Computer Vision
Near-perfect photo-ID of the Hula painted frog with zero-shot deep local-feature matching
Maayan Yesharim, R. G. Bina Perl, Uri Roll et al. (+3 more)
Accurate individual identification is essential for monitoring rare amphibians, yet invasive marking is often unsuitable for critically endangered species. We evaluate state-of-the-art computer-
vision methods for photographic re-identification of the Hula painted frog (Latonia nigriventer) using 1,233 ventral images from 191 individuals collected during 2013-2020 capture-recapture surveys. We compare deep local-feature matching in a zero-shot setting with deep global-feature
embedding models. The local-feature pipeline achieves 98% top-1 closed-set identification accuracy, outperforming all global-feature models; fine-tuning improves the best global-feature model to 60% top-1 (91% top-10) but remains below local matching. To combine scalability with accuracy, we implement a two-stage workflow in which a fine-tuned global-feature model retrieves a short candidate list that is re-ranked by local-feature matching, reducing end-to-end runtime from 6.5-7.8 hours to ~38 minutes while maintaining ~96% top-1 closed-set accuracy on the labeled
dataset. Separation of match scores between same- and different-individual pairs supports thresholding for open-set identification, enabling practical handling of novel individuals. We deploy this pipeline as a web application for routine field use, providing rapid, standardized, non-invasive identification to support conservation monitoring and capture-recapture analyses. Overall, in this species, zero-shot deep local-feature matching outperformed global-feature
embedding and provides a strong default for photo-identification.
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All authors Maayan Yesharim, R. G. Bina Perl, Uri Roll, Sarig Gafny, Eli Geffen, Yoav Ram
Computer Vision
DentalX: Context-Aware Dental Disease Detection with Radiographs
Zhi Qin Tan, Xiatian Zhu, Owen Addison et al. (+1 more)
Diagnosing dental diseases from radiographs is time-consuming and challenging due to the subtle nature of diagnostic evidence. Existing methods, which rely on object detection models designed for natural images with more distinct target patterns, struggle to detect dental diseases that present with far less visual support. To address this challenge, we propose DentalX, a novel context-aware dental disease detection approach that leverages oral structure information to mitigate the visual ambiguity inherent in radiographs. Specifically, we introduce a structural context extraction module that learns an auxiliary task: semantic
segmentation of dental anatomy. The module extracts meaningful structural context and integrates it into the primary disease detection task to enhance the detection of subtle dental diseases. Extensive experiments on a dedicated
benchmark demonstrate that DentalX significantly outperforms prior methods in both tasks. This mutual benefit arises naturally during model optimization, as the correlation between the two tasks is effectively captured. Our code is available at https://github.com/zhiqin1998/DentYOLOX.
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All authors Zhi Qin Tan, Xiatian Zhu, Owen Addison, Yunpeng Li
Computer Vision
Aggregating Diverse Cue Experts for AI-Generated Image Detection
Lei Tan, Shuwei Li, Mohan Kankanhalli et al. (+1 more)
The rapid emergence of image synthesis models poses challenges to the generalization of AI-generated image detectors. However, existing methods often rely on model-specific features, leading to overfitting and poor generalization. In this paper, we introduce the Multi-Cue Aggregation Network (MCAN), a novel framework that integrates different yet complementary cues in a unified network. MCAN employs a mixture-of-encoders adapter to dynamically process these cues, enabling more adaptive and robust feature representation. Our cues include the input image itself, which represents the overall content, and high-frequency components that emphasize edge details. Additionally, we introduce a Chromatic Inconsistency (CI) cue, which normalizes intensity values and captures noise information introduced during the image acquisition process in real images, making these noise patterns more distinguishable from those in AI-generated content. Unlike prior methods, MCAN's novelty lies in its unified multi-cue aggregation framework, which integrates spatial, frequency-domain, and chromaticity-based information for enhanced representation learning. These cues are intrinsically more indicative of real images, enhancing cross-model generalization. Extensive experiments on the GenImage, Chameleon, and UniversalFakeDetect
benchmark validate the state-of-the-art performance of MCAN. In the GenImage
dataset, MCAN outperforms the best state-of-the-art method by up to 7.4% in average ACC across eight different image generators.
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All authors Lei Tan, Shuwei Li, Mohan Kankanhalli, Robby T. Tan
Computer Vision
An Example for Domain Adaptation Using CycleGAN
Yanhua Zhao
Cycle-Consistent
Adversarial Network (CycleGAN) is very promising in domain adaptation. In this report, an example in medical domain will be explained. We present struecture of a CycleGAN model for unpaired image-to-image translation from microscopy to pseudo H&E stained histopathology images.
