Computer Vision · Cross-listed from cs.AI
Integrating Triaxial IMU Sensors and Ensemble Learning for Effective Parkinson Disease Severity Classification
Rehan Khan, Muhammad Junaid Asif and Rana Fayyaz Ahmad
Parkinson disease PD is a progressive neurodegenerative disease that can have a significant impact on motor performance resulting in the appearance of symptoms such as tremors
rigidity postural instabilities and bradykinesia. Timely clinical treatment disease management and quality life of the patients are closely linked to early and appropriate identification of PD. Over the past few years the growth of wearable sensor technology and artificial intelligence AI have made it possible to create noninvasive and data driven disease detection methods. This paper proposes a comparative system using artificial intelligence to detect Parkinsons disease by analyzing the motion and tremor data captured by an inertial measurement unit IMU. The data comprises the signals of the acceleration and gyroscope sensors measuring movement in three directions X Y and Z. The signs and symptoms provide helpful information about subtle motor deficits associated with PD. Several classification models like Support Vector Machine SVM Logistic Regression LR KNearest Neighbors KNN Decision Tree DT Extreme Gradient Boosting XGBoost and Light Gradient Boosting Machine LightGBM were used to compare their effectiveness. The Logistic Regression model had a performance around 75 percent in all evaluation metrics and KNearest Neighbours KNN around 90 percent. The support vector machine SVM performed almost 94 percent whereas the performance of classifiers such as Decision Tree and XGBoost was close to 96 percent and overall classification efficacy respectively. LightGBM model performs consistently at the best rank among all of the evaluated methods having Accuracy, Precision, Recall and F1score of around 97 percent. The results show that the proposed machine learning approach offers an accurate and effective predictive capability in the classification of PD severity.
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Computer Vision · Cross-listed from cs.AI
Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding
Linghao Meng, Qiankun Li, Junyuan Mao et al. (+7 more)
While
Multimodal Large
Language Models (MLLMs) demonstrate superior generalization in fundamental
video tasks, restricted context windows limit their long
video understanding. To accommodate this constraint, models typically resort to keyframe selection. However, uniform sampling or static query-guided selection often overlooks critical temporal context, failing to adapt to the varying query temporal granularities. In this paper, we propose ReMem, a temporal granularity-adaptive keyframe selection framework for training-free LongVideoQA. ReMem introduces a dual-level
memory-augmented adaptation. At the query level,
Memory-Driven Question Parsing leverages LLM long-term
memory to decode question temporal granularity and extract semantic entities. At the
video level, Synergistic Dual-Semantic Frame
Alignment exploits intrinsic structural
memory to align frames with query semantics, guiding Structure-Aware Dynamic Frame Routing to cluster events and optimally distribute sampling budgets. By explicitly preserving temporal information with
memory mechanisms, ReMem suppresses redundancy and empowers MLLMs to perform robust multi-granular
video reasoning. Evaluations across four popular LongVideoQA
benchmarks using three MLLMs demonstrate highly efficient, state-of-the-art zero-shot performance; notably, LLaVA-
Video with ReMem reaches 54.5% (+12.3%) on LVBench and 67.1% (+8.2%) on LongVideoBench.
