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新论文 · v1arXiv:2611.03741

Beyond Proposals: Region-Consistent Open-Set Object Detection

Jiehui Tang, Yunlong Yu, Zhao Wang, Jungong Han

原文摘要

Open-set object detection (OSOD) suffers from a fundamental limitation under dense proposal mechanisms: highly overlapping proposals associated with the same object are often assigned contradictory known/unknown predictions. Existing methods perform open-set reasoning independently for each proposal, while ignoring the shared semantics among correlated proposals, resulting in fragmented detections and unstable open-set decisions. In this paper, we reformulate OSOD as an object-centric semantic inference problem, where correlated proposals jointly support a coherent region-level semantic decision. Based on this perspective, we propose OCSI-Net, a unified framework that combines object-centric semantic consistency with geometry-aware uncertainty stabilization. Specifically, OCSI-Net aggregates correlated proposals into structured region clusters and infers region-level semantics from aggregated semantic evidence, thereby enforcing coherent open-set predictions within the same object region. Furthermore, a geometry-aware uncertainty mechanism is introduced to stabilize optimization near open-set transition boundaries. Extensive experiments on multiple benchmarks demonstrate that OCSI-Net consistently achieves state-of-the-art performance, improving the harmonic mean of known and unknown detection by an average of 6.83\% over the second-best method.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.27604

HiQGS: Hierarchical Spatiotemporal Querying for Feed-Forward Gaussian Reconstruction

Qiaowei Miao, Qifeng Cheng, Yijing Guo, Yuru Xiao, Ziqian Ni

原文摘要

Feed-forward Gaussian reconstruction from sparse posed monocular frames requires reasoning over observations that vary jointly in viewpoint and time. Existing methods either predict Gaussian primitives from observation-aligned features or aggregate multi-frame information through compact latent queries. However, the former tightly couples Gaussian allocation to individual frames, while the latter does not explicitly structure observed evidence according to its temporal relation to the requested timestamp. Consequently, evidence across spatial scales, viewpoints, and timestamps can be difficult to align and integrate. We present \textbf{HiQGS}, a query-based hierarchical framework for feed-forward Gaussian scene reconstruction from sparse monocular frames. HiQGS introduces a \textbf{Hierarchical Spatiotemporal Encoder} that organizes multi-resolution geometry-appearance visual features according to image location, observation time, and camera pose, thereby preserving complementary appearance and structural cues. Our \textbf{Time-Adaptive Decoder} use target timestamp queries the resulting hierarchical representation to retrieve relevant spatiotemporal evidence and predicts the corresponding Gaussian scene state. Experiments on static and dynamic scene reconstruction benchmarks demonstrate strong rendering quality and geometric accuracy, validating the effectiveness of hierarchical visual encoding and target-time-aware Gaussian querying.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02366

Do Visual RL Gains Scale at Test Time? A Quantile View of Post-Training

Austin Wang, Lexing Ying

原文摘要

Reinforcement learning (RL) post-training improves the single-sample quality of visual generative models, but deployment often provides additional inference compute through Best-of-N sampling. Whether RL gains transfer under such test-time scaling, particularly on difficult held-out tasks, remains poorly understood in the visual domain. Our central contribution is to systematically identify and characterize a failure mode of scaling transfer in visual RL: across multiple RL methods, model backbones, and evaluations spanning compositional generation, text rendering, and preference alignment, higher single-sample reward does not necessarily imply a better quality-compute frontier. RL gains may attenuate, saturate, or even reverse at large sampling budgets, with the base model eventually outperforming its post-trained counterpart. We explain these regimes through a quantile-transfer framework, showing that Best-of-N places increasing weight on the upper tail of the reward distribution as N grows. Consequently, RL can improve typical samples while compressing or degrading the rare high-quality outcomes that test-time scaling exploits. As a simple intervention motivated by this analysis, we introduce Quantile Utility Policy Optimization (QUPO), which optimizes a controllable mixture of quantile utilities corresponding to different inference budgets. Experiments on SD3.5-M and BAGEL show that QUPO better preserves upper-tail quality and substantially improves scaling on challenging held-out prompts relative to standard RL post-training. Our results suggest that visual generative models should be evaluated and optimized not only for single-sample quality, but for how post-training reshapes the full reward distribution that inference-time scaling exploits.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02364

Spectral-spatial vision test-time training for hyperspectral image classification

Xiang Zhao

原文摘要

Hyperspectral image (HSI) classification requires the representation of contiguous spectral signatures and spatial context under limited labeled supervision. Vision test-time training (TTT) offers an alternative to fixed-weight token mixing by performing self-supervised inner-model updates on the input sequence, but its application to center-pixel HSI classification remains insufficiently characterized. We present ViTTT-HSI, a spectral-spatial adaptation of the vision test-time training (ViT3) architecture. A spectral-aware embedding stem extracts ordered-band information using spectral convolution and forms spatial tokens without reducing their spatial resolution. Two ViT3-based TTT encoder blocks perform patch-conditioned token mixing, followed by the fusion of center-pixel and neighborhood-average features for classification. We evaluate the framework on Indian Pines, Pavia University, Salinas, and Houston 2013 using ten random seeds and dataset-specific training fractions of 1% or 5%. The mean overall accuracies are 97.6390%, 98.8753%, 99.3292%, and 98.4283%, respectively. Ablations reveal a substantial contribution from the TTT inner update on Indian Pines and a smaller positive contribution on Pavia University, while the relative benefits of the spectral stem and feature fusion depend on the dataset. The results demonstrate the feasibility of using patch-conditioned vision TTT for spectral--spatial HSI classification and highlight the role of the inner adaptation mechanism.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02363