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Computer Vision · Cross-listed from eess.IV
M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding
Juntao Jiang, Jiangning Zhang, Yali Bi et al. (+7 more)
Chain-of-Thought (CoT)
reasoning has proven effective in enhancing large
language models by encouraging step-by-step intermediate
reasoning, and recent advances have extended this paradigm to
Multimodal Large
Language Models (MLLMs). In the medical domain, where diagnostic decisions depend on nuanced visual cues and sequential
reasoning, CoT aligns naturally with clinical thinking processes. However, current
benchmarks for medical image understanding generally focus on the final answer while ignoring the
reasoning path. Such opaque
reasoning processes lack reliable bases for judgment, making it difficult to assist doctors in diagnosis. To address this gap, we introduce a new M3CoTBench
benchmark specifically designed to evaluate the correctness, efficiency, impact, and consistency of CoT
reasoning in medical image understanding. M3CoTBench features 1) a diverse, multi-level difficulty
dataset covering 24 examination types, 2) 13 varying-difficulty tasks, 3) a suite of CoT-specific evaluation metrics (correctness, efficiency, impact, and consistency) tailored to clinical
reasoning, and 4) a performance analysis of multiple MLLMs. M3CoTBench systematically evaluates CoT
reasoning across diverse medical imaging tasks, revealing current limitations of MLLMs in generating reliable and clinically interpretable
reasoning, and aims to foster the development of transparent, trustworthy, and diagnostically accurate AI systems for healthcare. Project page at https://juntaojianggavin.github.io/projects/M3CoTBench/.
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All authors Juntao Jiang, Jiangning Zhang, Yali Bi, Jinsheng Bai, Weixuan Liu, Weiwei Jin, Zhucun Xue, Yong Liu, Xiaobin Hu, Shuicheng Yan
Computer Vision · Cross-listed from eess.IV
A Single-Parameter Factor-Graph Image Prior
Tianyang Wang, Ender Konukoglu and Hans-Andrea Loeliger
We propose a novel piecewise smooth image model with piecewise constant local parameters that are automatically adapted to each image. Technically, the model is formulated in terms of factor graphs with NUP (normal with unknown parameters) priors, and the pertinent computations amount to iterations of conjugate-gradient steps and Gaussian message passing. The proposed model and
algorithms are demonstrated with applications to denoising and contrast enhancement.
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Computer Vision
ISLA: A U-Net for MRI-based acute ischemic stroke lesion segmentation with deep supervision, attention, domain adaptation, and ensemble learning
Vincent Roca, Martin Bretzner, Hilde Henon et al. (+3 more)
Accurate delineation of acute ischemic stroke lesions in MRI is a key component of stroke diagnosis and management. In recent years, deep learning models have been successfully applied to the automatic
segmentation of such lesions. While most proposed architectures are based on the U-Net framework, they primarily differ in their choice of loss functions and in the use of deep supervision, residual connections, and
attention mechanisms. Moreover, many implementations are not publicly available, and the optimal configuration for acute ischemic stroke (AIS) lesion
segmentation remains unclear. In this work, we introduce ISLA (Ischemic Stroke Lesion Analyzer), a new deep learning model for AIS lesion
segmentation from
diffusion MRI, trained on three multicenter databases totaling more than 1500 AIS participants. Through systematic optimization of the loss function, convolutional architecture, deep supervision, and
attention mechanisms, we developed a robust
segmentation framework. We further investigated unsupervised domain adaptation to improve generalization to an external clinical
dataset. ISLA outperformed two state-of-the-art approaches for AIS lesion
segmentation on an external test set. Codes and trained models will be made publicly available to facilitate reuse and reproducibility.
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All authors Vincent Roca, Martin Bretzner, Hilde Henon, Laurent Puy, Grégory Kuchcinski, Renaud Lopes
Computer Vision
Salience-SGG: Enhancing Unbiased Scene Graph Generation with Iterative Salience Estimation
Runfeng Qu, Ole Hall, Pia K Bideau et al. (+4 more)
Scene Graph Generation (SGG) suffers from a long-tailed distribution, where a few predicate classes dominate while many others are underrepresented, leading to biased models that underperform on rare relations. Unbiased-SGG methods address this issue by implementing debiasing strategies, but often at the cost of spatial understanding, resulting in an over-reliance on semantic priors. We introduce Salience-SGG, a novel framework featuring an Iterative Salience Decoder (ISD) that emphasizes triplets with salient spatial structures. To support this, we propose semantic-agnostic salience labels guiding ISD. Evaluations on Visual Genome, Open Images V6, and GQA-200 show that Salience-SGG achieves state-of-the-art performance and improves existing Unbiased-SGG methods in their spatial understanding as demonstrated by the Pairwise Localization Average Precision
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All authors Runfeng Qu, Ole Hall, Pia K Bideau, Julie Ouerfelli-Ethier, Martin Rolfs, Klaus Obermayer, Olaf Hellwich
Computer Vision · Cross-listed from cs.RO
Real-Time Localization Framework for Autonomous Basketball Robots
Naren Medarametla and Sreejon Mondal
Localization is a fundamental capability for autonomous
robots, enabling them to operate effectively in dynamic environments. In Robocon 2025, accurate and reliable localization is crucial for improving shooting precision, avoiding collisions with other
robots, and navigating the competition field efficiently. In this paper, we propose a hybrid localization
algorithm that integrates classical techniques with learning based methods that rely solely on visual data from the court's floor to achieve self-localization on the basketball field.
