Computer Vision · Cross-listed from cs.GR
FaceParts: Segmentation and Editing of Gaussian Splatting
Tymoteusz Zapała, Julia Farganus, Dominik Galus et al. (+3 more)
Facial editing is an important task with applications in entertainment, virtual reality, and digital avatars. Most existing approaches rely on generative models in the 2D image domain, while in 3D the task is typically performed through labor-intensive manual editing. We propose FaceParts, a framework for unsupervised
segmentation and editing of Gaussian Splatting avatars. Unlike existing 2D or mesh-assisted methods, our approach operates directly in the Gaussian domain, decomposing avatars into semantically coherent facial parts without supervision. The method integrates feature disentanglement, density-based clustering, and FLAME-anchored part transfer, enabling precise editing and cross-avatar part swapping. Experiments on the NeRSemble
dataset with 11 subjects demonstrate robust isolation of features such as beards, eyebrows, eyes and mustaches. Quantitative evaluation confirms that transferred segments adapt to pose and expression, while maintaining identity consistency (ID = 0.943), low Average Expression Distance (AED = 0.021) and low Average Pose Distance (APD = 0.004).
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All authors Tymoteusz Zapała, Julia Farganus, Dominik Galus, Mikołaj Czachorowski, Piotr Syga, Przemysław Spurek
Computer Vision · Cross-listed from cs.GR
Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning
Ido Sobol, Kihyuk Sohn, Yoav Blum et al. (+4 more)
We often aim to generate images that are both photorealistic and 3D-consistent, adhering to precise geometry, material, and viewpoint controls. Typically, this is achieved by fine-tuning an image generator, pre-trained on billions of real images, using renders of synthetic 3D assets, where annotations for control signals are available. While this approach can learn the desired controls, it often compromises the realism of the images due to domain gap between photographs and renders. We observe that this issue largely arises from the model learning an unintended association between the presence of control signals and the synthetic appearance of the images. To address this, we introduce Realiz3D, a lightweight framework for training
diffusion models, that decouples controls and visual domain. The key idea is to explicitly learn visual domain, real or synthetic, separately from other control signals by introducing a co-variate that, fed into small residual adapters, shifts the domain. Then, the generator can be trained to gain controllability, without fitting to specific visual domain. In this way, the model can be guided to produce realistic images even when controls are applied. We enhance control transferability to the real domain by leveraging insights about roles of different layers and denoising steps in
diffusion-based generators, informing new training and inference strategies that further mitigate the gap. We demonstrate the advantages of Realiz3D in tasks as text-to-multiview generation and texturing from 3D inputs, producing outputs that are 3D-consistent and photorealistic.
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All authors Ido Sobol, Kihyuk Sohn, Yoav Blum, Egor Zakharov, Max Bluvstein, Andrea Vedaldi, Or Litany
Computer Vision
PhyDCM: A Reproducible Open-Source Framework for AI-Assisted Brain Tumor Classification from Multi-Sequence MRI
Hayder Saad Abdulbaqi, Mohammed Hadi Rahim, Mohammed Hassan Hadi et al. (+2 more)
MRI-based medical imaging has become indispensable in modern clinical diagnosis, particularly for
brain tumor detection. However, the rapid growth in data volume poses challenges for conventional diagnostic approaches. Although deep learning has shown strong performance in automated classification, many existing solutions are confined to closed technical architectures, limiting reproducibility and further academic development. PhyDCM is introduced as an open-source software framework that integrates a hybrid classification architecture based on MedViT with standardized DICOM processing and an interactive desktop visualization interface. The system is designed as a modular digital library that separates computational logic from the graphical interface, allowing independent modification and extension of components. Standardized preprocessing, including intensity rescaling and limited data augmentation, ensures consistency across varying MRI acquisition settings. Experimental evaluation on MRI
datasets from BRISC2025 and curated Kaggle collections (FigShare, SARTAJ, and Br35H) demonstrates stable diagnostic performance, achieving over 93% classification accuracy across categories. The framework supports structured, exportable outputs and multi-planar reconstruction of volumetric data. By emphasizing transparency, modularity, and accessibility, PhyDCM provides a practical foundation for reproducible AI-driven medical image analysis, with flexibility for future integration of additional imaging modalities.
