuan Zhang, Jiapan GuoAbstract
Few-shot image classification (FSIC) frequently suffers from visual ambiguity, where background clutter and RGB appearance similarities hinder discriminative feature matching in data-scarce settings. To mitigate this, we propose DAFIC, a geometry-guided framework that incorporates monocular depth into representation learning, episodic adaptation, and dense support-query matching. DAFIC first learns geometry-aware token representations through RGB-D self-supervised pre-training. Subsequently, the Depth-guided Geometric Adaptation (DGA) module uses depth-derived geometry to refine patch representations with geometry-enriched class context while suppressing background interference. Spatial Bidirectional Patch Matching (SBiM) then performs bidirectional dense matching on the refined features, reducing sensitivity to spurious correspondences and background clutter. Extensive experiments on four benchmarks show that DAFIC achieves strong performance against existing methods, with ablation and robustness analyses supporting the effectiveness of coupling geometry-conditioned episodic adaptation with bidirectional matching for few-shot visual recognition.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28856
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Beyond Correct Answers: How Compositional Cues Shape Visual Judgments
2026-10-20 19:16:00Xiaoyu Ma, Zheyuan Zhang, Kaiwen Shi, Yanfang YeAbstract
Visual question answering requires models to assign attributes and relations to the correct objects, a problem known as visual binding. Structured prompts make these assignments explicit through labeled fields. Yet an improvement from the full prompt does not reveal which labels help or how they interact. First, we isolate field-label effects across ten models and six tasks by holding the image and claim fixed. These comparisons reveal differences that overall accuracy can hide. With a relation label present, adding object-role labels helps Qwen3 but hurts Qwen2 on Spatial. Second, we compare auxiliary hints that preserve or swap the question's role assignments against a no-hint baseline. Across two image sets, Qwen3's advantage with preserving hints mainly reflects errors caused by swapped hints. Preserving hints remain near baseline. Field order further affects the harm. Third, we test clearer hint wording and direct selection of the relation shown in the image. Clearer hints improve text matching but do not consistently improve visual judgments. In contrast, relation selection partly reduces errors for some hints in Qwen3 and GPT-4.1-mini.
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URL
https://arxiv.org/abs/2610.28843
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Sketch2Sim: an end-to-end modular framework for hand-drawn circuit analysis
2026-10-20 19:04:12Mohammad Kawsar, Al Amin Islam Ridoy, Md Rakib Hossain, Farzana Akter, AKM Bahalul Haque, Rejwan Bin Sulaiman, Mohammed Amin AlmaiahAbstract
This study proposes a sketch-to-simulation system to solve handwritten circuit diagrams. The system converts raw images into a Simulation Program with Integrated Circuit Emphasis (SPICE)-compatible netlist and automatically calculates the circuit's nodal voltages and currents. Another objective is to propose a holistic framework that benefits students and educators by enhancing the teaching and learning experience of electric circuits and expediting engineering design reviews. This article presents a multistage pipeline that converts raw photographs of handwritten electric circuits into executable SPICE-like netlists, providing a solution. A dataset of 1,000 hand-drawn Resistor-Inductor-Capacitor (RLC) circuits was curated and annotated for components, text regions, and character recognition. The proposed system decomposes a circuit diagram into three stages: (i) dual You Only Look Once (YOLO)-based detectors for components and text regions, (ii) a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) and Optical Character Recognition (OCR)-based domain-aware symbol value extraction, and (iii) image processing with thresholding, skeletonization, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to automatically identify nodes and map them with corresponding components. The proposed system produces the netlist and solves the circuit. The qualitative and quantitative ablation studies on modular stages, as well as end-to-end tests, demonstrate robust performance across various handwriting styles, lighting conditions, and this http URL study serves as an interactive learning aid and practical tool for professionals, providing a comprehensive user interface (UI), enabling rapid digitization of circuit sketches for study, design, prototyping, or troubleshooting.