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Computer Vision · Cross-listed from q-bio.QM
Automated Lesion Segmentation of Stroke MRI Using nnU-Net: A Comprehensive External Validation Across Acute and Chronic Lesions
Tammar Truzman, Matthew A. Lambon Ralph and Ajay D. Halai
Accurate and generalisable
segmentation of stroke lesions from
magnetic resonance imaging (MRI) is essential for advancing clinical research, prognostic modelling, and personalised interventions. Although deep learning has improved automated lesion delineation, many existing models are optimised for narrow imaging contexts and generalise poorly to independent
datasets, modalities, and stroke stages. Here, we systematically evaluated stroke lesion
segmentation using the nnU-Net framework across multiple heterogeneous, publicly available MRI
datasets spanning acute and chronic stroke. Models were trained and tested on
diffusion-weighted imaging (DWI), fluid-attenuated inversion recovery (FLAIR), and T1-weighted MRI, and evaluated on independent
datasets. Across stroke stages, models showed robust generalisation, with
segmentation accuracy approaching reported inter-rater reliability. Performance varied with imaging modality and training data characteristics. In acute stroke, DWI-trained models consistently outperformed FLAIR-based models, with only modest gains from
multimodal combinations. In chronic stroke, increasing training set size improved performance, with diminishing returns beyond several hundred cases. Lesion volume was a key determinant of accuracy: smaller lesions were harder to segment, and models trained on restricted volume ranges generalised poorly. MRI image quality further constrained generalisability: models trained on lower-quality scans transferred poorly, whereas those trained on higher-quality data generalised well to noisier images. Discrepancies between predictions and reference masks were often attributable to limitations in manual annotations. Together, these findings show that automated lesion
segmentation can approach human-level performance while identifying key factors governing generalisability and informing the development of lesion
segmentation tools.
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Computer Vision · Cross-listed from cs.AI
MEMEWEAVER: Inter-Meme Graph Reasoning for Sexism and Misogyny Detection
Paolo Italiani, David Gimeno-Gomez, Luca Ragazzi et al. (+2 more)
Women are twice as likely as men to face online harassment due to their gender. Despite recent advances in
multimodal content moderation, most approaches still overlook the social dynamics behind this phenomenon, where perpetrators reinforce prejudices and group identity within like-minded communities. Graph-based methods offer a promising way to capture such interactions, yet existing solutions remain limited by heuristic graph construction, shallow modality fusion, and instance-level
reasoning. In this work, we present MemeWeaver, an end-to-end trainable
multimodal framework for detecting sexism and misogyny through a novel inter-meme graph
reasoning mechanism. We systematically evaluate multiple visual–textual fusion strategies and show that our approach consistently outperforms state-of-the-art baselines on the MAMI and EXIST
benchmarks, while achieving faster training convergence. Further analyses reveal that the learned graph structure captures semantically meaningful patterns, offering valuable insights into the relational nature of online hate.
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All authors Paolo Italiani, David Gimeno-Gomez, Luca Ragazzi, Gianluca Moro, Paolo Rosso
Computer Vision · Cross-listed from eess.IV
Region of interest detection for efficient aortic segmentation
Loris Giordano, Ine Dirks, Tom Lenaerts et al. (+1 more)
Thoracic aortic dissection and aneurysms are the most lethal diseases of the aorta. The major hindrance to treatment lies in the accurate analysis of the medical images. More particularly, aortic
segmentation of the 3D image is often tedious and difficult. Deep-learning-based
segmentation models are an ideal solution, but their inability to deliver usable outputs in difficult cases and their computational cost cause their clinical adoption to stay limited. This study presents an innovative approach for efficient aortic
segmentation using targeted region of interest (ROI) detection. In contrast to classical detection models, we propose a simple and efficient detection model that can be widely applied to detect a single ROI. Our detection model is trained as a multi-task model, using an encoder-decoder architecture for
segmentation and a fully connected network attached to the bottleneck for detection. We compare the performance of a one-step
segmentation model applied to a complete image, nnU-Net and our cascade model composed of a detection and a
segmentation step. We achieve a mean Dice similarity coefficient of 0.944 with over 0.9 for all cases using a third of the computing power. This simple solution achieves state-of-the-art performance while being compact and robust, making it an ideal solution for clinical applications.
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All authors Loris Giordano, Ine Dirks, Tom Lenaerts, Jef Vandemeulebroucke
Computer Vision
Além do Desempenho: Um Estudo da Confiabilidade de Detectores de Deepfakes
Lucas Lopes, Rayson Laroca and André Grégio
Deepfakes are synthetic media generated by artificial intelligence, with positive applications in education and creativity, but also serious negative impacts such as fraud, misinformation, and
privacy violations. Although detection techniques have advanced, comprehensive evaluation methods that go beyond classification performance remain lacking. This paper proposes a reliability assessment framework based on four pillars: transferability, robustness, interpretability, and computational efficiency. An analysis of five state-of-the-art methods revealed significant progress as well as critical limitations.