GEAR: Generative Editing for Scene-Conditioned Outfit Retrieval for VR Travel

Gia-Huy Dinh, Thinh-Phuc Nguyen, Thanh-Hai Nguyen, Lam-Huy Nguyen-Ngoc, Hoang-Bach Nguyen-Phan, Nam-Quan Nguyen, Minh-Triet Tran, Trung-Nghia Le

原文摘要

In virtual environments, choosing appropriate outfits requires contextual harmony with surrounding scenes, yet bridging the visual gap between 3D ambient views and catalog garment assets remains challenging. This paper investigates scene-conditioned outfit retrieval for interactive 3D environments, where a user's egocentric view serves as the retrieval signal. We present GEAR (Generative Editing for Asset Retrieval), a framework that bridges the cross-domain gap by synthesizing intermediate visual queries rather than predicting direct compatibility. GEAR uses a vision-language model to describe suitable clothing from a captured scene, employs an instruction-guided diffusion model to render the concept onto a standardized reference character, and utilizes the cropped clothing region to retrieve existing 2D outfit assets via embedding similarity. An interactive Unity prototype demonstrates real-time scene capture, asset retrieval, and in-engine 3D avatar fitting. We benchmark GEAR against direct visual similarity, aesthetic scoring, and VLM-based reranking on a controlled dataset using automated Swiss-system pairwise judgments, where GEAR achieves competitive top-ranked performance (mean Elo@5 of 1012.75 and top-10 overlap of 0.180) alongside 0.539 pairwise agreement. Furthermore, a user study across 20 virtual travel scenes demonstrates that GEAR achieves the highest overall participant preference in both visual appeal (34.5%) and contextual suitability (34.5%).

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02341

AgiWorld: An Agentic Video World Model for Building Persistent Worlds

Shaoheng Fang, Yiqun Mei, Hanwen Jiang, Xin Sun, Haitian Zheng, Hao Tan, Kalyan Sunkavalli, Georgios Pavlakos 等 10 位作者

原文摘要

Recent interactive video world models generate visually compelling videos from a single image, following user-provided camera motion and text prompts. However, such low-level control demands substantial user effort to realize a high-level intent, and the generated world struggles to remain persistent as the exploration proceeds. In this work, we present AgiWorld, an agentic video world model that explores and builds a persistent world from a single image and a free-form instruction. For high-level planning, our agent AgiWorld-Agent designs the world and the journey through it in a world state and directs the exploration. For low-level realization, our video world model AgiWorld-WM generates the video chunk by chunk and stores it with its geometry in a spatial memory, from which the agent updates its world state. This closed loop keeps the world faithful to the user's intent and keeps the agent's world state consistent with the generated content throughout the exploration. We co-design the two levels around the shared spatial memory and an interface of metric camera trajectories and structured prompts. In AgiWorld-WM, to handle occlusion and accumulated geometric errors in the spatial memory over long rollouts, we propose occlusion-aware history selection, which retrieves past views by surface visibility, and reliability-based memory maintenance, which prefers reliable geometry and earlier observations. AgiWorld-WM achieves state-of-the-art overall performance on world revisiting and exploration benchmarks, and qualitative results and ablations show that AgiWorld builds persistent worlds from free-form instructions over minute-long journeys. Persistence thus arises not from the video model alone but from its closed loop with the agent.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02327

Findings-aware Language-Image Pretraining for CT Volume Interpretation

Takuro Shimaya, Junya Inoh, Masahiro Saiko

原文摘要

Computed tomography (CT) provides diverse patient information and various clinical tasks, for which numerous deep learning models have been investigated. Recent years have seen the development of vision-language foundation models that can comprehensively extract clinical features, driven by representation learning that combines CT volumes with radiology reports. Existing approaches typically adopt Contrastive Language-Image Pre-training (CLIP)-based representation learning, where pairs from different cases are, in principle, treated as negative pairs such that the volume and report embeddings are pushed apart. In the biomedical imaging, however, treating different cases as negative pairs is not always appropriate, particularly when clinical similarity exists. In this study, we propose a supervised representation learning framework grounded in clinical knowledge from language foundation models, rather than the weak supervision used in CLIP. Specifically, we utilized a large language model to extract abnormal finding phrases from reports. We then explicitly constructed the target features that the image encoder should output, as the semantic representation of the extracted findings using a biomedical text embedding model. Our Findings-aware Language-Image Pre-training (FiLIP) outperformed weakly supervised approaches in multi-label zero-shot finding classification in both in-domain and external-domain evaluations, and also proved effective when fine-tuned for the downstream task of radiology report generation.

cs.CV · physics.med-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02324

The DynaCell Evaluation Suite for Dynamic 3D Virtual Staining of Live Cells

Alexandr A. Kalinin, Dihan Zheng, Taylla Milena Theodoro, Ivan Ivanov, Eduardo Hirata-Miyasaki, See-Chi Lee, Aofei Liu, Sricharan Reddy Varra 等 15 位作者

