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Friday 24 April

Computer Vision

Text-Guided Multimodal Unified Industrial Anomaly Detection

Zewen Li, Shuo Ye, Zitong Yu et al. (+2 more)

Industrial anomaly detection based on RGB-3D multimodal data has emerged as a mainstream paradigm for intelligent quality inspection. However, existing unsupervised methods suffer from two critical limitations: ambiguous cross-modal alignment caused by the lack of high-level semantic guidance and insufficient geometric modeling for RGB-to-3D feature mapping. To address these issues, we propose a unified multimodal industrial anomaly detection framework guided by text semantics. The framework consists of two core modules: a Geometry-Aware Cross-Modal Mapper to preserve geometric structure during modality conversion, and an Object-Conditioned Textual Feature Adaptor to align multimodal features with semantic priors. Furthermore, we establish a unified learning paradigm for multimodal industrial anomaly detection, which breaks the one-model-one-class constraint and enables accurate anomaly detection across diverse classes using a single model. Extensive experiments on the MVTec 3D-AD and Eyecandies datasets demonstrate that our method achieves state-of-the-art performance in classification and localization under unsupervised settings.
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All authors Zewen Li, Shuo Ye, Zitong Yu, Weicheng Xie, Linlin Shen

Computer Vision · Cross-listed from eess.IV

Generalizable CT-Free PET Attenuation and Scatter Correction for Pediatric Patients

Jia-Mian Wu, Jun Liu, Siqi Li et al. (+7 more)

Computed tomography (CT)-based attenuation and scatter correction improves quantitative PET but adds radiation exposure that is particularly undesirable in pediatric imaging. Existing CT-free methods are commonly trained in homogeneous settings and often degrade under scanner or radiotracer shifts, which limits their clinical utility. We propose the Generalizable PET Correction Network (GPCN), a dual-domain network for domain-robust CT-free PET attenuation and scatter correction. GPCN combines a multi-band contextual refinement module, which models pediatric anatomical variability through wavelet-based multiscale decomposition and long-range spatial context modeling, with a frequency-aware spectral decoupling module, which performs coordinate-conditioned amplitude/phase refinement in the Fourier domain. By synergizing multi-band spatial contextual modeling with asymmetric frequency-spectrum decoupling, the network explicitly separates invariant topological structures from domain-specific noise, thereby achieving precise quantitative recovery of both anatomical organs and focal lesions. This design aims to separate anatomy-dominant structures from domain-sensitive spectral residuals and to improve robustness across heterogeneous imaging conditions. We train and evaluate the method on 1085 pediatric whole-body PET scans acquired with two scanners and five radiotracers. In both joint training and zero-shot cross-domain evaluation, GPCN outperforms representative baselines and maintains stable quantitative accuracy on unseen scanner-tracer combinations. The method is further supported by ablation, region-wise quantitative analysis, and downstream segmentation experiments. In our cohort, the CT component of the conventional protocol corresponded to an average effective dose of 10.8 mSv, indicating the potential clinical value of reliable CT-free correction for pediatric PET.
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All authors Jia-Mian Wu, Jun Liu, Siqi Li, Xiaoya Wang, Shibai Yin, Huanyu Luo, Lingling Zheng, Qiang Gao, Jigang Yang, Tai-Xiang Jiang

Computer Vision

Breaking Degradation Coupling: A Structural Entropy Guided Decoupled Framework and Benchmark for Infrared Enhancement

Pu Li, Huafeng Li, Yafei Zhang et al. (+2 more)

Thermal infrared image enhancement aims to restore high-quality images from complex compound degradations. Existing all-in-one approaches typically employ a single shared backbone to handle diverse degradations, which causes gradient interference and parameter competition. To address this, we propose a Structural Entropy-Guided Decoupled (SEGD) Framework. Unlike unified modeling paradigms, SEGD decomposes compound degradations into independent sub-processes and models them in a divide-and-conquer manner through Degradation-Specific Residual Modules (DRMs). Each DRM focuses on residual estimation for a specific degradation, enabling task decoupling while remaining jointly trainable, which mitigates parameter contention. A Degradation-Aware Evidential Network further estimates degradation type and intensity, providing priors that adaptively regulate DRM restoration strength. To handle compound cases, DRMs are composed in varying orders to form multiple restoration paths, from which the most informative features are aggregated under a structural-entropy criterion, yielding decoder-ready representations with structural fidelity and degradation awareness. Integrating divide-and-conquer restoration, evidential perception, and entropy-guided adaptation, SEGD achieves fine-grained and interpretable enhancement. We also construct a nighttime TIR benchmark for evaluation under real low-light conditions. Experimental results demonstrate that SEGD surpasses state-of-the-art methods while achieving higher efficiency with fewer parameters.
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All authors Pu Li, Huafeng Li, Yafei Zhang, Yu Liu, Wen Wang

Computer Vision

Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization

Hefeng Zhou, Xuan Liu, Sicheng Chen et al. (+7 more)