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Computer Vision · Cross-listed from astro-ph.IM
Blind Deconvolution in Astronomy: How Does a Standalone U-Net Perform?
Jean-Eric Campagne
Aims: This study investigates whether a U-Net architecture can perform standalone end-to-end blind deconvolution of astronomical images without any prior knowledge of the Point Spread Function (PSF) or noise characteristics. Our goal is to evaluate its performance against the number of training images, classical Tikhonov deconvolution and to assess its generalization capability under varying seeing conditions and noise levels. Methods: Realistic astronomical observations are simulated using the GalSim toolkit, incorporating random transformations, PSF convolution (accounting for both optical and atmospheric effects), and Gaussian white noise. A U-Net model is trained using a Mean Square Error (MSE) loss function on
datasets of varying sizes, up to 40,000 images of size 48x48 from the COSMOS Real
Galaxy Dataset. Performance is evaluated using PSNR, SSIM, and cosine similarity metrics, with the latter employed in a two-model framework to assess solution stability. Results: The U-Net model demonstrates effectiveness in blind deconvolution, with performance improving consistently as the training
dataset size increases, saturating beyond 5,000 images. Cosine similarity analysis reveals convergence between independently trained models, indicating stable solutions. Remarkably, the U-Net outperforms the oracle-like Tikhonov method in challenging conditions (low PSNR/medium SSIM). The model also generalizes well to unseen seeing and noise conditions, although optimal performance is achieved when training parameters include validation conditions. Experiments on synthetic $C^\alpha$ images further support the hypothesis that the U-Net learns a geometry-adaptive harmonic basis, akin to sparse representations observed in denoising tasks. These results align with recent mathematical insights into its adaptive learning capabilities.
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Computer Vision · Cross-listed from cs.RO
Shaoan Wang, Yuanfei Luo, Xingyu Chen et al. (+6 more)
VLA models have shown promising potential in
embodied navigation by unifying perception and planning while inheriting the strong generalization abilities of large VLMs. However, most existing VLA models rely on reactive mappings directly from observations to actions, lacking the explicit
reasoning capabilities and persistent
memory required for complex, long-horizon
navigation tasks. To address these challenges, we propose VLingNav, a VLA model for
embodied navigation grounded in linguistic-driven cognition. First, inspired by the dual-process theory of human cognition, we introduce an adaptive chain-of-thought mechanism, which dynamically triggers explicit
reasoning only when necessary, enabling the
agent to fluidly switch between fast, intuitive execution and slow, deliberate planning. Second, to handle long-horizon spatial dependencies, we develop a visual-assisted linguistic
memory module that constructs a persistent, cross-modal semantic
memory, enabling the
agent to recall past observations to prevent repetitive exploration and infer movement trends for dynamic environments. For the training recipe, we construct Nav-AdaCoT-2.9M, the largest
embodied navigation dataset with
reasoning annotations to date, enriched with adaptive CoT annotations that induce a
reasoning paradigm capable of adjusting both when to think and what to think about. Moreover, we incorporate an online expert-guided reinforcement learning stage, enabling the model to surpass pure imitation learning and to acquire more robust, self-explored
navigation behaviors. Extensive experiments demonstrate that VLingNav achieves state-of-the-art performance across a wide range of
embodied navigation benchmarks. Notably, VLingNav transfers to real-world robotic platforms in a zero-shot manner, executing various
navigation tasks and demonstrating strong cross-domain and cross-task generalization.
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All authors Shaoan Wang, Yuanfei Luo, Xingyu Chen, Aocheng Luo, Dongyue Li, Chang Liu, Sheng Chen, Yangang Zhang, Junzhi Yu
Computer Vision · Cross-listed from cs.LG
TRACE: Reconstruction-Based Anomaly Detection in Ensemble and Time-Dependent Simulations
Hamid Gadirov, Martijn Westra and Steffen Frey
Detecting anomalies in high-dimensional, time-dependent simulation data is challenging due to complex spatial and temporal dynamics. We study reconstruction-based anomaly detection for ensemble data from parameterized Kármán
vortex street simulations using convolutional autoencoders. We compare a 2D autoencoder operating on individual frames with a 3D autoencoder that processes short temporal stacks. The 2D model identifies localized spatial irregularities in single time steps, while the 3D model exploits spatio-temporal context to detect anomalous motion patterns and reduces redundant detections across time. We further evaluate volumetric time-dependent data and find that reconstruction errors are strongly influenced by the spatial distribution of mass, with highly concentrated regions yielding larger errors than dispersed configurations. Our results highlight the importance of temporal context for robust anomaly detection in dynamic simulations.