原文摘要

Understanding how cells respond to perturbations over time is central to cell biology and drug discovery. Live-cell imaging captures the dynamics of perturbation-induced phenotypes in individual cells. While fluorescence imaging can visualize specific molecular phenotypes, labeling and repeated illumination often affect cell health. Virtual staining offers an alternative by predicting these fluorescence images from images acquired without fluorescent labels. Although many virtual staining models have been developed, their performance on volumetric time-lapse data remains poorly characterized. We address this gap with an evaluation suite for 3D time-lapse virtual staining of live cells. The suite combines paired 3D time-lapse images acquired with and without fluorescent labels, a set of baseline models, and three metric tiers measuring pixel-level fidelity, organelle segmentation performance, and single-cell phenotype similarity. Using this evaluation suite, we study metric and model behavior across 3 cell types, 2 microscopes, 4 organelles, and 3 perturbation states. We find that regression baselines better predict the spatial localization of target organelles, whereas the generative baseline better captures the distribution of phenotypes across cells. These differences expose trade-offs between structural and phenotypic fidelity that are obscured by single-metric evaluation and suggest how virtual staining models can be developed for diverse downstream tasks such as localization, segmentation, tracking, and phenotypic profiling. We release the curated datasets, baseline checkpoints, and evaluation code as part of DynaCell, a broader framework for image-based profiling of cell-state dynamics.

cs.CV · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02306

Lie Routing Vision Language Models for View-Robust 3D Understanding

Jingjun Yi, Chen Hu, Qi Bi, Hao Zheng, Huimin Huang, Yongkang, Wei Ji, Yawen Huang 等 10 位作者

原文摘要

Multimodal Large Language Models (MLLMs) have made substantial progress in 3D scene understanding from multi-view RGB observations. Yet a fixed object and query can elicit inconsistent target-related responses across camera viewpoints, a phenomenon we characterize as viewpoint-induced semantic drift. We introduce LieVLM, a Lie routing framework for view-robust 3D understanding. Motivated by the rotational component of viewpoint change, Lie routing adapts visual representations by applying local $\mathrm{SO}(3)$ transformations to projected features within learned three-dimensional subspaces. Geometry tokens extracted by VGGT predict local rotation parameters and channel-wise routing coefficients. A closed-form Padé--Cayley map transforms projected visual features, whose identity-relative corrections are projected back and gated into the original visual and geometry features. This design retains the pretrained semantic and direct spatial context while learning view-dependent feature adjustments. Experiments on 3D captioning, grounding, detection, and spatial reasoning show consistent improvements over additive fusion across training scales and backbones. Controlled ablations examine the roles of geometric conditioning and transformation structure. Viewpoint analyses further reveal systematic variation in the predicted routing states alongside more localized target responses and stronger task performance across viewpoint changes.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02305

Peripheral prototypical contrastive loss for supervised medical image segmentation

Takuro Shimaya, Ryosaku Shino, Masahiro Saiko

原文摘要

Robust segmentation of biomedical images remains challenging primarily due to ill-defined object boundaries. Although several loss functions have recently been proposed to enhance object boundary recognition, concerns remain about the susceptibility to unstable annotations resulting from boundary ambiguities. Here, we introduce the Peripheral Prototypical Contrastive (PPC) loss, which leverages prototypical contrastive learning to improve boundary recognition and enable stable training even under noisy annotation conditions. The PPC loss utilizes prototype features obtained from local regions near object boundaries, focusing specifically on ambiguous areas that have been challenging for existing approaches. Extensive experiments across multiple datasets demonstrated that the PPC loss consistently improve segmentation performance even under conditions with noisy labels. A newly proposed strategy, which adaptively determines the local region for prototype calculation, enhanced robustness and outperformed existing methods. Our findings indicate the potential of the PPC loss in facilitating scalable and reliable medical image segmentation, including the development of foundation models with imperfect annotations.

cs.CV · physics.med-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02284

SVD-Prune: Spectral Vision Token Pruning under Extreme Compression

Yvon Apedo, Martyna Poreba, Michal Szczepanski, Samia Bouchafa

原文摘要

Vision-Language Models (VLMs) and Vision-Language-Action (VLA) models process hundreds of visual tokens, making visual representations a major computational bottleneck. We argue that visual token pruning should be formulated not as identifying individually important tokens, but as preserving the dominant low-rank spectral structure of the visual representation. Based on this formulation, we introduce SVD-Prune, a training-free framework that leverages Singular Value Decomposition (SVD) and statistical leverage scores for globally informed token selection. SVD-Prune is architecture-agnostic and can be applied at different stages of the multimodal pipeline, including encoder-side, decoder-side, and hybrid configurations Extensive experiments across VLM and VLA benchmarks demonstrate strong robustness under extreme compression. SVD-Prune reduces the visual token. SVD-Prune cuts the visual token budget by up to 99.3%, while, at a 16-token budget, encoder-side pruning lowers inference FLOPs by 82.4% and achieves a 4.18x end-to-end speedup, including pruning overhead.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.30621

Cost-efficient Active Learning for Referring Image Segmentation and Grounding

Junbeom Hong, Seonghoon Yu, Hyung Rok Jung, Sundong Kim, Jeany Son

原文摘要

Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (VG), as annotators must write descriptions that distinguish target regions from visually similar ones. We tackle this by formulating active learning (AL) for VG under the realistic setting where only raw images are available without accompanying text. Since ground-truth text is unavailable, sample selection must estimate which images contain ambiguous regions that would require discriminative referring expressions. To address this, we generate auxiliary region-text pairs using foundation models, and introduce Referred Region Ambiguity, a new acquisition function that measures whether the model's confidence collapses onto a single region or disperses across multiple candidates. It allows our method to prioritize images with strong cross-region competition, which are more informative due to their visual ambiguity. We also design a referring-expression annotation interface that helps annotators quickly focus on writing discriminative language with a few clicks. Experiments on RIS and REC benchmarks show that our AL framework consistently outperforms several AL baselines, while a user study shows up to 1.6X faster description labeling of ours.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.26638