Federated cross-modal retrieval faces severe challenges from heterogeneous client data, particularly non-IID semantic distributions and missing modalities. Under such heterogeneity, a single global model is often insufficient to capture both shared cross-modal knowledge and client-specific characteristics. We propose RCSR, a personalization-friendly federated framework that integrates prototype anchoring, retrieval-centric semantic routing, and optional client-specific adapters. Built on a frozen CLIP backbone, RCSR leverages lightweight shared adapters for global knowledge transfer while supporting efficient local personalization. Prototype anchoring helps unimodal clients align with global cross-modal semantics, and a server-side semantic router adaptively assigns aggregation weights based on retrieval consistency to mitigate alignment drift during heterogeneous updates. Extensive experiments on MS-COCO, Flickr30K, and other benchmarks show that RCSR consistently improves global retrieval accuracy and training stability, while further enhancing client-level retrieval performance, especially for clients with incomplete modalities. Code is available at https://github.com/RezinChow/RCSR-Retrieval-Centric-Semantic-Routing.
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All authors Hefeng Zhou, Xuan Liu, Sicheng Chen, Wutong Zhang, Wu Yan, Jiong Lou, Chentao Wu, Guangtao Xue, Wei Zhao, Jie Li

Computer Vision

Can Multimodal Large Language Models Truly Understand Small Objects?

Fujun Han, Junan Chen, Xintong Zhu et al. (+4 more)

Multimodal Large Language Models (MLLMs) have shown promising potential in diverse understanding tasks, e.g., image and video analysis, math and physics olympiads. However, they remain blank and unexplored for Small Object Understanding (SOU) tasks. To fill this gap, we introduce SOUBench, the first and comprehensive benchmark for exploring the small objects understanding capability of existing MLLMs. Specifically, we first design an effective and automatic visual question-answer generation strategy, constructing a new SOU-VQA evaluation dataset, with 18,204 VQA pairs, six relevant sub-tasks, and three dominant scenarios (i.e., Driving, Aerial, and Underwater). Then, we conduct a comprehensive evaluation on 15 state-of-the-art MLLMs and reveal their weak capabilities in small object understanding. Furthermore, we develop SOU-Train, a multimodal training dataset with 11,226 VQA pairs, to improve the SOU capabilities of MLLMs. Through supervising fine-tuning of the latest MLLM, we demonstrate that SOU-Train can effectively enhance the latest MLLM's ability to understand small objects. Comprehensive experimental results demonstrate that, the proposed SOUBench, along with the SOU-VQA and SOU-Train datasets, provides a crucial empirical foundation to the community for further developing models with enhanced small object understanding capabilities. Datasets and Code: https://github.com/Hanfj-X/SOU.
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All authors Fujun Han, Junan Chen, Xintong Zhu, Jingqi Ye, Xuanjie Mao, Tao Chen, Peng Ye

Computer Vision

NeuroAPS-Net: Neuro-Anatomically Aware Point Cloud Representation for Efficient Alzheimer's Disease Classification

Towhidul Islam and Mufti Mahmud

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a major cause of dementia. Structural MRI is widely used to analyze AD-related brain atrophy; however, most deep learning methods rely on computationally expensive 3D convolutional neural networks (CNNs), limiting deployment in resource-constrained settings. This work introduces two main contributions. First, we propose a pipeline that converts T1-weighted MRI into anatomically informed 2D point clouds using Anatomical Priority Sampling (APS), producing ADNI-2DPC, the first neuroanatomically labeled MRI-derived point cloud dataset. Second, we present NeuroAPS-Net, a lightweight geometric deep learning model that incorporates anatomical priors via region-aware feature encoding and ROI token aggregation. Experiments on ADNI-2DPC demonstrate that NeuroAPS-Net achieves competitive classification accuracy while significantly reducing inference latency and GPU memory compared to state-of-the-art point cloud methods. These results highlight the potential of anatomically guided point cloud learning as an efficient and interpretable alternative to voxel-based CNNs for AD classification.
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Computer Vision

Inter-Stance: A Dyadic Multimodal Corpus for Conversational Stance Analysis

Xiang Zhang, Xiaotian Li, Taoyue Wang et al. (+9 more)

Social interactions dominate our perceptions of the world and shape our daily behavior by attaching social meaning to acts as simple and spontaneous as gestures, facial expressions, voice, and speech. People mimic and otherwise respond to each other's postures, facial expressions, mannerisms, and other verbal and nonverbal behavior, and form appraisals or evaluations in the process. Yet, no publicly-available dataset includes multimodal recordings and self-report measures of multiple persons in social interaction. Dyadic recordings and annotation are lacking. We present a new data corpus of multimodal dyadic interaction (45 dyads, 90 persons) that includes synchronized multi-modality behavior (2D face video, 3D face geometry, thermal spectrum dynamics, voice and speech behavior, physiology (PPG, EDA, heart-rate, blood pressure, and respiration), and self-reported affect of all participants in a communicative interaction scenario. Two types of dyads are included: persons with shared past history and strangers. Annotations include social signals, agreement, disagreement, and neutral stance. With a potent emotion induction, these multimodal data will enable novel modeling of multimodal interpersonal behavior. We present extensive experiments to evaluate multimodal dyadic communication of dyads with and without interpersonal history, and their affect. This new database will make multimodal modeling of social interaction never possible before. The dataset includes 20TB of multimodal data to share with the research community.
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All authors Xiang Zhang, Xiaotian Li, Taoyue Wang, Nan Bi, Xin Zhou, Cody Zhou, Zoie Wang, Andrew Yang, Yuming Su, Jeff Cohn, Qiang Ji, Lijun Yin