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Computer Vision
SafeRedir: Prompt Embedding Redirection for Robust Unlearning in Image Generation Models
Renyang Liu, Kangjie Chen, Han Qiu et al. (+4 more)
Image generation models (IGMs), while capable of producing impressive and creative content, often memorize a wide range of undesirable concepts from their training data, leading to the reproduction of unsafe content such as NSFW imagery and copyrighted artistic styles. Such behaviors pose persistent safety and compliance risks in real-world deployments and cannot be reliably mitigated by post-hoc filtering, owing to the limited robustness of such mechanisms and a lack of fine-grained semantic control. Recent unlearning methods seek to erase harmful concepts at the model level, which exhibit the limitations of requiring costly retraining, degrading the quality of benign generations, or failing to withstand
prompt paraphrasing and
adversarial attacks. To address these challenges, we introduce SafeRedir, a lightweight inference-time framework for robust unlearning via
prompt embedding redirection. Without modifying the underlying IGMs, SafeRedir adaptively routes unsafe
prompts toward safe semantic regions through
token-level interventions in the
embedding space. The framework comprises two core components: a latent-aware multi-modal safety classifier for identifying unsafe generation trajectories, and a
token-level delta generator for precise semantic redirection, equipped with auxiliary predictors for
token masking and adaptive scaling to localize and regulate the intervention. Empirical results across multiple representative unlearning tasks demonstrate that SafeRedir achieves effective unlearning capability, high semantic and perceptual preservation, robust image quality, and enhanced resistance to
adversarial attacks. Furthermore, SafeRedir generalizes effectively across a variety of
diffusion backbones and existing unlearned models, validating its plug-and-play compatibility and broad applicability. Code and data are available at https://github.com/ryliu68/SafeRedir.
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All authors Renyang Liu, Kangjie Chen, Han Qiu, Jie Zhang, Kwok-Yan Lam, Tianwei Zhang, See-Kiong Ng
Computer Vision · Cross-listed from cs.AI
ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios
António Loison, Quentin Macé, Antoine Edy et al. (+7 more)
Retrieval-Augmented Generation (RAG) pipelines must address challenges beyond simple single-document
retrieval, such as interpreting visual elements (tables, charts, images), synthesizing information across documents, and providing accurate source grounding. Existing
benchmarks fail to capture this complexity, often focusing on textual data, single-document comprehension, or evaluating
retrieval and generation in isolation. We introduce ViDoRe v3, a comprehensive
multimodal RAG
benchmark featuring multi-type queries over visually rich document corpora. It covers 10
datasets across diverse professional domains, comprising ~26,000 document pages paired with 3,099 human-verified queries, each available in 6
languages. Through 12,000 hours of human annotation effort, we provide high-quality annotations for
retrieval relevance, bounding box localization, and verified reference answers. Our evaluation of state-of-the-art RAG pipelines reveals that visual retrievers outperform textual ones, late-interaction models and textual reranking substantially improve performance, and hybrid or purely visual contexts enhance answer generation quality. However, current models still struggle with non-textual elements, open-ended queries, and fine-grained visual grounding. To encourage progress in addressing these challenges, the
benchmark is released under a commercially permissive license at https://hf.co/vidore.
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All authors António Loison, Quentin Macé, Antoine Edy, Victor Xing, Tom Balough, Gabriel Moreira, Bo Liu, Manuel Faysse, Céline Hudelot, Gautier Viaud
Computer Vision
SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning
Leo Fillioux, Omprakash Chakraborty, Ismail Ben Ayed et al. (+4 more)
With the increasing adoption of
vision-
language models (VLMs) in critical decision-making systems such as healthcare or autonomous driving, the calibration of their uncertainty estimates becomes paramount. Yet, this dimension has been largely underexplored in the VLM test-time
prompt-tuning (TPT) literature, which has predominantly focused on improving their discriminative performance. Recent state-of-the-art advocates for enforcing full orthogonality over pairs of text
prompt embeddings to enhance separability, and therefore calibration. Nevertheless, as we theoretically show in this work, the inherent gradients from fully orthogonal constraints will strongly push semantically related classes away, ultimately making the model overconfident. Based on our findings, we propose Semantic Orthogonal Calibration (SoC), a Huber-based regularizer that enforces smooth prototype separation while preserving semantic proximity, thereby improving calibration compared to prior orthogonality-based approaches. Across a comprehensive empirical validation, we demonstrate that SoC consistently improves calibration performance, while also maintaining competitive discriminative capabilities.
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All authors Leo Fillioux, Omprakash Chakraborty, Ismail Ben Ayed, Paul-Henry Cournède, Stergios Christodoulidis, Maria Vakalopoulou, Jose Dolz
Computer Vision · Cross-listed from cs.IR
VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking
Mark Rothermel, Marcus Kornmann, Marcus Rohrbach et al. (+1 more)
The growing scale of online misinformation urgently demands Automated Fact-Checking (AFC). Existing
benchmarks for evaluating AFC systems, however, are largely limited in terms of task scope, modalities, domain,
language diversity, realism, or coverage of misinformation types. Critically, they are static, thus subject to data leakage as their claims enter the pretraining corpora of LLMs. As a result,
benchmark performance no longer reliably reflects the actual ability to verify claims. We introduce Verified Theses and Statements (VeriTaS), the first dynamic
benchmark for
multimodal AFC, designed to remain robust under ongoing large-scale pretraining of foundation models. VeriTaS currently comprises 25,000 real-world claims from 104 professional fact-checking organizations across 54
languages, covering textual and audiovisual content. Claims are added quarterly via a fully automated seven-stage pipeline that normalizes claim formulation, retrieves original media, and maps heterogeneous expert verdicts to a novel, standardized, and disentangled scoring scheme with textual justifications. Through human evaluation, we demonstrate that the automated annotations closely match human judgments. We commit to updating VeriTaS in the future, establishing a leakage-resistant
benchmark, supporting meaningful AFC evaluation in the era of rapidly evolving foundation models. The code and data are publicly available under https://veritas.mai.informatik.tu-darmstadt.de .