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All authors Hayder Saad Abdulbaqi, Mohammed Hadi Rahim, Mohammed Hassan Hadi, Haider Ali Aboud, Ali Hussein Allawi
Computer Vision
Simplifying, Scaling, and Saturating Flow Matching for Cell Microscopy
Charles Jones, Emmanuel Noutahi, Zeinab Navidi et al. (+4 more)
Flow-matching generative models are increasingly used to simulate
cell responses to biological perturbations. However, the design space for building such models is large and underexplored. We systematically analyse the design space of flow matching models for
cell microscopy images, finding that many popular techniques can add unnecessary complexity and may even hurt performance. We develop a simple, stable, and scalable recipe which we use to train our generative microscopy model. We scale training compute to an order of magnitude larger than prior methods, saturating existing public
benchmarks with approximately two-fold FID and ten-fold KID improvements over prior methods. We report an extended battery of metrics, finding that improved evaluation encoders can alleviate the saturation problem. Our methods represent progress towards high-fidelity virtual microscopy assays for tasks such as virtual screening, batch effect correction, and data enrichment.
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All authors Charles Jones, Emmanuel Noutahi, Zeinab Navidi, Alessandro Palma, Kian Kenyon-Dean, Jason Hartford, Cian Eastwood
Computer Vision
Confidence Matters: Uncertainty Quantification and Precision Assessment of Deep Learning-based CMR Biomarker Estimates Using Scan-rescan Data
Dewmini Hasara Wickremasinghe, Michelle Gibogwe, Andrew Bell et al. (+6 more)
The performance of deep learning (DL) methods for the analysis of cine cardiovascular
magnetic resonance (CMR) is typically assessed in terms of accuracy, overlooking precision. In this work, uncertainty estimation techniques, namely deep ensemble, test-time augmentation, and Monte Carlo dropout, are applied to a state-of-the-art DL pipeline for cardiac functional biomarker estimation, and new distribution-based metrics are proposed for the assessment of biomarker precision. The model achieved high accuracy (average Dice 87%) and point estimate precision on two external validation scan-rescan CMR
datasets. However, distribution-based metrics showed that the overlap between scan/rescan confidence intervals was >50% in less than 45% of the cases. Statistical similarity tests between scan and rescan biomarkers also resulted in significant differences for over 65% of the cases. We conclude that, while point estimate metrics might suggest good performance, distributional analyses reveal lower precision, highlighting the need to use more representative metrics to assess scan-rescan agreement.
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All authors Dewmini Hasara Wickremasinghe, Michelle Gibogwe, Andrew Bell, Esther Puyol-Antón, Muhummad Sohaib Nazir, Reza Razavi, Bruno Paun, Paul Aljabar, Andrew P. King
Computer Vision · Cross-listed from cs.RO
ReMemNav: Memory-Based Decision Correction and Target Verification for Zero-Shot Object Navigation
Feng Wu, Wei Zuo, Wenliang Yang et al. (+3 more)
Zero-shot object
navigation requires
agents to locate unseen targets in unfamiliar environments without prior maps or task-specific training. Despite the commonsense
reasoning ability of
vision-
language models (VLMs), existing mapless navigators often suffer from repeated exploration and premature stopping caused by limited historical context and false-positive target predictions. We propose ReMemNav, a training-free framework that combines lightweight semantic grounding,
memory-based decision correction, and target verification. RAM-derived semantic priors guide panoramic direction selection, while a geometry-triggered correction mechanism retrieves historical scene descriptions when the selected direction points toward previously visited regions. When a target is predicted to be visible, ReMemNav verifies the corresponding single-view observation before the final approach. Depth-based action sampling then converts the high-level decision into collision-free motion. Experiments on HM3D and MP3D show that ReMemNav achieves higher success rates and path efficiency than existing training-free zero-shot baselines. Specifically, ReMemNav achieves absolute SR/SPL gains of 1.7%/5.3% on HM3D v0.1, 18.2%/11.1% on HM3D v0.2, and 8.7%/6.9% on MP3D.