Computer Vision

Long-tail Internet photo reconstruction

Yuan Li, Yuanbo Xiangli, Hadar Averbuch-Elor et al. (+2 more)

Internet photo collections exhibit an extremely long-tailed distribution: a few famous landmarks are densely photographed and easily reconstructed in 3D, while most real-world sites are represented with sparse, noisy, uneven imagery beyond the capabilities of both classical and learned 3D methods. We believe that tackling this long-tail regime represents one of the next frontiers for 3D foundation models. Although reliable ground-truth 3D supervision from sparse scenes is challenging to acquire, we observe that it can be effectively simulated by sampling sparse subsets from well-reconstructed Internet landmarks. To this end, we introduce MegaDepth-X, a large dataset of 3D reconstructions with clean, dense depth, together with a strategy for sampling sets of training images that mimic camera distributions in long-tail scenes. Finetuning 3D foundation models with these components yields robust reconstructions under extreme sparsity, and also enables more reliable reconstruction in symmetric and repetitive scenes, while preserving generalization to standard, dense 3D benchmark datasets.
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All authors Yuan Li, Yuanbo Xiangli, Hadar Averbuch-Elor, Noah Snavely, Ruojin Cai

Computer Vision

4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh et al. (+2 more)

Modeling and predicting neurodegenerative disease progression from medical images remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most longitudinal neuroimaging datasets are temporally sparse, with substantial gaps and missing follow-up scans for individual subjects. This makes it difficult to learn and accurately capture the continuous anatomical changes associated with disease progression at the level of individual subjects. To address this problem, we propose a novel model named 4DLoG, a full 4D (3DxT) Longitudinal Generative framework that effectively models and synthesizes follow-up brain anatomy over time, conditioned on available clinical and demographic variables. In contrast to previous approaches, our 4DLoG features two main contributions. First, it introduces a full 4D generative diffusion framework that jointly models spatial and temporal dependencies across complete longitudinal sequences through dedicated spatiotemporal attention, with robust spatial patch extraction and temporal alignment. Second, it explicitly learns the distribution of topology-preserving spatiotemporal deformations, which captures realistic geometric changes in brain structures over time. These new components enable a better generation of anatomically plausible future states from an imaging scan at any time point, providing greater flexibility for modeling individual longitudinal brain trajectories. We validate our model through both synthetic sequence generation and downstream longitudinal disease classification, as well as brain segmentation. Experiments on two large-scale longitudinal neuroimage datasets demonstrate that our method outperforms state-of-the-art baselines in generating anatomically accurate, temporally consistent, and clinically meaningful brain trajectories. Our code is available on Github
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All authors Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh, Qingyu Zhao, Miaomiao Zhang

Computer Vision

SS3D: End2End Self-Supervised 3D from Web Videos

Marwane Hariat, Gianni Franchi, David Filliat et al. (+1 more)

We present SS3D, a web-scale SfM-based self-supervision pretraining pipeline for feed-forward 3D estimation from monocular video. Our model jointly predicts depth, ego-motion, and intrinsics in a single forward pass and is trained/evaluated as a coherent end-to-end 3D estimator. To stabilize joint learning, we use an intrinsics-first two-stage schedule and a unified single-checkpoint evaluation protocol. Scaling SfM self-supervision to unconstrained web video is challenging due to weak multi-view observability and strong corpus heterogeneity; we address these with a multi-view signal proxy (MVS) used for filtering and curriculum sampling, and with expert training distilled into a single student. Pretraining on YouTube-8M (~100M frames after filtering) yields strong cross-domain zero-shot transfer and improved fine-tuning performance over prior self-supervised baselines. We release the pretrained checkpoint and code.
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All authors Marwane Hariat, Gianni Franchi, David Filliat, Antoine Manzanera

Computer Vision

PASR: Pose-Aware 3D Shape Retrieval from Occluded Single Views

Jiaxin Shi, Guofeng Zhang, Wufei Ma et al. (+3 more)

Single-view 3D shape retrieval is a fundamental yet challenging task that is increasingly important with the growth of available 3D data. Existing approaches largely fall into two categories: those using contrastive learning to map point cloud features into existing vision-language spaces and those that learn a common embedding space for 2D images and 3D shapes. However, these feed-forward, holistic alignments are often difficult to interpret, which in turn limits their robustness and generalization to real-world applications. To address this problem, we propose Pose-Aware 3D Shape Retrieval (PASR), a framework that formulates retrieval as a feature-level analysis-by-synthesis problem by distilling knowledge from a 2D foundation model (DINOv3) into a 3D encoder. By aligning pose-conditioned 3D projections with 2D feature maps, our method bridges the gap between real-world images and synthetic meshes. During inference, PASR performs a test-time optimization via analysis-by-synthesis, jointly searching for the shape and pose that best reconstruct the patch-level feature map of the input image. This synthesis-based optimization is inherently robust to partial occlusion and sensitive to fine-grained geometric details. PASR substantially outperforms existing methods on both clean and occluded 3D shape retrieval datasets by a wide margin. Additionally, PASR demonstrates strong multi-task capabilities, achieving robust shape retrieval, competitive pose estimation, and accurate category classification within a single framework.
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All authors Jiaxin Shi, Guofeng Zhang, Wufei Ma, Naifu Liang, Adam Kortylewski, Alan Yuille