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All authors Mark Rothermel, Marcus Kornmann, Marcus Rohrbach, Anna Rohrbach
Computer Vision
SfMamba: Efficient Source-Free Domain Adaptation via Selective Scan Modeling
Xi Chen, Hongxun Yao, Sicheng Zhao et al. (+3 more)
Source-free domain adaptation (SFDA) tackles the critical challenge of adapting source-pretrained models to unlabeled target domains without access to source data, overcoming data
privacy and storage limitations in real-world applications. However, existing SFDA approaches struggle with the trade-off between perception field and computational efficiency in domain-invariant feature learning. Recently, Mamba has offered a promising solution through its selective scan mechanism, which enables long-range dependency modeling with linear complexity. However, the Visual Mamba (i.e., VMamba) remains limited in capturing channel-wise frequency characteristics critical for domain
alignment and maintaining spatial robustness under significant domain shifts. To address these, we propose a framework called SfMamba to fully explore the stable dependency in source-free model transfer. SfMamba introduces Channel-wise Visual State-Space block that enables channel-sequence scanning for domain-invariant feature extraction. In addition, SfMamba involves a Semantic-Consistent Shuffle strategy that disrupts background patch sequences in 2D selective scan while preserving prediction consistency to mitigate error accumulation. Comprehensive evaluations across multiple
benchmarks show that SfMamba achieves consistently stronger performance than existing methods while maintaining favorable parameter efficiency, offering a practical solution for SFDA. Our code is available at https://github.com/chenxi52/SfMamba.
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All authors Xi Chen, Hongxun Yao, Sicheng Zhao, Jiankun Zhu, Jing Jiang, Kui Jiang
Computer Vision
Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas
Yaxi Chen, Simin Ni, Shuai Li et al. (+6 more)
For automated assessment of knee MRI scans, both accuracy and interpretability are essential for clinical use and adoption. Traditional radiomics rely on predefined features chosen at the population level; while more interpretable, they are often too restrictive to capture patient-specific variability and can underperform end-to-end deep learning (DL). To address this, we propose two complementary strategies that bring individuality and interpretability: radiomic fingerprints and healthy personas. First, a radiomic fingerprint is a dynamically constructed, patient-specific feature set derived from MRI. Instead of applying a uniform population-level signature, our model predicts feature relevance from a pool of candidate features and selects only those most predictive for each patient, while maintaining feature-level interpretability. This fingerprint can be viewed as a latent-variable model of feature usage, where an image-conditioned predictor estimates usage probabilities and a transparent logistic regression with global coefficients performs classification. Second, a healthy persona synthesises a pathology-free baseline for each patient using a
diffusion model trained to reconstruct healthy knee MRIs. Comparing features extracted from pathological images against their personas highlights deviations from normal anatomy, enabling intuitive, case-specific explanations of disease manifestations. We systematically compare fingerprints, personas, and their combination across three clinical tasks. Experimental results show that both approaches yield performance comparable to or surpassing state-of-the-art DL models, while supporting interpretability at multiple levels. Case studies further illustrate how these perspectives facilitate human-explainable biomarker discovery and pathology localisation.
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All authors Yaxi Chen, Simin Ni, Shuai Li, Shaheer U. Saeed, Aleksandra Ivanova, Rikin Hargunani, Jie Huang, Chaozong Liu, Yipeng Hu
Computer Vision
WaveFormer: Frequency-Time Decoupled Vision Modeling with Wave Equation
Zishan Shu, Juntong Wu, Wei Yan et al. (+5 more)
Vision modeling has advanced rapidly with
Transformers, whose
attention mechanisms capture visual dependencies but lack a principled account of how semantic information propagates spatially. We revisit this problem from a wave-based perspective: feature maps are treated as spatial signals whose
evolution over an internal propagation time (aligned with network depth) is governed by an underdamped wave equation. In this formulation, spatial frequency-from low-frequency global layout to high-frequency edges and textures-is modeled explicitly, and its interaction with propagation time is controlled rather than implicitly fixed. We derive a closed-form, frequency-time decoupled solution and implement it as the Wave Propagation Operator (WPO), a lightweight module that models global interactions in O(N log N) time-far lower than
attention. Building on WPO, we propose a family of WaveFormer models as drop-in replacements for standard ViTs and CNNs, achieving competitive accuracy across image classification, object detection, and semantic
segmentation, while delivering up to 1.6x higher throughput and 30% fewer FLOPs than
attention-based alternatives. Furthermore, our results demonstrate that wave propagation introduces a complementary modeling bias to heat-based methods, effectively capturing both global coherence and high-frequency details essential for rich visual semantics. Codes are available at: https://github.com/ZishanShu/WaveFormer.