RigidDriveGS: Object-Centric Vehicle Reconstruction from Fixed Multi-Camera Drive-Through Captures

Nitin Kulkarni, Akhil Devarashetti, Charlie Cluss, Livio Forte, Philip Schneider, Chunming Qiao, Alina Vereshchaka

原文摘要

High-fidelity 3D reconstruction of vehicle exteriors improves buyer confidence in online automotive marketplaces, but fixed multi-camera drive-through capture departs from standard Structure-from-Motion (SfM): static cameras observe a moving vehicle amid clutter, rotating wheels, fisheye distortion, and specular reflections. We present RigidDriveGS, an object-centric pipeline combining motion-gated tracking, bifurcated masks that exclude wheels for SfM while optionally retaining them for rendering, mask-guided dense correspondences on raw distorted images, rig-constrained global SfM, and clean object-centric neural rendering with trimap-aware opacity or density supervision and native-alpha white compositing. We evaluate 25 vehicles from 10 dealership installations and six phone-recorded vehicles, each captured from 14 independent handheld positions. Under a staggered per-camera temporal-block holdout, 3DGUT+MCMC achieves 30.42 dB PSNR, 0.90 SSIM, and 0.20 LPIPS, improving PSNR by 7.78 dB over undistorted 3D-GS; independently recorded reference LiDAR scans yield full/body Chamfer-L1 of 0.026/0.013 m and F1@5 cm of 0.703/0.885. These results support clutter-free, object-centric novel-view synthesis without controlled studio infrastructure.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.02074

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges

Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma

原文摘要

Crash detection from video feeds is a critical problem in intelligent transportation systems. Recent developments in large language models (LLMs) and vision-language models (VLMs) have transformed how we process, reason about, and summarize multimodal information. This paper surveys recent methods leveraging LLMs for crash detection from video data. We present a structured taxonomy of fusion strategies, summarize key datasets, analyze model architectures, compare performance benchmarks, and discuss ongoing challenges and opportunities. Our review provides a foundation for future research in this fast-growing intersection of video understanding and foundation models.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02245

Modeling Visual Cortex with Layerwise Multiscale Manifold Capacity Maximization

Thomas Yerxa, SueYeon Chung, Eero P. Simoncelli

原文摘要

Task-optimized deep neural networks have risen to prominence as the most predictive phenomenological models of responses in primate visual cortex, but leave much to be desired from the perspective of biological plausibility. One such limitation is the reliance on precise credit assignment through global backpropagation of error signals. Recent work has shown that this weakness can be circumvented by requiring each subsequent stage to solve a distinct and increasingly complex task, allowing for layerwise local learning signals. We propose a novel strategy for crafting such intermediate losses that uses an efficient coding framework formulated in terms of manifold capacity, and which can be computed using a sequence of {canonical cortical computations}. In particular, we leverage the relationship between the multiscale nature of visual signals and the dilation of receptive field sizes in cascaded visual representations to modulate complexity, allowing for the reapplication of these common loss computations at each stage of the hierarchy. We evaluate our approach on its ability to predict neural datasets spanning three areas of the ventral stream hierarchy in macaques, as well as human psychophysical data on an object classification task. We find that our unsupervised layerwise model matches or exceeds the performance of competitive architecture-matched baselines on all evaluations considered.

q-bio.NC · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.19060

Efficient Factorized Volume Synthesis with Lifted Inter-Slice Feature Trajectories

Xinhe Zhang, Yuyang Zhang, Pengfei Jin, Arnau Marin-Llobet, Na Li, Quanzheng Li

原文摘要

High-resolution 3D medical image generation remains challenging because fully volumetric models are computationally expensive, while efficient 2D slice generators often fail to preserve anatomical consistency across the third dimension. We propose LiFT, a framework for Lifted inter-slice Feature Trajectories that factorizes 3D volume synthesis into per-slice image generation and inter-slice trajectory learning. Rather than using a fully volumetric synthesis backbone, LiFT treats a volume as an ordered trajectory in feature space, capturing how anatomical structures appear, transform, and disappear across depth. We demonstrate the LiFT framework in both unconditional and conditional settings. In unconditional generation, a tri-planar drifting loss matches feature distributions between generated and real volumes across three orthogonal planes; in paired translation, a bidirectional $z$-context mixer trained with paired target supervision supplies through-plane coherence while preserving per-slice fidelity. We evaluate LiFT on BraTS 2023 for unconditional and missing-modality MR synthesis, and on SynthRAD2023 for MR-to-CT translation. LiFT achieves competitive image quality while retaining the efficiency of predominantly 2D synthesis, operating up to $390\times$ faster than a volumetric diffusion baseline and consistently improving through-plane coherence through explicit inter-slice modeling. These results establish lightweight inter-slice trajectory learning as an efficient approach to high-resolution 3D medical synthesis.

cs.CV · cs.AI · eess.IV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02211

Forecasting Earth Surface Change in Embedding Space

Jingtao Li, Sadiq Jaffer, Anil Madhavapeddy, Pedro Sousa, Anthony Ortiz, Kyle Gao, Zhengpeng Feng, Yanfei Zhong 等 10 位作者