Computer Vision

A Non-Invasive Alternative to RFID: Self-Sufficient 3D Identification of Group-Housed Livestock

Shiva Paudel, TsungCheng Tsai and Dongyi Wang

Accurate identification of individual farm animals in group-housed environments is a cornerstone of precision livestock management. However, current industry standards rely heavily on Radio Frequency Identification (RFID) ear tags, which are invasive, prone to loss, and restricted by the spatial limitations of antenna fields. In this paper, we propose a non-intrusive, vision-based identification system leveraging 3D point cloud data captured within a commercial electronic feeding station (EFS). Departing from traditional supervised frame-level inference, we introduce the Temporal Adaptive Recognition Architecture (TARA), a self-sufficient, semi-supervised framework designed to maintain identity consistency over time. TARA employs a dynamic recalibration mechanism that updates individual identity profiles to account for morphological changes in the livestock. To facilitate training in label-scarce environments, we utilize a visit-level majority voting strategy to generate high-fidelity pseudo-labels from raw temporal sequences. Experimental results on a group housed sow dataset collected from an operational commercial barn demonstrate that our approach achieves 100% identification accuracy at the visit level. These results suggest that vision-based 3D point cloud analysis offers a robust, superior alternative to RFID-based systems, paving the way for fully autonomous individual animal monitoring.
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Computer Vision · Cross-listed from cs.NE

Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction

Yongxiang Lian, Yueyang Cang, Pingge Hu et al. (+2 more)

Objective: Decoding visual information from electroencephalography (EEG) is an important problem in neuroscience and brain-computer interface (BCI) research. Existing methods are largely restricted to natural images and categorical representations, with limited capacity to capture structural features and to differentiate objective perception from subjective cognition. We propose a Structure-Guided Diffusion Model (SGDM) that incorporates explicit structural information for EEG-based visual reconstruction. Approach: SGDM is evaluated on the Kilogram abstract visual object dataset and the THINGS natural image dataset using a two-stage generative mechanism. The framework combines a structurally supervised variational autoencoder with a spatiotemporal EEG encoder aligned to a visual embedding space via contrastive learning. Structural information is integrated into a diffusion model through ControlNet to guide image generation from EEG features. Results: SGDM outperforms existing methods on both abstract and natural image datasets. Reconstructed images achieve higher fidelity in low-level visual features and semantic representations, indicating improved decoding accuracy and strong generalization across diverse visual domains. Spatiotemporal analysis of EEG signals further reveals hierarchical structural encoding patterns, consistent with the neural dynamics of visual cognition. Significance: These findings validate the effectiveness of SGDM in capturing explicit structural geometry and generating images with high fidelity to individual cognitive representations. By enabling decoding of complex visual content from EEG signals, the framework extends neural decoding beyond low-dimensional or categorical outputs. This supports BCIs with increased degrees of freedom for intention decoding and more flexible brain-to-machine communication.
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All authors Yongxiang Lian, Yueyang Cang, Pingge Hu, Yuchen He, Li Shi

Computer Vision

EV-CLIP: Efficient Visual Prompt Adaptation for CLIP in Few-shot Action Recognition under Visual Challenges

Hyo Jin Jon, Longbin Jin and Eun Yi Kim

CLIP has demonstrated strong generalization in visual domains through natural language supervision, even for video action recognition. However, most existing approaches that adapt CLIP for action recognition have primarily focused on temporal modeling, often overlooking spatial perception. In real-world scenarios, visual challenges such as low-light environments or egocentric viewpoints can severely impair spatial understanding, an essential precursor for effective temporal reasoning. To address this limitation, we propose Efficient Visual Prompting for CLIP (EV-CLIP), an efficient adaptation framework designed for few-shot video action recognition across diverse scenes and viewpoints. EV-CLIP introduces two visual prompts: mask prompts, which guide the model's attention to action-relevant regions by reweighting pixels, and context prompts, which perform lightweight temporal modeling by compressing frame-wise features into a compact representation. For a comprehensive evaluation, we curate five benchmark datasets and analyze domain shifts to quantify the influence of diverse visual and semantic factors on action recognition. Experimental results demonstrate that EV-CLIP outperforms existing parameter-efficient methods in overall performance. Moreover, its efficiency remains independent of the backbone scale, making it well-suited for deployment in real-world, resource-constrained scenarios. The code is available at https://github.com/AI-CV-Lab/EV-CLIP.
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Computer Vision

FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing

Ze Chen, Lan Chen, Yuanhang Li et al. (+1 more)