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All authors Feng Wu, Wei Zuo, Wenliang Yang, Jun Xiao, Yang Liu, Xinhua Zeng
Computer Vision
Brain-Inspired Multimodal Spiking Neural Network for Image-Text Retrieval
Xintao Zong, Xian Zhong, Wenxuan Liu et al. (+3 more)
Spiking neural networks (SNNs) have recently shown strong potential in unimodal visual and textual tasks, yet building a directly trained, low-energy, and high-performance SNN for
multimodal applications such as image-text
retrieval (ITR) remains highly challenging. Existing artificial neural network (ANN)-based methods often pursue richer unimodal semantics using deeper and more complex architectures, while overlooking cross-modal interaction,
retrieval latency, and energy efficiency. To address these limitations, we present a
brain-inspired Cross-Modal Spike Fusion network (CMSF) and apply it to ITR for the first time. The proposed spike fusion mechanism integrates unimodal features at the spike level, generating enhanced
multimodal representations that act as soft supervisory signals to refine unimodal spike
embeddings, effectively mitigating semantic loss within CMSF. Despite requiring only two time steps, CMSF achieves top-tier
retrieval accuracy, surpassing state-of-the-art ANN counterparts while maintaining exceptionally low energy consumption and high
retrieval speed. This work marks a significant step toward
multimodal SNNs, offering a
brain-inspired framework that unifies temporal dynamics with cross-modal
alignment and provides new insights for future spiking-based
multimodal research. The code is available at https://github.com/zxt6174/CMSF.
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All authors Xintao Zong, Xian Zhong, Wenxuan Liu, Jianhao Ding, Zhaofei Yu, Tiejun Huang
Computer Vision · Cross-listed from eess.IV
Beyond Benchmarks: A Framework for Post Deployment Validation of CT Lung Nodule Detection AI
Daniel Soliman
Background: Artificial intelligence (AI) assisted lung nodule detection systems are increasingly deployed in clinical settings without site-specific validation. Performance reported under
benchmark conditions may not reflect real-world behavior when acquisition parameters differ from training data. Purpose: To propose and demonstrate a physics-guided framework for evaluating the sensitivity of a deployed lung nodule detection model to systematic variation in CT acquisition parameters. Methods: Twenty-one cases from the publicly available LIDC-IDRI
dataset were evaluated using a MONAI RetinaNet model pretrained on LUNA16 (fold 0, no fine-tuning). Five imaging conditions were tested: baseline, 25% dose reduction, 50% dose reduction, 3 mm slice thickness, and 5 mm slice thickness. Dose reduction was simulated via image-domain Gaussian noise; slice thickness via moving average along the z-axis. Detection sensitivity was computed at a confidence threshold of 0.5 with a 15 mm matching criterion. Results: Baseline sensitivity was 45.2% (57/126 consensus nodules). Dose reduction produced slight degradation: 41.3% at 25% dose and 42.1% at 50% dose. The 5 mm slice thickness condition produced a marked drop to 26.2% - a 19 percentage point reduction representing a 42% relative decrease from baseline. This finding was consistent across confidence thresholds from 0.1 to 0.9. Per-case analysis revealed heterogeneous performance including two cases with complete detection failure at baseline. Conclusion: Slice thickness represents a more fundamental constraint on AI detection performance than image noise under the conditions tested. The proposed framework is reproducible, requires no proprietary scanner data, and is designed to serve as the basis for ongoing post-deployment QA in resource-constrained environment.