原文摘要

Repeated satellite imagery permits understanding of how our planet changes over time, letting us predict its future and plan mitigating actions. Most existing studies that rely on a time series of satellite observations forecast the spectral values at each pixel for a future target date. However, clouds, shadows, and sensor noise often corrupt these observations, so that spectral forecasters waste much of their capacity on low-level features or noise rather than real change. With the advent of Earth foundation models, we argue that forecasting in embedding space is a feasible and more effective alternative. To this end, we design a token space that handles irregular histories from different sensors and foundation models and supports forecasts at any query date. Tokens from this space are inputs to our proposed forecaster, E$^2$Forecast, which is a transformer with two branches: a Gaussian branch that directly predicts a mean point forecast and per-dimension standard deviations, and a rectified-flow branch that generates forecast ensembles. On Sentinel-1 and Sentinel-2 benchmarks, forecasting embeddings instead of spectral values halves the cosine distance (from 0.20 to 0.10) and greatly reduces overconfidence, lowering the z-score standard deviation from 2.63 to 1.22. By comparing new observations with its forecasts, E$^2$Forecast can also detect anomalous changes without labels or retraining, raising F$_1$ from 41.58 to 48.70 on a global benchmark. This consistent superiority is validated on nine remote sensing foundation models. Beyond change detection, we believe our approach advances the field by permitting effective reuse of diverse pretrained task heads. Our code is publicly available at https://github.com/ucam-eo/e2forecast.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.28449

Establishing the Minimal Clinically Important Difference (MCID) for Smartphone-Derived Gait Measures in Multiple Sclerosis

Mike D Rinderknecht, Bernhard Fehlmann, Dimitar Stanev, Cedric Simillion, Ernst Bos, Letizia Leocani, Agne Kazlauskaite, Gary Cutter 等 10 位作者

原文摘要

Background: Digital health technologies allow for frequent, remote gait monitoring in people with multiple sclerosis (MS). However, to differentiate daily variability from actual disease progression in longitudinal data, established minimal clinically important differences (MCID) are required. Currently, there is limited literature defining these thresholds for digital gait metrics. Objective: To establish MCIDs for digital gait measures reflecting progression in MS. Methods: Digital gait measures were captured via daily, remote, smartphone-based Two-Minute Walk Tests in CONSONANCE (NCT03523858), a phase 3b study of ocrelizumab in progressive MS. Using an anchor-based approach, median changes from baseline at Week 96 on digital gait measures were computed for patients showing clinically meaningful worsening on either Timed 25-Foot Walk, Ambulation Score, Expanded Disability Status Scale, or 12-item Multiple Sclerosis Walking Scale. These changes were subsequently triangulated to derive the MCID estimates. Results: 243 patients with progressive MS (female: n=125 (51%); mean [SD] age: 49.3 [9.3]; mean [SD] EDSS: 4.8 [1.4]) had digital gait data available at baseline and Week 96. Median changes were generally consistent across anchors. Triangulated MCIDs are: Step Velocity = -0.16 m/s, Step Velocity Scaled to Walking Time = -0.18 m/s, Step Duration = 0.06 s, Step Length = -0.07 m, Total Number of Steps = -28, and Total Distance Walked = -24 m. Conclusion: These MCIDs provide a framework for interpreting meaningful gait changes and integrating digital measures into MS outcome evaluation. Beyond facilitating novel clinical trial endpoints to evaluate treatment efficacy, they enable objective, real-world monitoring to advance personalized patient care.

q-bio.QM · cs.CV · physics.med-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02204

Learning from high-resolution 2D images to reconstruct 3D microstructures

Wenbo Sui, Lars P. Mikkelsen, Jens W. Andreasen, Daniel Lichau, Harold Phelippeau, Philip J. Withers

原文摘要

High-resolution three-dimensional (3D) microstructure characterization remains difficult to undertake at scale because volumetric imaging at the required spatial resolution is expensive, time-consuming or destructive. In many settings, however, fine-resolution two-dimensional (2D) imaging and lower-resolution volumetric measurements are both readily available. We introduce a Privileged Cross-modal Relational Distillation (PCRD) framework that leverages fine-resolution 2D observations during training to improve specimen-specific 3D reconstruction from lower-resolution volumetric imaging alone. We demonstrate PCRD on pultruded carbon fiber-reinforced polymer composites, using scanning electron microscopy as the privileged modality and micro-CT as the inference input. The framework transfers spatial organization by matching similarity distributions defined around corresponding spatial anchors in the teacher and student representations, avoiding direct alignment of heterogeneous feature vectors. In 2D cross-sectional reconstruction, PCRD improves mAP by 1.66 percentage points over direct micro-CT training. Independent 3D validation against high-resolution synchrotron X-ray CT data shows close agreement in fiber volume fraction, diameter and local spacing. The PCRD reconstruction matches 89.97% of the reference fiber centerline length, compared with 75.08% for the interpolated low-resolution volume.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02203

Depth Anyviewpoint? Evaluating Monocular Depth Over Viewpoint Sequences

Theo Rode, Rui Zhang, Ryan Aubrey, Olivia Li, Jessica Huang, Liv Chu, Healani Dowd, Calden Wloka