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URL
https://arxiv.org/abs/2610.28836
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Conditional Imaging for Privacy-Preserving Visual Sensing
2026-10-20 19:02:26Hengyu Lian, Debabrata Mandal, Anzhou Wen, Yujie Wang, Xi Peng, Praneeth ChakravarthulaAbstract
Conventional visual sensing faces a fundamental dilemma: information that should remain private today may need to be recovered tomorrow. This challenge is becoming increasingly important as cameras become pervasive in our daily surroundings. Existing safeguards either protect visual content only after an image is formed or suppress information during capture in ways that prevent its later recovery. Here, we show through an information-theoretic analysis that privacy and recoverability cannot be achieved simultaneously in static optical systems. Information preserved in a measurement for later reconstruction also remains available to an observer of that measurement, limiting the privacy such systems can provide. To overcome this limitation, we introduce conditional imaging, an imaging approach that changes the optical state for every captured frame. The measurement alone conceals the scene, while knowledge of the corresponding physical system state enables its reconstruction. We analyze how the optical parameters determine the information that can be recovered and use this analysis to guide the design of a camera prototype. We evaluate the system on a captured dataset of human faces using increasingly powerful attempts to recover the scene, from direct visualization and computational inversion to learned reconstruction and identity recognition. Our experiments show that conditional imaging preserves identity in authorized reconstructions while withholding visual information from observers without access to the system state. By making the physical state of the camera part of the information needed for reconstruction, conditional imaging enables visual information to be protected at the point of capture while remaining recoverable for future authorized use.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28835
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HorizonComposer: Spatiotemporally Consistent Driving Video Editing with Enriched Traffic Semantics
2026-10-20 18:51:48Mauricio Soroco, Yuqiu Liu, Zaid Tasneem, Francesco Pittaluga, Abhishek Aich, Wuyang Chen, Manmohan Chandraker, Ziyu JiangAbstract
Instruction-guided video generative models offer a scalable solution for stress-testing autonomous driving systems by simulating diverse environmental conditions. However, driving scenes operate within a dense Operational Design Domain (ODD) governed by strict traffic semantics. When applied to global driving-condition edits, general-purpose instruction-guided video editors frequently corrupt these semantics, leading to structural hallucinations and ``temporal shock.'' This work distills and protects traffic semantics during video editing at three levels. First, we construct a hybrid dataset by combining temporally aligned real-world videos with synthetic edits, balancing foundational scene structure with unambiguous appearance signals. Second, during training, we adapt advantage conditioning from offline reinforcement learning to align the model using dense rewards. We extract dense, pixel-level rewards from the hybrid data to serve as a localized signal and actively steer generation toward structurally reliable pixels. Finally, our model mitigates temporal shock by introducing thinking frames, a transition mechanism that seamlessly reconciles static image conditioning with dynamic video context. Extensive experiments demonstrate that our method prevents the corruption of multi-actor dynamics, establishing state-of-the-art spatiotemporal consistency, yielding consistent user preference (39% relative improvement in content preservation and 12% in temporal consistency), and achieving a significant 5% gain in downstream road line segmentation and 7% gain in downstream multi-object detection.
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URL
https://arxiv.org/abs/2610.28829
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WorldStream: Streaming Interactive 3D Worlds with Hierarchical Planning and Persistent State
2026-10-20 18:47:26Xiaoxiao Ma, Weiyang Jin, Yanwen Ma, Haoran Li, Zhixiang Wei, Songchun Zhang, Yaowei Li, Junhao Zhuang, Zeyue Xue, Yuming Li, Zheming Fu, Haoyang Huang, Nan Duan, Feng ZhaoAbstract
We present WorldStream, an open-ended 3D world model with three simultaneous capabilities: it extends playable terrain beyond any pre-specified map, acts on open-ended language in the running world, and restores visited places with their edits. A hierarchical planner over a typed decision model emits structured decision state: Macro fixes settlement, ecology, transport, and hydrological structure across a group of tiles; Micro specializes that contract into tile semantics under measured terrain and accepted neighbor commitments. Deterministic local code compiles accepted plans into terrain, roads, settlements, vegetation, and entities. Streaming separates their reach: plans run ahead along the predicted corridor, compilation only where movement will soon need it, geometry behind the user released. One world record retains plans, seeds, object identities and mutable state, boundary commitments, and edits by attribute, revised as the user advances and edits; returning tiles recompile from it. The same record grounds interaction: the model acts on existing entities, including stateful NPCs that update as they act, edits the present region, or attaches intent to unplanned regions. Together they yield a world expanding under bounded budgets, connected across independently planned tiles, and interactive.
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URL
https://arxiv.org/abs/2610.28827
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Register Anything Model for Generalizable and Robust Point Cloud Registration
2026-10-20 18:40:50Zheng Qin, Junhua Xi, Yan Huang, Jiahong Lai, Anwen Huang, Qiong Li, Guangda Zhang, Kai XuAbstract
We present Register Anything Model (RAM), a unified model for generalizable 3D point cloud registration that robustly estimates 6-DoF transformations across diverse domains spanning sensors and environments. Existing registration methods are typically tailored to a single domain, with carefully tuned architectures and hyperparameters, and thus degrade markedly when applied to novel domains. We bridge this gap by training a single model on diverse data from indoor, outdoor, and object domains. A central challenge is the severe geometric discrepancy across domains, i.e., differences in scene scale, sensor noise, point density, and structural patterns, which hinders processing with a unified point-based model. To address this, we introduce a geometry-aware normalization strategy that jointly voxelizes and normalizes point clouds into a standardized geometric space, adaptively based on their geometric properties to preserve salient local structures while standardizing resolution. Built on this normalization, RAM follows a hierarchical coarse-to-fine paradigm, and employs an efficient geometric transformer that models local geometric consistency instead of globally over all points. This design mitigates discretization noise introduced by voxelization, and improves computational efficiency. Extensive experiments on 12 benchmarks demonstrate state-of-the-art performance in terms of inlier ratio and registration recall. Notably, our method outperforms the counterparts trained on each separate domain, showing superior generalization capability.