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All authors Zishan Shu, Juntong Wu, Wei Yan, Xudong Liu, Hongyu Zhang, Chang Liu, Youdong Mao, Jie Chen
Computer Vision
MoCha:End-to-End Video Character Replacement without Structural Guidance
Zhengbo Xu, Jie Ma, Ziheng Wang et al. (+3 more)
Controllable
video character replacement with a user-provided identity remains a challenging problem due to the lack of paired
video data. Prior works have predominantly relied on a reconstruction-based paradigm that requires per-frame
segmentation masks and explicit structural guidance (e.g., skeleton, depth). This reliance, however, severely limits their generalizability in complex scenarios involving occlusions, character-object interactions, unusual poses, or challenging illumination, often leading to visual artifacts and temporal inconsistencies. In this paper, we propose MoCha, a pioneering framework that bypasses these limitations by requiring only a single arbitrary frame mask. To effectively adapt the multi-modal input condition and enhance facial identity, we introduce a condition-aware RoPE and employ an RL-based post-training stage. Furthermore, to overcome the scarcity of qualified paired-training data, we propose a comprehensive data construction pipeline. Specifically, we design three specialized
datasets: a high-fidelity rendered
dataset built with Unreal Engine 5 (UE5), an expression-driven
dataset synthesized by current portrait animation techniques, and an augmented
dataset derived from existing
video-mask pairs. Extensive experiments demonstrate that our method substantially outperforms existing state-of-the-art approaches. We will release the code to facilitate further research. Please refer to our project page for more details: orange-3dv-team.github.io/MoCha
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All authors Zhengbo Xu, Jie Ma, Ziheng Wang, Zhan Peng, Jun Liang, Jing Li
Computer Vision
REVNET: Rotation-Equivariant Point Cloud Completion via Vector Neuron Anchor Transformer
Zhifan Ni and Eckehard Steinbach
Incomplete point clouds captured by 3D sensors often result in the loss of both
geometric and semantic information. Most existing point cloud completion methods are built on rotation-variant frameworks trained with data in canonical poses, limiting their applicability in real-world scenarios. While data augmentation with random rotations can partially mitigate this issue, it significantly increases the learning burden and still fails to guarantee robust performance under arbitrary poses. To address this challenge, we propose the Rotation-Equivariant Anchor
Transformer (REVNET), a novel framework built upon the Vector
Neuron (VN) network for robust point cloud completion under arbitrary rotations. To preserve local details, we represent partial point clouds as sets of equivariant anchors and design a VN Missing Anchor
Transformer to predict the positions and features of missing anchors. Furthermore, we extend VN networks with a rotation-equivariant bias formulation and a ZCA-based layer normalization to improve feature expressiveness. Leveraging the flexible conversion between equivariant and invariant VN features, our model can generate point coordinates with greater stability. Experimental results show that our method outperforms state-of-the-art approaches on the synthetic MVP
dataset in the equivariant setting. On the real-world KITTI
dataset, REVNET delivers competitive results compared to non-equivariant networks, without requiring input pose
alignment. The source code will be released on GitHub under URL: https://github.com/nizhf/REVNET.
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Computer Vision
VideoHEDGE: Entropy-Based Hallucination Detection for Video-VLMs via Semantic Clustering and Spatiotemporal Perturbations
Sushant Gautam, Cise Midoglu, Vajira Thambawita et al. (+2 more)
Hallucinations in
video-capable
vision-
language models (
Video-VLMs) remain frequent and high-confidence, while existing uncertainty metrics often fail to align with correctness. We introduce VideoHEDGE, a modular framework for
hallucination detection in
video question answering that extends
entropy-based reliability estimation from images to temporally structured inputs. Given a
video-question pair, VideoHEDGE draws a baseline answer and multiple high-temperature generations from both clean clips and photometrically and spatiotemporally perturbed variants, then clusters the resulting textual outputs into semantic hypotheses using either Natural
Language Inference (NLI)-based or
embedding-based methods. Cluster-level probability masses yield three reliability scores: Semantic
Entropy (SE), RadFlag, and
Vision-Amplified Semantic
Entropy (VASE). We evaluate VideoHEDGE on the SoccerChat
benchmark using an LLM-as-a-judge to obtain binary
hallucination labels. Across three 7B
Video-VLMs (Qwen2-VL, Qwen2.5-VL, and a SoccerChat-finetuned model), VASE consistently achieves the highest ROC-AUC, especially at larger distortion budgets, while SE and RadFlag often operate near chance. We further show that
embedding-based clustering matches NLI-based clustering in detection performance at substantially lower computational cost, and that domain fine-tuning reduces
hallucination frequency but yields only modest improvements in calibration. The hedge-bench PyPI library enables reproducible and extensible benchmarking, with full code and experimental resources available at https://github.com/Simula/HEDGE#videohedge .