原文摘要

Monocular depth estimation aims to predict depth maps from single RGB images (forgoing the need for stereo camera pairs) and has seen impressive leaps in performance in recent work due to the introduction of affine-invariant losses and the use of foundation model features. These monocular depth models predict affine-invariant depth, which describes the relative positions of objects in the scene instead of absolute depth. While these models demonstrate strong performance on single image benchmarks and qualitatively consistent predictions in video, their effectiveness at preserving consistent spatial structure has been less thoroughly explored. Many potential real-world applications, such as handling video streams as input, rely on coherent three dimensional consistency in scenes. Further, anchoring relative depth predictions to a metric reference for a given scene is a key challenge for applying monocular depth models to real-world situations. We develop a novel evaluation protocol for monocular depth models using video sequences with moving camera positions to test the consistency of prediction across variation in viewpoint. We demonstrate this methodology over diverse, real-world video sequences where we are able to anchor depth model output to metric depth values using structure from motion. Our work demonstrates open challenges in the development of broadly applicable monocular depth foundation models, particularly with their ability to provide temporal consistency over long time frames. We also identify challenges in the straightforward adoption of structure-from-motion as a metric grounding tool for monocular depth estimation models.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13235

Pose accuracy is not grasp reliability: residual pose posteriors improve simulated robotic grasp planning

Md Selim Sarowar, Sungho Kim

原文摘要

Model-based robotic grasping estimates an object's six-dimensional (6D) pose and plans on its mesh as if that pose were exact. We show that this assumption reduces simulated grasp success even when the pose passes the standard accuracy test. We built LM-Grasp, a benchmark of 15{,}800 real LINEMOD RGB-D images with grasp outcomes simulated on scanned meshes. FoundationPose meets the ADD(-S) criterion (mean model-point error below 10\% of the object diameter) on 0.997 of test scenes, yet planning on its estimate succeeds in 0.791 of scenes, compared with 0.856 using the annotated pose. Because the estimator reports no uncertainty, we build a pose posterior from its own errors on annotated training frames, resampled from frames with similar observable fit, and score each grasp by its mean success across sampled poses and four equally weighted physics scenarios. Without retraining the estimator, posterior planning yields 0.859 (difference from the annotated-pose result +0.003; 95\% scene-wise paired-bootstrap interval [$-$0.003, +0.009]). This interval is compatible with no difference and a small shortfall; we did not conduct an equivalence test. For pose errors of 5 to 10\% of the diameter, simulated success rises from 0.796 to 0.984. Among test scenes assigned singleton-success conformal prediction sets, observed held-out precision is 0.972, compared with 0.904 for the point-estimate planner; these are empirical precision estimates, not conditional guarantees. On occluded LM-O scenes never used for tuning, posterior planning closes 66\% of the gap to the annotated pose.

cs.RO · cs.CV

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新论文 · v1arXiv:2611.02147

Refining Roadside 4D Radar Vehicle Classification Using Point-Cloud Dispersion

Mohammed Alsakabi, Andreia Figueiredo, António Almeida, John Dolan, Miguel Luís, Pedro Rito, Ozan Tonguz, Susana Sargento

原文摘要

Roadside sensing infrastructure is essential for Intelligent Transportation Systems (ITS), but cameras and legacy sensors are limited in range, weather robustness, and depth accuracy. 4D radar complements these modalities by resolving range, azimuth, elevation, and radial velocity under adverse conditions, though its sparse, noisy point clouds make reliable classification difficult. We present a roadside multi-sensor platform combining a 4D radar and stereo depth camera under synchronized acquisition, used to collect a real-world dataset. Our analysis reveals a substantial class-distribution discrepancy between modalities, most notably a strong radar confusion between passenger cars and motorcycles (49.01% and\ 48.60% of the dataset). To address this, we propose a Neyman-Pearson-based binary classifier on the temporally-smoothed 2D positional standard deviation of radar point cloud clusters, reducing classification error from 52.1% to 42.7%. This lightweight and edge-deployable approach improves radar-only classification reliability and lays the foundation for future camera-radar fusion.

cs.CV

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新论文 · v1arXiv:2611.02114

CNN-Based Retinal OCT Classification: Research Gap Analysis and a From-Scratch Baseline, ED126-A

Shkelqim Sherifi

原文摘要

Automated classification of retinal pathologies from Optical Coherence Tomography (OCT) images remains challenging because diagnostically relevant morphological patterns may exhibit substantial inter-class similarity. Although high classification accuracies have been reported on benchmark OCT datasets, cross-study comparison is limited by heterogeneous evaluation protocols and by the limited characterization of compact convolutional neural network (CNN) baselines trained from random initialization. This study addresses this gap through two contributions. First, it provides a focused review of CNN-based retinal OCT classification and identifies methodological considerations relevant to controlled benchmarking. Second, it presents ED126-A, a purpose-built four-class CNN baseline trained from scratch under a documented experimental protocol on the Kermany labeled OCT dataset. The model contains 1,241,380 total parameters, including 1,239,460 trainable parameters, and is evaluated using training-dynamics analysis, class-wise performance metrics, confusion-matrix analysis, confidence analysis, t-SNE representation-space visualization, Grad-CAM, hierarchical feature-map analysis, and one-vs-rest ROC/Precision-Recall analysis. On the image-level held-out test set, ED126-A achieved 95.03% accuracy, with a macro-F1 of 0.9205 and a weighted-F1 of 0.9521. The principal class-conditional limitation occurred for Drusen, which attained a recall of 0.9605 but a precision of 0.7493 and an F1-score of 0.8419. The confusion matrix identified Normal to Drusen and CNV to Drusen as major error channels. These results support the use of ED126-A as a documented from-scratch reference baseline and provide an empirical basis for controlled evaluation of transfer learning, increased depth, attention, multi-scale feature fusion, and calibration in subsequent studies.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.05268