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Computer Vision
HighlightBench: Benchmarking Markup-Driven Table Reasoning in Scientific Documents
Lexin Wang, Shenghua Liu, Yiwei Wang et al. (+5 more)
Visual markups such as highlights, underlines, and bold text are common in table-centric documents. Although
multimodal large
language models (MLLMs) have made substantial progress in document understanding, their ability to treat such cues as explicit logical directives remains under-explored. More importantly, existing evaluations cannot distinguish whether a model fails to see the markup or fails to reason with it. This creates a key blind spot in assessing markup-conditioned behavior over tables. To address this gap, we introduce HighlightBench, a diagnostic
benchmark for markup-driven table understanding that decomposes evaluation into five task families: Markup Grounding, Constrained
Retrieval, Local Relations, Aggregation & Comparison, and Consistency & Missingness. We further provide a reference pipeline that makes intermediate decisions explicit, enabling reproducible baselines and finer-grained attribution of errors along the perception-to-execution chain. Experiments show that even strong models remain unstable when visual cues must be consistently aligned with symbolic
reasoning under structured output constraints.
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All authors Lexin Wang, Shenghua Liu, Yiwei Wang, Yujun Cai, Yuyao Ge, Jiayu Yao, Jiafeng Guo, Xueqi Cheng
Computer Vision
Can We Change "Stroke Size" to Improve Flow Matching?
Yunwei Bai, Ying Kiat Tan and Tsuhan Chen
Deep-learning-based image generation models are useful but difficult to train. Inspired by the varying stroke sizes used in human painting, we hypothesize that applying “rough strokes” can attenuate prediction biases and improve flow-matching image generation. We develop MultiStroke, which progressively varies the spatial granularity of the clean-signal component in the velocity regression target. A lightweight corrector aligns adjacent roughness levels during sampling. On ImageNet-1K, MultiStroke reduces FID by $22.5\%$ relative to a matched flow-matching baseline, despite the limited corrector capacity. Code is available at https://github.com/DaChengRuoQue/MultiStroke-Code. A quick
video explanation is available at https://dachengruoque.github.io/MultiStroke-Web/.
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Computer Vision
RatSeizure: A Benchmark and Saliency-Context Transformer for Rat Seizure Localization
Ting Yu Tsai, An Yu, Lucy Lee et al. (+5 more)
Animal models, particularly rats, play a critical role in seizure research for studying epileptogenesis and treatment response. However, progress is limited by the lack of
datasets with precise temporal annotations and standardized evaluation protocols. Existing animal behavior
datasets often have limited accessibility, coarse labeling, and insufficient temporal localization of clinically meaningful events. To address these limitations, we introduce RatSeizure, the first publicly
benchmark for fine-grained seizure behavior analysis. The
dataset consists of recorded clips annotated with seizure-related action units and temporal boundaries, enabling both behavior classification and temporal localization. We further propose RaSeformer, a saliency-context
Transformer for temporal action localization that highlights behavior-relevant context while suppressing redundant cues. Experiments on RatSeizure show that RaSeformer achieves strong performance and provides a competitive reference model for this challenging task. We also establish standardized
dataset splits and evaluation protocols to support reproducible benchmarking.
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All authors Ting Yu Tsai, An Yu, Lucy Lee, Felix X.-F. Ye, Damian S. Shin, Tzu-Jen Kao, Xin Li, Ming-Ching Chang
Computer Vision
Limits of Spatial Imagery Reasoning in Frontier LLM Models
Sergio Y. Hayashi and Nina S. T. Hirata
Large
Language Models (LLMs) have demonstrated impressive
reasoning capabilities, yet they struggle with spatial tasks that require mental simulation, such as mental rotation. This paper investigates whether equipping an LLM with an external “Imagery Module” – a tool capable of rendering and rotating 3D models – can bridge this gap, functioning as a “cognitive prosthetic.” We conducted experiments using a dual-module architecture in which a
reasoning module (an MLLM) interacts with an imagery module on 3D model rotation tasks. Performance was lower than expected, with accuracy reaching at most 62.5%. Further investigation suggests that even when the burden of maintaining and manipulating a holistic 3D state is outsourced, the system still fails. This reveals that current frontier models lack the foundational visual-spatial primitives required to interface with imagery. Specifically, they lack: (1) the low-level sensitivity to extract spatial signals such as (a) depth, (b) motion, and (c) short-horizon dynamic prediction; and (2) the capacity to reason contemplatively over images, dynamically shifting visual focus and balancing imagery with symbolic and associative information.