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All authors Sushant Gautam, Cise Midoglu, Vajira Thambawita, Michael A. Riegler, Pål Halvorsen
Computer Vision · Cross-listed from cs.RO
Keyframe-based Dense Mapping with the Graph of View-Dependent Local Maps
Krzysztof Zielinski and Dominik Belter
In this article, we propose a new keyframe-based mapping system. The proposed method updates local Normal Distribution Transform maps (NDT) using data from an RGB-D sensor. The
cells of the NDT are stored in 2D view-dependent structures to better utilize the properties and uncertainty model of RGB-D cameras. This method naturally represents an object closer to the camera origin with higher precision. The local maps are stored in the pose graph which allows correcting global map after loop closure detection. We also propose a procedure that allows merging and filtering local maps to obtain a global map of the environment. Finally, we compare our method with Octomap and NDT-OM and provide example applications of the proposed mapping method.
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Computer Vision
CD^2: Constrained Dataset Distillation for Few-Shot Class-Incremental Learning
Kexin Bao, Daichi Zhang, Hansong Zhang et al. (+3 more)
Few-shot class-incremental learning (FSCIL) receives significant
attention from the public to perform classification continuously with a few training samples, which suffers from the key catastrophic forgetting problem. Existing methods usually employ an external
memory to store previous knowledge and treat it with incremental classes equally, which cannot properly preserve previous essential knowledge. To solve this problem and inspired by recent
distillation works on knowledge transfer, we propose a framework termed Constrained
Dataset Distillation (\textbf{CD$^2$}) to facilitate FSCIL, which includes a
dataset distillation module (DDM) and a
distillation constraint module~(DCM). Specifically, the DDM synthesizes highly condensed samples guided by the classifier, forcing the model to learn compacted essential class-related clues from a few incremental samples. The DCM introduces a designed loss to constrain the previously learned class distribution, which can preserve distilled knowledge more sufficiently. Extensive experiments on three public
datasets show the superiority of our method against other state-of-the-art competitors.
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All authors Kexin Bao, Daichi Zhang, Hansong Zhang, Yong Li, Yutao Yue, Shiming Ge
Computer Vision
Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models
Tolgay Atinc Uzun, Dmitry Ignatov and Radu Timofte
Channel-configuration search, the optimization of layer specifications such as channel widths in deep neural networks, presents a combinatorial challenge constrained by tensor-shape compatibility and computational budgets. We investigate whether large
language models (LLMs) can support neural architecture search (NAS) by
reasoning over architectural code structures in ways that complement traditional search heuristics. We apply an LLM-driven NAS framework to channel-configuration search, formulating the task as conditional code generation in which the LLM refines architectural specifications using performance feedback. To address data scarcity, we generate a corpus of valid, shape-consistent architectures through abstract syntax tree (AST) mutations. Although these mutated networks are not necessarily optimized for performance, they provide structural examples that help the LLM learn executable architectural patterns and relate channel configurations to model performance. Experimental results on CIFAR-100 show that the closed-loop LLM improves upon the initial AST-generated architecture population under the same proxy-evaluation protocol. Our analysis further shows that the generated architectures reflect domain-specific design patterns, including non-standard channel widths and late-stage expansion, highlighting the potential of
language-driven design for code-level NAS. The code and
prompts are publicly available at https://github.com/ABrain-One/NN-GPT, and the generated deep neural networks are published at https://github.com/ABrain-One/NN-
Dataset under model names with the prefix ast-dimension-.
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Computer Vision
EfficientFSL: Enhancing Few-Shot Classification via Query-Only Tuning in Vision Transformers
Wenwen Liao, Hang Ruan, Jianbo Yu et al. (+3 more)
Large models such as
Vision Transformers (ViTs) have demonstrated remarkable superiority over smaller architectures like ResNet in few-shot classification, owing to their powerful representational capacity. However, fine-tuning such large models demands extensive GPU
memory and prolonged training time, making them impractical for many real-world low-resource scenarios. To bridge this gap, we propose EfficientFSL, a query-only fine-tuning framework tailored specifically for few-shot classification with ViT, which achieves competitive performance while significantly reducing computational overhead. EfficientFSL fully leverages the knowledge embedded in the pre-trained model and its strong comprehension ability, achieving high classification accuracy with an extremely small number of tunable parameters. Specifically, we introduce a lightweight trainable Forward Block to synthesize task-specific queries that extract informative features from the intermediate representations of the pre-trained model in a query-only manner. We further propose a Combine Block to fuse multi-layer outputs, enhancing the depth and robustness of feature representations. Finally, a Support-Query
Attention Block mitigates distribution shift by adjusting prototypes to align with the query set distribution. With minimal trainable parameters, EfficientFSL achieves state-of-the-art performance on four in-domain few-shot
datasets and six cross-domain
datasets, demonstrating its effectiveness in real-world applications.
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All authors Wenwen Liao, Hang Ruan, Jianbo Yu, Bing Song, YuansongWang, Xiaofeng Yang