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All authors Linghao Meng, Qiankun Li, Junyuan Mao, Pujin Liao, Zhicheng He, Enbo Zhang, Kun Wang, Yang Liu, Huazhu Fu, Yueming Jin
Computer Vision · Cross-listed from cs.LG
FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images
Alessandro Di Matteo, Sara Moccia, Giuseppe Rizzo et al. (+5 more)
Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities. However, this task remains highly challenging due to the intrinsic limitations of US imaging, including low contrast, speckle noise, and the considerable anatomical variability of the CC. We propose FedCC, a
federated learning (FL)-based framework for CC localization in fetal US images, specifically designed for realistic multi-center and resource-constrained clinical settings without requiring data sharing. The framework integrates a frozen DINOv2 backbone with a lightweight YOLO-based detection head. To enable parameter-efficient adaptation, Low-Rank Adaptation (LoRA) modules are incorporated, allowing only a small subset of parameters to be optimized and exchanged among clients. This strategy substantially reduces both computational and communication overhead, making the framework suitable for low-resource environments. The proposed approach was evaluated on a multi-center
dataset comprising 10,970 ultrasound frames acquired from 58 pregnant women during routine neurosonographic examinations across three clinical sites using heterogeneous imaging devices. The proposed framework achieved strong performance in the
federated setting. In particular, the combination of DINOv2 and LoRA under the FedAvg strategy achieved an average mAP@50 of 0.857 and an F1-score of 0.803, outperforming both full fine-tuning and encoder-freezing baselines. Notably, the proposed approach reduced the number of trainable parameters to 2.9M compared with 24.4M in full fine-tuning, corresponding to an approximately 8.5$\times$ reduction in communication cost. These findings represent a promising step toward scalable,
privacy-preserving, and clinically deployable AI systems for fetal neurosonography.
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All authors Alessandro Di Matteo, Sara Moccia, Giuseppe Rizzo, Gianpaolo Grisolia, Ricciarda Raffaelli, Lorenzo Vasciaveo, Francesco D'Antonio, Maria Chiara Fiorentino
Computer Vision
Classroom Behavior Monitoring with YOLO An Empirical Study in Higher Education Settings
Sinh Vu Trong, Dung Nguyen Manh, Hieu Hoang Minh et al. (+3 more)
Classroom behavior monitoring plays a vital role in evaluating student engagement and improving teaching effectiveness. Traditional observation methods remain subjective and lack scalability. This study introduces a real-world
dataset of classroom
videos collected at the Banking Academy of Vietnam (BAV-Classroom
dataset), annotated with nine distinctive behavioral categories. State-of-the-art Computer
Vision models were evaluated and compared, with YOLOv11 achieving the best performance. Experimental results indicate that students' concentration often decreases notably during the final part of lectures, highlighting challenges in sustaining engagement. Our findings demonstrate the feasibility of applying computer
vision for automated classroom monitoring, providing valuable insights for academic quality management.
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All authors Sinh Vu Trong, Dung Nguyen Manh, Hieu Hoang Minh, Hieu Pham Trung, Thu Pham Ha, Nhu Le Hoang
Computer Vision
Criterion-Conditional In-Context Learning: Evaluating Criterion-Shift Adaptation in Vision-Language Models
Kaiyun Yang, Ruilin Yang, Zhimin Yao et al. (+2 more)
Vision-
language models can perform new tasks without parameter updates through in-context learning (ICL), whose core mechanism is utilizing the support set for task induction. In the standard ICL setting, once the task is induced, its decision criterion remains fixed. However, in real-world applications, many tasks exhibit a stable high-level intent, while their decision criteria shift according to specific requirements. Thus, we introduce a new setting, denoted as Criterion-Conditional In-Context Learning (CC-ICL), where models must infer the latent criterion from context and adjust predictions accordingly under fixed task semantics. To evaluate this capability, we propose two complementary metrics, Criterion Invariance and Criterion Sensitivity, capturing the model's robustness and adaptability under criterion shifts. We further construct CC-Bench, a multi-domain
benchmark that supports evaluation under the CC-ICL setting. By employing a dual-level data hierarchy, CC-Bench enables legitimate ground-truth variation conditioned on the active criterion even when the task remains fixed. Experiments on CC-Bench reveal that most models exhibit a rigid boundary bias, struggling to align their decisions with the latent criterion. We also find that even a simple multi-criterion training strategy can significantly reduce this bias, improving Criterion Sensitivity and enabling 7B-scale models to surpass proprietary models without degrading general
multimodal performance.