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Computer Vision
A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning
Changyu Liu, James Chenhao Liang, Wenhao Yang et al. (+6 more)
Diffusion models have significantly reshaped the field of generative artificial intelligence and are now increasingly explored for their capacity in discriminative representation learning.
Diffusion Transformer (DiT) has recently gained
attention as a promising alternative to conventional U-Net-based
diffusion models, demonstrating a promising avenue for downstream discriminative tasks via generative pre-training. However, its current training efficiency and representational capacity remain largely constrained due to the inadequate timestep searching and insufficient exploitation of DiT-specific feature representations. In light of this view, we introduce Automatically Selected Timestep (A-SelecT) that dynamically pinpoints DiT's most information-rich timestep from the selected
transformer feature in a single run, eliminating the need for both computationally intensive exhaustive timestep searching and suboptimal discriminative feature selection. Extensive experiments on classification and
segmentation benchmarks demonstrate that DiT, empowered by A-SelecT, surpasses all prior
diffusion-based attempts efficiently and effectively.
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All authors Changyu Liu, James Chenhao Liang, Wenhao Yang, Yiming Cui, Jinghao Yang, Tianyang Wang, Qifan Wang, Dongfang Liu, Cheng Han
Computer Vision
OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding
Xiaoyu Tang, Jun Dong, Jintao Cheng et al. (+1 more)
Remote sensing visual grounding (RSVG) aims to localize specific targets in remote sensing images using natural
language expressions. However, existing methods are restricted to single-sensor domains, i.e., either optical or synthetic aperture radar (SAR), limiting their real-world applicability. In this paper, we introduce the Cross-Domain RSVG (CD-RSVG) task and construct OptSAR-RSVG, the first large-scale
benchmark dataset for this setting. To tackle the challenges of cross-domain feature modeling, computational inefficiency, and fine-grained semantic discrimination, we propose OptiSAR-Net++. Our framework features a patch-level Low-Rank Adaptation Mixture of Experts (PL-MoE) for efficient cross-domain feature decoupling. To mitigate the substantial computational overhead of
Transformer decoding frameworks, we adopt a CLIP-based contrastive paradigm and further incorporate dynamic
adversarial negative sampling, thereby transforming generative regression into an efficient cross-modal matching process. Additionally, a text-guided dual-gate fusion module (TGDF-SSA) and a region-aware auxiliary head are introduced to enhance semantic-visual
alignment and spatial modeling. Extensive experiments demonstrate that OptiSAR-Net++ achieves SOTA performance on both OptSAR-RSVG and DIOR-RSVG
benchmarks, offering significant advantages in localization accuracy and efficiency. The model and
dataset have been made publicly available at https://github.com/JunDong-dev/OptiSAR-Net-PlusPlus.