Is the Geometry Doing the Work? An Operating-Point Audit of Hierarchy in Hyperbolic Vision-Language Models

Jaeyoung Kim, EunSeok Kim, Dongsuk Jang

原文摘要

Hyperbolic vision-language models are designed to encode abstraction geometrically: general concepts near the origin, specific ones farther out, and entailment cones representing directed order. We ask whether trained MERU, HyCoCLIP, and PHyCLIP models actually use these mechanisms. We audit seven released checkpoints and matched from-scratch interventions. All audited converged checkpoints remain near-Euclidean in the dimensionless radius $u=\sqrt{c}ρ$: the largest observed image-side value is $0.37$ -- well below $u\approx0.84$, where local metric distortion reaches $10\%$. Releasing the curvature floor changes curvature and norms but not this regime, with mixed, generally modest downstream shifts. Trained entailment cones are saturated or nearly saturated, so low violation rates can arise from trivially wide cones rather than learned order. Preregistered semantic traversal detects weak within-branch order but no operative full-hierarchy readout. Shuffle-controlled tests detect no pair-specific radial ordering in released checkpoints, and no positive result is consistent across all three matched ViT-B seeds. This non-detection is sensitivity-bounded: the diagnostic reaches $80\%$ power only at $\approx13.5$ pp of shuffle-controlled excess, while the largest released gap is $\approx5$ pp. We trace this to a low-curvature shortcut: lowering curvature widens entailment cones and suppresses violations without learning order. In the probed trajectories, gradient decomposition identifies entailment as the dominant curvature-lowering pressure during collapse. Yet curvature contracts even when entailment is removed, so the shortcut is not the sole cause. Under our diagnostics, the audited formulations do not demonstrate an operative radial or cone-based hierarchy. We distill the audit into a five-number geometry report for evaluating future hierarchy claims.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.00557

NeuroVolve: Evolving personalized visual stimuli toward user-defined brain response targets

Haomiao Chen, Keith W Jamison, Daniel Chong, Yunrui Zhang, Mert R. Sabuncu, Amy Kuceyeski

原文摘要

A central question in neuroscience is what visual features drive brain responses across regions. Classic approaches rely on handcrafted stimuli to test specific hypotheses, limiting discovery to features defined before experiments. Here we introduce NeuroVolve, a data-driven generative framework for synthesizing images that achieve flexible, user-defined patterns of brain activation in individuals. NeuroVolve recovers established selectivity across visual cortical regions and, paired with subject-specific brain encoding models, generates personalized stimuli reflecting individual differences in neural preference. Beyond single-region selectivity, NeuroVolve uncovers multi-region response selectivity, characterizing how regions jointly represent visual content during naturalistic perception. We further demonstrate generalization beyond visual cortex: in a prospective fMRI experiment, NeuroVolve-generated stimuli targeting the amygdala, a classically non-sensory region, drove measured activation consistent with predictions in novel participants. Together, these results establish NeuroVolve as a less constrained, data-driven tool for probing individual-level neural selectivity across the brain.

cs.CV · q-bio.NC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02101

When Delay Meets Drift: Physical Safety of Robot Foundation Models Is a Cross-Layer Problem

Liangkai Liu, Bimal Thapa Magar

原文摘要

Robot foundation models now act in a closed loop, yet their safety is evaluated one variation at a time: robustness benchmarks perturb the scene while assuming instantaneous inference, and real-time execution methods study inference delay in nominal scenes. We argue that physical safety is not a property of the model alone, but emerges from how variations in the physical environment, the model, the computing platform, and execution interact in closed loop. We formalize this as cross-layer variation propagation across these system layers and use an interaction contrast to measure when joint variation creates more risk than the sum of its parts. In 4,000 paired closed-loop episodes of the pi0.5 vision-language-action model on LIBERO, a 400 ms inference delay raises the safety-violation rate by 3.5 percentage points under the training viewpoint but by 37 percentage points under a 60-degree camera shift: 33.5 percentage points (95% CI: 25-43) more than the two effects combined. We close with a research agenda toward predictable physical intelligence: predicting when variation becomes risk, and adapting compute, power, and control accordingly.

cs.RO · cs.CV · cs.ET

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新论文 · v1arXiv:2611.02096

EarthJev: Calibrated, Non-Generative Answers to Natural-Language Questions about Sentinel-2 Land Cover