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URL
https://arxiv.org/abs/2610.28821
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SAIL: A Learnable Generalisation of Spatial Autocorrelation over Graph-Structured Data
2026-10-20 18:28:15Piotr Keller, Fayyaz MinhasAbstract
In vision and scientific domains involving spatial data, a key question is whether node feature values vary independently across space or form coherent spatial structures. Classical spatial autocorrelation statistics such as Moran's $I$ and Geary's $C$ quantify this phenomenon but operate on a single scalar variable with fixed neighbourhood weights, limiting their use for high-dimensional graph-like data. We introduce SAIL (Spatial Autocorrelation Index Learner), a differentiable framework that generalises spatial autocorrelation into a learnable operator by jointly learning feature projections defining candidate scalar fields and inter-node neighbourhood affinities. SAIL produces interpretable local and global spatial coherence descriptors, enabling supervised prediction and unsupervised discovery of spatial autocorrelation modes and hotspots. We show that SAIL exactly recovers classical spatial statistics under appropriate parametrisations and, for tasks driven by latent spatial autocorrelation, can approximate the Bayes-optimal predictor with an inductive bias matched to such tasks. Experiments on synthetic data and large-scale biomedical graphs demonstrate statistical correctness and discovery of spatial coherence with applications across vision, geospatial analysis, and biomedicine. The code repository is available at: this https URL.
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URL
https://arxiv.org/abs/2610.28806
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JUICE: Unified Multimodal Modeling in Pretrained Representation Spaces
2026-10-20 18:25:09Jiahan Li, Huangyuan Su, Shengbang Tong, Shusheng Yang, Ruihan Guo, Suozhi Huang, Hexu Zhao, Pratyusha Sharma, Sherry Yang, Jiatao Gu, Rob Fergus, Kyunghyun ChoAbstract
Most unified multimodal models treat images and text asymmetrically: a pretrained language model generates text, an attached diffusion head generates images, and outputs that contain both are produced in a fixed order, so the first output cannot adapt to the second. We ask whether both modalities can instead be generated by one process that is trained on them jointly from scratch. Pretrained encoder spaces hold both modalities in one continuous form and carry knowledge learned at scale, and recent work finds them effective as generative targets for vision and language. We therefore model the joint distribution directly in these spaces. We introduce JUICE (Joint Unified multimodal modeling In Continuous Encoder spaces), a single flow matching Transformer over frozen image and text representations, in which each modality has its own noise level. Which modalities are clean and which are generated then decides the task: image generation, captioning, visual question answering, or joint generation. Controlled pretraining runs test each design choice. At equal guidance, generating text as continuous states leaves image generation intact (48.41 GenEval, against 47.86 for a model trained only for text-to-image generation), while captions lose some exact wording. Training on joint and conditional denoising together beats either alone, and semantic encoder spaces beat VAE latents and raw pixels as targets. At 2.1B parameters, with no language model initialization and at 224x224, JUICE reaches up to 0.88 on GenEval and 73.1 on VQAv2. By sampling both modalities from the joint distribution (coupled sampling), it can also draw a route on a map while writing out the matching moves. On mazes with several valid solutions, joint success rises from 54.6% with independent sampling to 93.7%, and no modality has to go first. On visual spatial planning, JUICE solves 99.8% of maps at its training sizes.
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URL
https://arxiv.org/abs/2610.28801
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Complexity-Aware Surgical Video Reasoning Segmentation with Digital Twin and Context Memory
2026-10-20 18:10:55Yiqing Shen, Mathias UnberathAbstract
Conventional surgical segmentation relies on closed-set categories or explicit text queries, which cannot resolve implicit queries that require reasoning about workflows, instrument functions, and temporal relationships. To address these limitations, we propose SurgReasonSeg, a complexity-aware method that brings reasoning segmentation over digital twin (DT) representations to surgical video. Specifically, SurgReasonSeg constructs DT representations from surgical foundation models and trains two low-rank adapters (LoRA) of one large language model with reinforcement learning (RL), each specialized for a different query complexity. A lightweight router, also trained with RL, selects between the two adapters to balance latency against accuracy. SurgReasonSeg also sets the temporal context with an adaptive sliding window conditioned on query complexity and workflow phase, and retrieves relevant historical cases from a surgical context memory by workflow similarity and procedural relevance. We further introduce EndoVis-ReasonSeg, a surgical reasoning segmentation benchmark with 1,460 training and 961 test samples across semantic, spatial, and temporal categories. On EndoVis-ReasonSeg, SurgReasonSeg improves J&F over the strongest baseline, ReSurgSAM2, by 0.178, 0.237, and 0.297 on semantic, spatial, and temporal queries, and it also achieves the best results on the Ref-EndoVis17 and Ref-EndoVis18 referring segmentation benchmarks. A backbone-matched attribution protocol separates the contribution of the upstream segmenter from that of the reasoning modules. Our method runs at 18.8 FPS averaged over the query distribution, enabling real-time reasoning segmentation in surgical videos.
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URL
https://arxiv.org/abs/2610.28776
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