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All authors Xiaoyu Tang, Jun Dong, Jintao Cheng, Rui Fan
Computer Vision · Cross-listed from cs.AI
How Far Are Vision-Language Models from Constructing the Real World? A Benchmark for Physical Generative Reasoning
Luyu Yang, Yutong Dai, An Yan et al. (+3 more)
The physical world is not merely visual; it is governed by rigorous structural and procedural constraints. Yet, the evaluation of
vision-
language models (VLMs) remains heavily skewed toward perceptual realism, prioritizing the generation of visually plausible 3D layouts, shapes, and appearances. Current
benchmarks rarely test whether models grasp the step-by-step processes and physical dependencies required to actually build these artifacts, a capability essential for automating design-to-construction pipelines. To address this, we introduce DreamHouse, a novel
benchmark for physical generative
reasoning: the capacity to synthesize artifacts that concurrently satisfy
geometric, structural, constructability, and code-compliance constraints. We ground this
benchmark in residential timber-frame construction, a domain with fully codified engineering standards and objectively verifiable correctness. We curate over 26,000 structures spanning 13 architectural styles, ach verified to construction-document standards (LOD 350) and develop a deterministic 10-test structural validation framework. Unlike static
benchmarks that assess only final outputs, DreamHouse supports iterative agentic interaction. Models observe intermediate build states, generate construction actions, and receive structured environmental feedback, enabling a fine-grained evaluation of planning, structural
reasoning, and self-correction. Extensive experiments with state-of-the-art VLMs reveal substantial capability gaps that are largely invisible on existing leaderboards. These findings establish physical validity as a critical evaluation axis orthogonal to visual realism, highlighting physical generative
reasoning as a distinct and underdeveloped frontier in
multimodal intelligence. Available at https://luluyuyuyang.github.io/dreamhouse
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All authors Luyu Yang, Yutong Dai, An Yan, Viraj Prabhu, Ran Xu, Zeyuan Chen
Computer Vision · Cross-listed from cs.CR
AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective
Zhenyi Wang and Siyu Luan
As machine learning (ML) systems expand in both scale and functionality, the security landscape has become increasingly complex, with a proliferation of attacks and defenses. However, existing studies largely treat these threats in isolation, lacking a coherent framework to expose their shared principles and interdependencies. This fragmented view hinders systematic understanding and limits the design of comprehensive defenses. Crucially, the two foundational assets of ML – data and models – are no longer independent; vulnerabilities in one directly compromise the other. The absence of a holistic framework leaves open questions about how these bidirectional risks propagate across the ML pipeline. To address this critical gap, we propose a unified closed-loop threat taxonomy that explicitly frames model-data interactions along four directional axes. Our framework offers a principled lens for analyzing and defending foundation models. The resulting four classes of security threats represent distinct but interrelated categories of attacks: (1) Data$\rightarrow$Data (D$\rightarrow$D): including data decryption attacks and watermark removal attacks; (2) Data$\rightarrow$Model (D$\rightarrow$M): including poisoning, harmful fine-tuning attacks, and jailbreak attacks; (3) Model$\rightarrow$Data (M$\rightarrow$D): including model inversion, membership inference attacks, and training data extraction attacks; (4) Model$\rightarrow$Model (M$\rightarrow$M): including model extraction attacks. Our unified framework elucidates the underlying connections among these security threats and establishes a foundation for developing scalable, transferable, and cross-modal security strategies, particularly within the landscape of foundation models.
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Computer Vision
Towards automatic smoke detector inspection: Recognition of the smoke detectors in industrial facilities and preparation for future drone integration
Lukas Kratochvila, Jakub Stefansky, Simon Bilik et al. (+6 more)
Fire safety consists of a complex pipeline, and it is a very important topic of concern. One of its frontal parts are the smoke detectors, which are supposed to provide an alarm prior to a massive fire appears. As they are often difficult to reach due to high ceilings or problematic locations, an automatic inspection system would be very beneficial as it could allow faster revisions, prevent workers from dangerous work in heights, and make the whole process cheaper. In this study, we present the smoke detector recognition part of the automatic inspection system, which could easily be integrated to the drone system. As part of our research, we compare two popular convolutional-based object detectors YOLOv11 and SSD widely used on embedded devices together with the state-of-the-art
transformer-based RT-DETRv2 with the backbones of different sizes. Due to a complicated way of collecting a sufficient amount of data for training in the real-world environment, we also compare several training strategies using the real and semi-synthetic data together with various augmentation methods. To achieve a robust testing, all models were evaluated on two test
datasets with an expected and difficult appearance of the smoke detectors including motion blur, small resolution, or not complete objects. The best performing detector is the YOLOv11n, which reaches the average mAP@0.5 score of 0.884. Our code, pretrained models and
dataset are publicly available.