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All authors Kaiyun Yang, Ruilin Yang, Zhimin Yao, Jikai Wang, Wei Ge
Computer Vision
Symmetry-Structured Neural Completion of Islamic Geometric Patterns from Sparse Control Geometry
Hassan Ugail and Irfan Mehmood
Islamic
geometric patterns are governed by exact rotational
symmetry and strict construction rules. This paper treats these rules as formal
geometric knowledge and embeds them in a neural completion framework, rather than leaving them to be learned statistically from data. Given sparse control geometry and a target
symmetry order, the system completes the pattern as a vector graph by predicting edges and refinements of bounded curves over a candidate
lattice whose edges are organised into rotational orbits under the cyclic group.
Symmetry is enforced either by constraining predictions within these orbits or by projecting them onto them during inference. The orbit-tied variant provides a constructive guarantee: for any input and any orbit-level selection rule, it produces exact N-fold
symmetry, preserves anchor points, and keeps all refinements within prescribed bounds. These properties are verified numerically. The study focuses on rotational
symmetry, and all quantitative results are obtained from procedurally generated graphs inspired by Islamic
geometric design rather than from a historical corpus. On clean inputs, enforcing exact validity produces no measurable loss in fidelity. When control geometry is missing, an unstructured decoder loses fidelity and breaks
symmetry; retraining on corrupted inputs recovers much of the fidelity but not exact validity.
Symmetry-structured inference, by contrast, keeps violations at zero throughout. The results show that augmentation and
symmetry structure address distinct failure modes: augmentation improves fidelity under corruption, while
symmetry structure guarantees validity. The framework therefore provides a knowledge-constrained, guarantee-backed approach to neural completion for scalable vector ornaments whose validity depends on exact
geometric structure.
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Computer Vision
Additive Causal Construction for Transferable and Reconfigurable Cross-System Learning in Multi-Source Image Fusion
Zhizhong Fu, Wei Zhou, Zhaoyang Jiang et al. (+5 more)
In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems. However, cross-system discrepancy (CSD) and cross-system
entanglement (CSE) commonly arise during the fusion process, often leading to significant performance degradation under out-of-distribution (OOD) predictions. To address the CSD and CSE issues, we propose the additive causal construction (ACC) framework, which characterizes information fusion at two levels: firstly, it establishes causal "anchors" shared among multiple systems through intervention consistency to enable causal graph transferability (CGT); and secondly, it formalizes the fusion process as causal construction and models the reliability of constructed paths through uncertainty quantification to ensure causal graph reconfigurability (CGR). Building upon this, we revisit the traditional causal representation learning (CRL) with ACC and propose ACC-CRL as a learnable instantiation of the framework. The method explores joint causal content representations across systems via content-mechanism decoupling, and performs response
alignment under shared anchors to mitigate CSD. Furthermore, it incorporates structural uncertainty to adaptively regulate the fusion process, thereby suppressing unstable CSE. We conduct systematic experiments on synthetic data (ColorMNIST) and real-world multi-center medical imaging tasks (microvascular invasion (MVI) prediction). The results demonstrate that the proposed method significantly improves OOD generalization while maintaining in-distribution (ID) performance, validating the effectiveness and robustness of the ACC-CRL strategy based on mechanism
alignment and uncertainty modeling in open environments.