Domenico Benfenati, Antonio Maria Rinaldi, Cristian Tommasino

原文摘要

Vision-language models for remote sensing answer questions about satellite images by generating text: every answer costs a decoding loop, has to be parsed back to an option, and comes without a usable probability. We present EarthJev, a 0.8B-parameter model that answers typed questions about multispectral Sentinel-2 patches without generating any token. All 12 bands enter the vision encoder of Qwen3.5-0.8B through a widened input layer initialised from the RGB kernels, and, following the open OneJev recipe, the answer is read from the logits of the option letters at a single position and trained with cross-entropy plus Brier score. Under a pre-specified analysis plan, we compare it with the same backbone fine-tuned to generate the answer and with three multimodal language models fine-tuned with LoRA, all trained on the same 70,890 questions from 5% of BigEarthNet v2.0 and timed in one job on the same GPU; VQA models that classify over a fixed answer vocabulary are reported as a reference for the cost of answering only the questions seen in training. EarthJev answers a question in 19.9 ms, 4.3 to 8.7 times faster than the multimodal language models and 6.8 times faster than its own backbone used generatively; when all the questions about an image are asked together, it answers them in 42.8 ms, 3.7 to 10.1 times faster than the batched generative models. On 36,028 test questions it reaches 86.0% accuracy (three seeds), 0.6 and 1.2 points below the two 2B multimodal models and 3.5 points below the remote-sensing EarthDial-4B, with answers that are valid by construction and calibrated probabilities (ECE 0.006). Replacing the nine non-RGB bands with their mean costs 4.5 points, so the model uses the multispectral input, and in an ablation training the whole vision encoder raises accuracy to 88.4% at the same cost. Code and model: https://github.com/CosmoIknosLab/EarthJev

cs.CV · cs.AI

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版本更新 · v3arXiv:2512.17541

FLEG: Feed-Forward Language Embedded Gaussian Splatting from Any Views via Compact Semantic Representation

Qijian Tian, Jiayu Ying, Xuhong Wang, Yuan Xie, Lizhuang Ma, Xin Tan

原文摘要

We present FLEG, a feed-forward network that reconstructs language-embedded 3D Gaussians from multi-view images. Previous feed-forward language-embedded Gaussian reconstruction methods are restricted to a fixed number of input views and typically attach a language-aligned semantic embedding to each Gaussian, resulting in impractical input settings and semantic redundancy. In contrast, we introduce a geometry-semantic dual-branch distillation framework that supports a variable number of uncalibrated and unposed multi-view images as input. We also propose a novel-view-based distillation strategy during training that mitigates overfitting to input views. In addition, we observe that semantic representations are significantly sparser than geometric ones, and per-Gaussian language embedding is unnecessary. To exploit this sparsity, we design a decoupled language embedding strategy that represents language information with a sparse set of semantic Gaussians, rather than attaching embeddings to every Gaussian. Compared with dense pixel-aligned per-Gaussian embedding schemes, our method uses only 5% of the language embeddings while maintaining comparable semantic fidelity, effectively reducing storage costs. Extensive experiments demonstrate that FLEG outperforms state-of-the-art feed-forward reconstruction and language-embedded Gaussian methods in both reconstruction quality and language-aligned semantic representation.

cs.CV

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版本更新 · v2arXiv:2607.08839

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing

Dominick Reilly, Qiyu Wu, Hiromi Wakaki, Srijan Das, Yuki Mitsufuji

原文摘要

Multimodal Large Language Models (MLLMs) are typically designed under the assumption that all modalities available during training will also be accessible at inference. However, many real-world settings violate this assumption, requiring models to operate under a privileged modality setting, where auxiliary modalities are available only during training. While these modalities contain valuable information, existing MLLMs largely fail to leverage them effectively, as they treat modalities as interchangeable inputs rather than sources of complementary supervision. We propose Mixture of Probes (MoP), a novel framework that disentangles modality-specific and modality-general signals within the MLLM, allowing the model to preserve modality-dependent structure while learning transferable representations across modalities. At its core, MoP achieves this through a structured probing mechanism that extracts and organizes information from intermediate representations of a shared modality encoder, rather than relying only on final-layer alignment as done in existing MLLMs. To support this disentanglement, we further introduce MoP Cross-modal Training (MoP-X), a training strategy for MoP centered around a probe disentanglement loss that prevents probe collapse and encourages cross-modal learning. We evaluate MoP across two domains spanning eight tasks and four modalities under a comprehensive evaluation protocol tailored to the privileged modality setting, where each modality is independently treated as the sole input at inference time. MoP consistently outperforms strong MLLM baselines, achieving up to 65% relative improvement, demonstrating that auxiliary modalities, even when unavailable at inference, can provide substantial gains when effectively leveraged during training. Code, model checkpoints, and evaluation protocols will be made available at https://github.com/Sony/MoP.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02076

Scene-Adaptive Readout and Decoding in Diffusion Driving VLAs

Seungjun Yu, Jiho Choi, Inha Kang, Hojun Choi, Seungwook Kim, Hyunjung Shim

原文摘要

Diffusion driving vision-language-action models repeatedly execute the backbone at every denoising stage, incurring substantial inference cost, while it remains unclear whether all scenes require the same decoding computation. To distinguish when a trajectory becomes readable from how much further refinement it requires, we analyze trajectory readout across network depth and decoding progress. We identify a depth-decoding dissociation in which late-layer representations already support useful trajectory readout after the first forward sweep, whereas early-layer representations benefit more from continued decoding. We further find that both the preferred readout layer and decoding depth vary across scenes. Based on these observations, we propose a scene-adaptive readout and decoding framework. A scene-conditioned readout MoE adaptively weights and combines trajectory predictions from layer-specific late-layer representations after a single backbone sweep, while an adaptive decoding policy applies additional native refinement only when required by the scene. We evaluate our framework on two diffusion driving VLAs across the Waymo E2E and nuScenes benchmarks. Our method improves validation RFS by up to 0.56 while also improving test performance, and achieves an end-to-end speedup of up to $6.0\times$ on Waymo E2E, while consistently reducing trajectory error on nuScenes. These results demonstrate that scene-level readout and decoding allocation improves planning quality while avoiding unnecessary decoding. Our code is available at https://github.com/sjyu001/SARD

cs.CV · cs.AI · cs.RO

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