We propose FlowAnchor, a training-free framework for stable and efficient inversion-free, flow-based video editing. Inversion-free editing methods have recently shown impressive efficiency and structure preservation in images by directly steering the sampling trajectory with an editing signal. However, extending this paradigm to videos remains challenging, often failing in multi-object scenes or with increased frame counts. We identify the root cause as the instability of the editing signal in high-dimensional video latent spaces, which arises from imprecise spatial localization and length-induced magnitude attenuation. To overcome this challenge, FlowAnchor explicitly anchors both where to edit and how strongly to edit. It introduces Spatial-aware Attention Refinement, which enforces consistent alignment between textual guidance and spatial regions, and Adaptive Magnitude Modulation, which adaptively preserves sufficient editing strength. Together, these mechanisms stabilize the editing signal and guide the flow-based evolution toward the desired target distribution. Extensive experiments demonstrate that FlowAnchor achieves more faithful, temporally coherent, and computationally efficient video editing across challenging multi-object and fast-motion scenarios. The project page is available at https://cuc-mipg.github.io/FlowAnchor.github.io/.
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All authors Ze Chen, Lan Chen, Yuanhang Li, Qi Mao

Computer Vision · Cross-listed from eess.IV

Useful nonrobust features are ubiquitous in biomedical images

Coenraad Mouton, Randle Rabe, Niklas C. Koser et al. (+4 more)

We study whether deep networks for medical imaging learn useful nonrobust features - predictive input patterns that are not human interpretable and highly susceptible to small adversarial perturbations - and how these features impact test performance. We show that models trained only on nonrobust features achieve well above chance accuracy across five MedMNIST classification tasks, confirming their predictive value in-distribution. Conversely, adversarially trained models that primarily rely on robust features sacrifice in-distribution accuracy but yield markedly better performance under controlled distribution shifts (MedMNIST-C). Overall, nonrobust features boost standard accuracy yet degrade out-of-distribution performance, revealing a practical robustness-accuracy trade-off in medical imaging classification tasks that should be tailored to the requirements of the deployment setting.
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All authors Coenraad Mouton, Randle Rabe, Niklas C. Koser, Nicolai Krekiehn, Christopher Hansen, Jan-Bernd Hövener, Claus-C. Glüer

Computer Vision · Cross-listed from cs.LG

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy

Asim Ukaye, Mubarak Abdu-Aguye, Nurbek Tastan et al. (+1 more)

Client contribution estimation in Federated Learning is necessary for identifying clients' importance and for providing fair rewards. Current methods often rely on server-side validation data or self-reported client information, which can compromise privacy or be susceptible to manipulation. We introduce a data-free signal based on the matrix von Neumann (spectral) entropy of the final-layer updates, which measures the diversity of the information contributed. We instantiate two practical schemes: (i) SpectralFed, which uses normalized entropy as aggregation weights, and (ii) SpectralFuse, which fuses entropy with class-specific alignment via a rank-adaptive Kalman filter for per-round stability. Across CIFAR-10/100 and the naturally partitioned FEMNIST and FedISIC benchmarks, entropy-derived scores show a consistently high correlation with standalone client accuracy under diverse non-IID regimes - without validation data or client metadata. We compare our results with data-free contribution estimation baselines and show that spectral entropy serves as a useful indicator of client contribution.
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All authors Asim Ukaye, Mubarak Abdu-Aguye, Nurbek Tastan, Karthik Nandakumar

Computer Vision

Cross-Stage Coherence in Hierarchical Driving VQA: A Consistency Protocol and Parameter-Efficient Gated Context Routing

Gautam Kumar Jain, Carsten Markgraf and Julian Stähler

Graph Visual Question Answering (GVQA) for autonomous driving organizes reasoning into ordered stages, namely Perception, Prediction, and Planning, where planning decisions should remain consistent with the model's own perception. We present a comparative study of cross-stage context passing on DriveLM-nuScenes using two complementary mechanisms. The explicit variant evaluates three prompt-based conditioning strategies on a domain-adapted 4B VLM (Mini-InternVL2-4B-DA-DriveLM) without additional training, reducing NLI contradiction by up to 42.6% and establishing a strong zero-training baseline. The implicit variant introduces gated context projectors, which extract a hidden-state vector from one stage and inject a normalized, gated projection into the next stage's input embeddings. These projectors are jointly trained with stage-specific QLoRA adapters on a general-purpose 8B VLM (InternVL3-8B-Instruct) while updating only approximately 0.5% of parameters. The implicit variant achieves a statistically significant 34% reduction in planning-stage NLI contradiction (bootstrap 95% CIs, p < 0.05) and increases cross-stage entailment by 50%, evaluated with a multilingual NLI classifier to account for mixed-language outputs. Planning language quality also improves (CIDEr +30.3%), but lexical overlap and structural consistency degrade due to the absence of driving-domain pretraining. Since the two variants use different base models, we present them as complementary case studies: explicit context passing provides a strong training-free baseline for surface consistency, while implicit gated projection delivers significant planning-stage semantic gains, suggesting domain adaptation as a plausible next ingredient for full-spectrum improvement.
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Computer Vision · Cross-listed from eess.IV

Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?

Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier et al. (+2 more)

The emergence of large-scale pretrained foundation models has transformed computer vision, enabling strong performance across diverse downstream tasks. However, their potential for physics-based inverse problems, such as accelerated cardiac MRI reconstruction, remains largely underexplored. In this work, we investigate whether natural-domain foundation models can serve as effective image priors for accelerated cardiac MRI reconstruction, and compare the performance obtained against domain-specific counterparts such as BiomedCLIP. We propose an unrolled reconstruction framework that incorporates pretrained, frozen visual encoders, such as CLIP, DINOv2, and BiomedCLIP, within each cascade to guide the reconstruction process. Through extensive experiments, we show that while task-specific state-of-the-art reconstruction models such as E2E-VarNet achieve superior performance in standard in-distribution settings, foundation-model-based approaches remain competitive. More importantly, in challenging cross-domain scenarios, where models are trained on cardiac MRI and evaluated on anatomically distinct knee and brain datasets–foundation models exhibit improved robustness, particularly under high acceleration factors and limited low-frequency sampling. We further observe that natural-image-pretrained models, such as CLIP, learn highly transferable structural representations, while domain-specific pretraining (BiomedCLIP) provides modest additional gains in more ill-posed regimes. Overall, our results suggest that pretrained foundation models offer a promising source of transferable priors, enabling improved robustness and generalization in accelerated MRI reconstruction.
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All authors Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier, Kathleen M. Curran, Noel E. O'Connor

Computer Vision

Video Analysis and Generation via a Semantic Progress Function

Gal Metzer, Sagi Polaczek, Ali Mahdavi-Amiri et al. (+2 more)

Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely changes are followed by sudden, abrupt semantic jumps. To analyze and correct this behavior, we introduce a Semantic Progress Function, a one-dimensional representation that captures how the meaning of a given sequence evolves over time. For each frame, we compute distances between semantic embeddings and fit a smooth curve that reflects the cumulative semantic shift across the sequence. Departures of this curve from a straight line reveal uneven semantic pacing. Building on this insight, we propose a semantic linearization procedure that reparameterizes (or retimes) the sequence so that semantic change unfolds at a constant rate, yielding smoother and more coherent transitions. Beyond linearization, our framework provides a model-agnostic foundation for identifying temporal irregularities, comparing semantic pacing across different generators, and steering both generated and real-world video sequences toward arbitrary target pacing.
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All authors Gal Metzer, Sagi Polaczek, Ali Mahdavi-Amiri, Raja Giryes, Daniel Cohen-Or

Computer Vision

Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models

Shihui Yan, Ziqi Zhou, Yufei Song et al. (+3 more)

Physical adversarial patch attacks critically threaten pedestrian detection, causing surveillance and autonomous driving systems to miss pedestrians and creating severe safety risks. Despite their effectiveness in controlled settings, existing physical attacks face two major limitations in practice: they lack systematic disruption of the multi-stage decision pipeline, enabling residual modules to offset perturbations, and they fail to model complex physical variations, leading to poor robustness. To overcome these limitations, we propose a novel pedestrian adversarial patch generation method that combines multi-stage collaborative attacks with robustness enhancement under physical diversity, called TriPatch. Specifically, we design a triplet loss consisting of detection confidence suppression, bounding-box offset amplification, and non-maximum suppression (NMS) disruption, which jointly act across different stages of the detection pipeline. In addition, we introduce an appearance consistency loss to constrain the color distribution of the patch, thereby improving its adaptability under diverse imaging conditions, and incorporate data augmentation to further enhance robustness against complex physical perturbations. Extensive experiments demonstrate that TriPatch achieves a higher attack success rate across multiple detector models compared to existing approaches.
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All authors Shihui Yan, Ziqi Zhou, Yufei Song, Yifan Hu, Minghui Li, Shengshan Hu

Computer Vision

ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation

Amir Hosseini, Sara Farahani, Xinyi Li et al. (+1 more)

Open-vocabulary scene graph generation (SGG) aims to describe visual scenes with flexible relation phrases beyond a fixed predicate set. Existing methods usually treat annotated triplets as positives and all unannotated object-pair relations as negatives. However, scene graph annotations are inherently incomplete: many valid relations are missing, and the same interaction can be described at different granularities, e.g., on, standing on, resting on, and supported by. This issue becomes more severe in open-vocabulary SGG due to the much larger relation space. We propose ReLIC-SGG, a relation-incompleteness-aware framework that treats unannotated relations as latent variables rather than definite negatives. ReLIC-SGG builds a semantic relation lattice to model similarity, entailment, and contradiction among open-vocabulary predicates, and uses it to infer missing positive relations from visual-language compatibility, graph context, and semantic consistency. A positive-unlabeled graph learning objective further reduces false-negative supervision, while lattice-guided decoding produces compact and semantically consistent scene graphs. Experiments on conventional, open-vocabulary, and panoptic SGG benchmarks show that ReLIC-SGG improves rare and unseen predicate recognition and better recovers missing relations.
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All authors Amir Hosseini, Sara Farahani, Xinyi Li, Suiyang Guang

Computer Vision

Evolving Thematic Map Design in Academic Cartography: A Thirty-Year Study Based on Multilingual Journals