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All authors Zhizhong Fu, Wei Zhou, Zhaoyang Jiang, Yulong Lin, Yifu Hou, Xiaorong Ding, Qiang Yan, Yifan Chen
Computer Vision
Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation
Ying Chen, Jinyue Li, Kun Wang et al. (+2 more)
The Segment Anything Model with Concepts (SAM3) heralds a new paradigm for open-vocabulary
segmentation through natural
language interaction, offering significant potential for medical image analysis. However, effectively adapting such a powerful
vision-
language model to the diverse and nuanced domain of medical imaging remains a key challenge. Naive fine-tuning is parameter-inefficient, while standard Mixture-of-Experts (MoE) methods introduce prohibitive computational overhead, limiting their clinical applicability. To address this, we propose Dual-Adaptive SAM3 (DA-SAM3), a novel framework that achieves both high
segmentation accuracy and extreme parameter efficiency via a dual-adaptive specialization mechanism. Our first adaptation is task-aware: a Dynamic Expert Router (DER) that sparsely activates the most relevant experts by jointly
reasoning about the visual input and the textual concept
prompt, mimicking a clinical consultation process. Our second adaptation is parameter-aware: a Decomposed Parameterized Experts (DPE) design that represents each expert as a shared frozen base (inherited from the pretrained SAM3) and a lightweight trainable low-rank delta, reducing MoE parameter overhead by over 80%. Extensive experiments on multiple public medical
segmentation benchmarks demonstrate that Dual-Adaptive SAM3 not only matches or exceeds the accuracy of fully fine-tuned SAM3 and standard MoE baselines, but also achieves a notable 5% gain over current state-of-the-art methods, with interpretable results validating its effectiveness. The code is available at: https://github.com/Reconsider80/DA-SAM3.
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All authors Ying Chen, Jinyue Li, Kun Wang, Qiankun Li, Yang Liu
Computer Vision
Uncertainty-Aware Last-Layer Adaptation of RETFound for Referable Diabetic Retinopathy Screening Under Dataset Shift
Karim Mardhani
This paper presents a safety-centered empirical evaluation of uncertainty-aware last-layer adaptation for referable diabetic retinopathy screening using RETFound, a self-supervised
vision-
transformer retinal foundation model used here as a frozen feature encoder, and the public APTOS 2019 and DDR diabetic retinopathy fundus image
datasets. We compare a cached-feature softmax head, post-hoc temperature scaling, variational
Bayesian last-layer heads, a diagonal Laplace last-layer approximation, and an SNGP-style cached-feature head. On APTOS, uncertainty-aware operating points improved sensitivity and selective-referral behavior. The strongest APTOS selective-referral result deferred approximately 20 percent of cases and reduced accepted-case false negatives to zero while preserving high accepted-case specificity. However, threshold tuning also reduced false negatives at high false-positive cost, so false-negative reduction alone was not unique to
Bayesian modeling. On DDR, native
Bayesian heads qualitatively reproduced the APTOS direction but with weaker tradeoffs, while the APTOS-trained SNGP checkpoint transferred poorly and failed to provide useful external selective-referral behavior. These results highlight the value of safety-centered evaluation beyond aggregate accuracy: uncertainty-aware last-layer heads can improve internal safety-oriented operating points, but trustworthy retinal screening claims require explicit safety-coverage evaluation and second-
dataset validation under shift.
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Computer Vision
MAGE: View-guided Point Cloud Completion with Efficient Modality Alignment and Adaptive Geometry Enhancement
Weize Quan, Zhengwei Wu, Kai Wang et al. (+1 more)
View-based point cloud completion aims to recover a complete 3D shape from a partial point cloud, guided by a single-view image. However, existing approaches often suffer from limited performance due to weak modality
alignment and limited self-geometry enhancement. To overcome these challenges, we propose a unified geometry-aware framework that integrates efficient modality
alignment and adaptive geometry enhancement, mainly to address cross-modal
geometric inconsistency of view-guided point cloud completion. Specifically, we propose a geometry-aware modality
alignment by integrating a shared self-
attention Transformer and cross-modality reconstruction supervision, which aims to bring features of the image and point cloud close to each other in a shared latent space describing the 3D object. To enhance the perception of global shape and local
geometric details, we propose an adaptive geometry-aware self-
attention module, which simultaneously considers local geometry-aware
attention computation and the spatially-variant feature fusion. In addition, we apply a geometry-perceptive anchor refinement module to reorganize the anchor points (representing a local region of the shape) under appropriate supervision, further boosting the completion performance of our method. Extensive experiments on both synthetic and real-world
datasets demonstrate that our method achieves superior performance over existing approaches. Our code will be available at https://github.com/weizequan/MAGE.