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All authors Lukas Kratochvila, Jakub Stefansky, Simon Bilik, Robert Rous, Tomas Zemcik, Michal Wolny, Frantisek Rusnak, Ondrej Cech, Karel Horak
Computer Vision · Cross-listed from cs.HC
Gaze patterns predict preference and confidence in pairwise AI image evaluation
Nikolas Papadopoulos, Shreenithi Navaneethan, Sheng Bai et al. (+2 more)
Preference learning methods, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), rely on pairwise human judgments, yet little is known about the cognitive processes underlying these judgments. We investigate whether eye-tracking can reveal preference formation during pairwise AI-generated image evaluation. Thirty participants completed 1,800 trials while their gaze was recorded. We replicated the gaze cascade effect, with gaze shifting toward chosen images approximately one second before the decision. Cascade dynamics were consistent across confidence levels. Gaze features predicted binary choice (68% accuracy), with chosen images receiving more dwell time, fixations, and revisits. Gaze transitions distinguished high-confidence from uncertain decisions (66% accuracy), with low-confidence trials showing more image switches per second. These results show that gaze patterns predict both choice and confidence in pairwise image evaluations, suggesting that eye-tracking provides implicit signals relevant to the quality of preference annotations.
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All authors Nikolas Papadopoulos, Shreenithi Navaneethan, Sheng Bai, Ankur Samanta, Paul Sajda
Computer Vision
CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning
Jinkui Hao, Gorkem Durak, Halil Ertugrul Aktas et al. (+4 more)
Coronary artery disease, a leading cause of cardiovascular mortality worldwide, can be assessed non-invasively by coronary computed
tomography angiography (CCTA). Although deep learning has advanced automated CCTA analysis, clinical translation remains constrained by the scarcity of expert-annotated data and by the spatial sparsity of coronary pathology, which occupies only a small fraction of each scan. Widely used label-free pretraining strategies, such as masked image modeling and contrastive learning, optimize for global anatomical reconstruction and tend to under-represent these tiny localized pathological features. Here we present CORA, an annotation-efficient model for comprehensive coronary artery disease assessment. Rather than reconstructing background anatomy, CORA learns from volumetric CCTA through a synthesis-driven self-supervised strategy: an anatomy-guided engine inserts diverse synthetic calcified and non-calcified lesions into unlabeled scans, reframing pretraining as an abnormality-detection task that biases representation learning toward clinically relevant disease features. We pretrained CORA on 10,138 unlabeled CCTA volumes and evaluated it across
datasets from nine independent hospitals. Across plaque characterization, stenosis detection, and coronary artery
segmentation, CORA consistently outperformed strong self-supervised pretraining baselines, with the largest gains on external multi-center data, indicating robust generalization under distributional shift. Coupling the imaging encoder with structured clinical variables further enabled near-term major adverse cardiac event (MACE) risk stratification. Our results show that pathology-centric, synthesis-driven pretraining is an effective and scalable strategy for annotation-efficient coronary artery disease assessment from CCTA.
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All authors Jinkui Hao, Gorkem Durak, Halil Ertugrul Aktas, Ulas Bagci, Bradley D. Allen, Nilay S. Shah, Bo Zhou
Computer Vision
NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders
Katarina Trojachanec Dineva, Stefan Andonov, Ilinka Ivanoska et al. (+5 more)
Recent advances in
multimodal large
language models enable new possibilities for image-based decision support. However, their reliability and operational trade-offs in neuroimaging remain insufficiently understood. We present a comprehensive benchmarking study of
vision-enabled large
language models for 2D neuroimaging using curated MRI and CT
datasets covering multiple sclerosis, stroke,
brain tumors, other abnormalities, and normal controls. Models are required to generate multiple outputs simultaneously, including diagnosis, diagnosis subtype, imaging modality, specialized sequence, and anatomical plane. Performance is evaluated across four directions: discriminative classification with abstention, calibration, structured-output validity, and computational efficiency. A multi-phase framework ensures fair comparison while controlling for selection bias. Across twenty frontier
multimodal models, the results show that technical imaging attributes such as modality and plane are nearly solved, whereas diagnostic
reasoning, especially subtype prediction, remains challenging. Tumor classification emerges as the most reliable task, stroke is moderately solvable, while multiple sclerosis and rare abnormalities remain difficult. Few-shot prompting improves performance for several models but increases
token usage, latency, and cost. Gemini-2.5-Pro and GPT-5-Chat achieve the strongest overall diagnostic performance, while Gemini-2.5-Flash offers the best efficiency-performance trade-off. Among open-weight architectures, MedGemma-1.5-4B demonstrates the most promising results, as under few-shot prompting, it approaches the zero-shot performance of several proprietary models, while maintaining perfect structured output. These findings provide practical insights into performance, reliability, and efficiency trade-offs, supporting standardized evaluation of
multimodal LLMs in neuroimaging.