Zhiwei Wei, Chenxi Song, Tazhu Wang et al. (+4 more)

Thematic maps play a central role in academic communication, yet their large-scale design evolution has rarely been examined empirically. This study presents a longitudinal and multilingual analysis of thematic map design practices in academic cartography from 1990 to 2020. We compile a corpus of 45,732 research articles from sixteen authoritative Chinese- and English-language journals and extract 23,928 maps using computer vision and large-model-based document parsing to build a structured dataset. Map design characteristics are quantified across three dimensions: map elements, color design, and layout structure. Results show that Chinese- and Englishlanguage academic maps share highly similar structural conventions, typically employing restrained color palettes with neutral dominant hues, low saturation, high brightness, and limited hue diversity, as well as centered layouts with high main-map occupation ratios. Differences exist in that English-language maps show slightly greater hue richness and compactness, whereas Chinese-language maps historically rely more on neutral hues and integrated layouts. Temporal analysis reveals parallel evolutionary trends in both groups, including increasing element richness, legend usage, and hue diversity, alongside stable layout structures. Overall, the findings suggest that academic map design evolution is characterized more by institutional convergence than cultural divergence.
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All authors Zhiwei Wei, Chenxi Song, Tazhu Wang, Fan Wu, Hua Liao, Su Ding, Nai Yang

Computer Vision

Distilling Vision Transformers for Distortion-Robust Representation Learning

Konstantinos Alexis, Giorgos Giannopoulos and Dimitrios Gunopulos

Self-supervised learning has achieved remarkable success in learning visual representations from clean data, yet remains challenging when clean observations are sparse or not available at all. In this paper, we demonstrate that pretrained vision models can be leveraged to learn distortion-robust representations, which can then be effectively applied to downstream tasks operating on distorted observations. In particular, we propose an asymmetric knowledge distillation framework in which both teacher and student are initialized from the same pretrained Vision Transformer but receive different views of each image: the teacher processes clean images, while the student sees their distorted versions. We introduce multi-level distillation that aligns global embeddings, patch-level features, and attention maps and show that the student is able to approximate clean-image representations despite never directly accessing clean data. We evaluate our approach on image classification tasks across several datasets and under various distortions, consistently outperforming existing alternatives for the same amount of human supervision.
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Computer Vision

Non-Minimal Sampling and Consensus for Prohibitively Large Datasets

Seong Hun Lee, Patrick Vandewalle and Javier Civera

We introduce NONSAC (Non-Minimal Sampling and Consensus), a general framework for robust and scalable model estimation from arbitrarily large datasets contaminated with noise and outliers. NONSAC repeatedly samples non-minimal subsets of data and generates model hypotheses using a robust estimator, producing multiple candidate models. The final model is selected based on a predefined scoring rule that evaluates hypothesis quality. Our framework is estimator-agnostic and can be integrated with existing geometric fitting algorithms such as RANSAC to improve both scalability and robustness to outliers. We propose and evaluate various scoring rules for NONSAC on relative camera pose estimation, Perspective-n-Point, and point cloud registration. Furthermore, we showcase the applicability of NONSAC to correspondence-free point cloud registration by hypothesizing all-to-all correspondences.
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Computer Vision

Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts

Hamza A. Abushahla, Ariel Justine N. Panopio, Layth Al-Khairulla et al. (+1 more)

Handwritten Arabic manuscripts preserve the Arab world's intellectual and cultural heritage, and writer identification supports provenance, authenticity verification, and historical analysis. Using the Muharaf dataset of historical Arabic manuscripts, we evaluate writer identification from individual line images and, to the best of our knowledge, provide the first baselines reported under both line-level and page-disjoint evaluation protocols. Since the dataset is only partially labeled for writer identification, we manually verified and expanded writer labels in the public portion from 6,858 (28.00%) to 21,249 lines (86.75%) out of 24,495 line images, correcting inconsistencies and removing non-handwritten text. After further filtering, we retained 18,987 lines (77.51%). We propose a Convolutional Neural Network (CNN)-based model with attention mechanisms for closed-set writer identification, including rare two-writer lines modeled as composite writer-pair classes. We benchmark fourteen configurations and conduct ablations across different feature extractors and training regimes. To assess generalization to unseen pages, the page-disjoint protocol assigns all lines from each page to a single split. Under the line-level protocol, a fine-tuned DenseNet201 with attention achieves 99.05% Top-1 accuracy, 99.73% Top-5 accuracy, and 97.44% F1-score. Under the more challenging page-disjoint protocol, the best observed results are 78.61% Top-1 accuracy, 87.79% Top-5 accuracy, and 66.55% F1-score, thus quantifying the impact of page-level cues. By expanding the Muharaf dataset's labeled subset and reporting both protocols, we provide a clearer benchmark and a practical resource for historians and linguists engaged with culturally and historically significant documents. The code and implementation details are available on GitHub.
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All authors Hamza A. Abushahla, Ariel Justine N. Panopio, Layth Al-Khairulla, Mohamed I. AlHajri

Computer Vision

Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain

Annika Bätz, Pavel Klasek, Seo-Young Ham et al. (+1 more)

Benchmark suites are crucial for advancing computer vision research, providing not only curated datasets but also standardized evaluation protocols for reproducible comparison. The railway domain lacks such resources, hindering progress in automated train operation. To address this gap, we present RAIL-BENCH, the first perception benchmark suite designed specifically for railways. RAIL-BENCH comprises five challenges - rail track detection, object detection, vegetation segmentation, multi-object tracking, and monocular visual odometry - each tailored to railway-specific characteristics and including a curated dataset from diverse real-world scenarios, evaluation metrics, and public scoreboards; we demonstrate the first three challenges in this paper. We further introduce LineAP, a novel segment-based average precision metric for rail track detection that decouples geometric accuracy from instance-level grouping and employs a track-width-based distance threshold approximating a fixed real-world distance, addressing key limitations of existing line detection metrics. Our benchmark suite is publicly available at https://www.mrt.kit.edu/railbench/.
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All authors Annika Bätz, Pavel Klasek, Seo-Young Ham, Martin Lauer

Computer Vision

ICPR 2026 Competition on Low-Resolution License Plate Recognition

Rayson Laroca, Valfride Nascimento, Donggun Kim et al. (+19 more)

Low-Resolution License Plate Recognition (LRLPR) remains a challenging problem in real-world surveillance scenarios, where long capture distances, compression artifacts, and adverse imaging conditions can severely degrade license plate legibility. To promote progress in this area, we organized the ICPR 2026 Competition on Low-Resolution License Plate Recognition, the first competition specifically dedicated to LRLPR using real low-quality data collected under operationally relevant conditions. The competition was based on the LRLPR-26 dataset, which comprises 20,000 training tracks and 3,000 test tracks; each training track contains five low-resolution and five high-resolution images of the same license plate. Notably, a total of 269 teams from 41 countries registered for the competition, and 99 teams submitted valid entries in the Blind Test Phase. The winning team achieved a Recognition Rate of 82.13%, and four teams surpassed the 80% mark, highlighting both the high level of competition at the top of the leaderboard and the continued difficulty of the task. In addition to presenting the competition design, evaluation protocol, and main results, this paper summarizes the methods adopted by the top-5 teams and discusses current trends and promising directions for future research on LRLPR. The competition webpage is available at https://icpr26lrlpr.github.io/
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All authors Rayson Laroca, Valfride Nascimento, Donggun Kim, Sanghyeok Chung, Subin Bae, Uihwan Seo, Seungsang Oh, Chi M. Phung, Minh G. Vo, Xingsong Ye, Yongkun Du, Yuchen Su, Zhineng Chen, Sunhee Heo, Hyangwoo Lee, Kihyun Na, Khanh V. Vu Nguyen, Sang T. Pham, Duc N. N. Phung, Trong P. Le, Vy N. Vo Tran, David Menotti

Computer Vision

CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding

Lihao Zheng, Zhenwei Shao, Yu Zhou et al. (+5 more)

Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding, often exhibiting spatial hallucination, attention leakage, and failures in object constancy. In addition, existing approaches typically rely on expensive human annotations or large-scale chain-of-thought (CoT) data generation. We propose Compositional Grounded Contrast (abbr. CGC), a low-cost full framework for boosting fine-grained multi-image understanding of MLLMs. Built on existing single-image grounding annotations, CGC constructs compositional multi-image training instances through Inter-Image Contrast and Intra-Image Contrast, which introduce semantically decoupled distractor contexts for cross-image discrimination and correlated cross-view samples for object constancy, respectively. CGC further introduces a Rule-Based Spatial Reward within the GRPO framework to improve source-image attribution, spatial alignment, and structured output validity under a Think-before-Grounding paradigm. Experiments show that CGC achieves state-of-the-art results on fine-grained multi-image benchmarks, including MIG-Bench and VLM2-Bench. The learned multi-image understanding capability also transfers to broader multimodal understanding and reasoning tasks, yielding consistent gains over the Qwen3-VL-8B base model on MathVista (+2.90), MuirBench (+2.88), MMStar (+1.93), MMMU (+1.77), and BLINK (+1.69).
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All authors Lihao Zheng, Zhenwei Shao, Yu Zhou, Yan Yang, Xintian Shen, Jiawei Chen, Hao Ma, Tao Wei

Computer Vision · Cross-listed from eess.IV

MTT-Bench: Predicting Social Dominance in Mice via Multimodal Large Language Models

Yunquan Chen and Haoyu Chen

Understanding social dominance in animal behavior is critical for neuroscience and behavioral studies. In this work, we explore the capability of Multimodal Large Language Models(MLLMs) to analyze raw behavioral video of mice and predict their dominance hierarchy. We introduce MTT-Bench, a novel benchmark comprising annotated videos of pairwise mouse interactions for Mouse Tube Test analysis. Building on existing MLLM architectures, we fine-tune these models to perform zero-shot inference on unseen behavioral sequences, predicting social dominance without explicit labels during testing. Our framework demonstrates promising results, showing high agreement with tube test rankings. This work opens a new direction for applying foundation models to ethology and social behavior analysis, without the need to design domain-specific models.
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