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All authors Katarina Trojachanec Dineva, Stefan Andonov, Ilinka Ivanoska, Ivan Kitanovski, Sasho Gramatikov, Tamara Kostova, Monika Simjanoska Misheva, Kostadin Mishev
Computer Vision
WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching
Yihan Wang and Jia Deng
We introduce WAFT-Stereo, a simple and effective warping-based method for stereo matching. WAFT-Stereo demonstrates that cost volumes, a common design used in many leading methods, are not necessary for strong performance and can be replaced by warping with improved efficiency. WAFT-Stereo ranks first on ETH3D (BP-0.5), Middlebury (RMSE), and KITTI (all metrics), reducing the zero-shot error by 81% on ETH3D, while being 1.8-6.7x faster than competitive methods. Code and model weights are available at https://github.com/princeton-vl/WAFT-Stereo.
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Computer Vision
DCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation
Junyi Ouyang, Wenbin Teng, Gonglin Chen et al. (+2 more)
Long-trajectory
video generation is a crucial yet challenging task for world modeling primarily due to the limited scalability of existing
video diffusion models (VDMs). Autoregressive models, while offering infinite rollout, suffer from visual drift and poor controllability. To address these issues, we propose DCARL, a novel divide-and-conquer, autoregressive framework that effectively combines the structural stability of the divide-and-conquer scheme with the high-fidelity generation of VDMs. Our approach first employs a dedicated Keyframe Generator trained without temporal compression to establish long-range, globally consistent structural anchors. Subsequently, an Interpolation Generator synthesizes the dense frames in an autoregressive manner with overlapping segments, utilizing the keyframes for global context and a single clean preceding frame for local coherence. Trained on a large-scale internet long trajectory
video dataset, our method achieves superior performance in both visual quality (lower FID and FVD) and camera adherence (lower ATE and ARE) compared to state-of-the-art autoregressive and divide-and-conquer baselines, demonstrating stable and high-fidelity generation for long trajectory
videos up to 32 seconds in length.
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All authors Junyi Ouyang, Wenbin Teng, Gonglin Chen, Yajie Zhao, Haiwei Chen
Computer Vision
Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting
Alabi Mehzabin Anisha, Guangjing Wang and Sriram Chellappan
State-of-the-art crowd counting and localization are primarily modeled using two paradigms: density maps and point regression. Given the field's security ramifications, there is active interest in model robustness against
adversarial attacks. Recent studies have demonstrated transferability across density-map-based approaches via
adversarial patches, but cross-paradigm attacks (i.e., across both density map-based models and point regression-based models) remain unexplored. We introduce a novel
adversarial framework that compromises both density map and point regression architectural paradigms through a comprehensive multi-task loss optimization. For point-regression models, we employ scene-density-specific high-confidence logit suppression; for density-map approaches, we use peak-targeted density map suppression. Both are combined with model-agnostic perceptual constraints to ensure that perturbations are effective and imperceptible to the human eye. Extensive experiments demonstrate the effectiveness of our attack, achieving on average a 7X increase in Mean Absolute Error compared to clean images while maintaining competitive visual quality, and successfully transferring across seven state-of-the-art crowd models with transfer ratios ranging from 0.55 to 1.69. Our approach strikes a balance between attack effectiveness and imperceptibility compared to state-of-the-art transferable attack strategies. The source code is available at https://github.com/simurgh7/CrowdGen
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