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All authors Weize Quan, Zhengwei Wu, Kai Wang, Dong-Ming Yan
Computer Vision · Cross-listed from cs.GR
Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification
Aizierjiang Aiersilan
Deploying 3D point cloud analysis in
privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must run on limited edge hardware. We present a multi-seed
benchmark jointly evaluating
federated learning (FL) and knowledge
distillation (KD) for 3D point cloud classification. It spans 13 FL
algorithms and 10 KD objectives (a 130-pair cross-product) across 504 training runs, evaluated on ModelNet40 and a clinical craniosynostosis
dataset. We report three findings. First, under extreme non-IID label skew, standalone FL degrades sharply: on ModelNet40, the strongest method reaches 76.32% against a 92.26% centralized reference; on clinical data, the best reaches 75.83% against 100%. Second,
distillation successfully compresses the teacher into a student 74.51% smaller and roughly twice as fast at inference, often matching or surpassing the teacher. Third, the combined pipeline exposes an evaluation pitfall: when
distillation keeps a hard-label cross-
entropy term on a labeled proxy split, a collapsed
federated teacher (8.50%) paired with Logit-MSE still yields a 92.94% student. This 84.4-point gap reflects the proxy labels rather than the
federated model, reusing the very labels whose
privacy motivated federation. Objectives without hard labels instead track teacher quality ($r \approx 0.99$) and collapse when the teacher does. We therefore recommend evaluating FL-KD pipelines with label-free
distillation so reported accuracy reflects the
federated teacher, not the proxy.
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Computer Vision
AEGIS: A Multi-Task Joint-Embedding Predictive Architecture for Mammography
Scott Chase Waggener, Sai Karthik Navuluru and Lakshman Tamil
We present Aegis, a joint-
embedding predictive architecture for breast cancer detection and density assessment in mammography. We train three
Vision Transformer variants (Small/Base/Large) using self-supervised joint-
embedding predictive architecture (JEPA) pre-training on 71,103 studies from 14 clinical sites, followed by supervised fine-tuning with progressive resolution scaling up to 2048x1536. On a curated 785-study test set, our largest model achieves area under the receiver operating characteristic curve (AUC) 0.949 for breast cancer triage with 93% sensitivity and 75% specificity at the optimal operating point. An ensemble combining our model with a U.S. Food and Drug Administration-cleared baseline further improves discrimination to 0.952 AUC. For breast density classification, the model achieves 0.953 AUC for binary (dense vs. non-dense) classification and 62.6% exact accuracy across four Breast Imaging Reporting and Data System (BI-RADS) categories, with 98.8% adjacent accuracy comparable to reported human inter-reader agreement. External validation on the public VinDr-Mammo
dataset provides evidence of cross-population transfer under a different reference standard, with the largest model achieving 0.871 AUC for triage in a zero-shot setting.
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Computer Vision
MVDGC: Joint 3D and 2D Multi-view Pedestrian Detection via Dual Geometric Constraints
Thinh Phan, Hao Vo, Khoa Vo et al. (+3 more)
The core challenge in multi-view pedestrian detection (MVPD) lies in effective aggregation of visual features from different viewpoints for robust occlusion
reasoning. Recent approaches have addressed this by first projecting image-view features onto a Bird's Eye View (BEV) map, where ground localization is then performed. Despite impressive performance, the perspective transformation induces severe distortion, causing spatial structure break and degrading the quality of object feature extraction. The blurred and ambiguous features hinder accurate BEV point localization, especially in densely populated regions. Moreover, the strong mutual relationship between the BEV ground point and image bounding boxes is not capitalized on. Although multi-view consistency of 2D detections can serve as a powerful constraint in BEV space, these detections are commonly treated as auxiliary signals rather than being jointly optimized with the primary task.In this work, we propose MVDGC, a unified framework that jointly estimates pedestrian locations on the BEV plane and 2D bounding boxes in image views. MVDGC employs a sparse set of 3D cylindrical queries that embraces
geometric context across both BEV and image views, enforcing dual spatial constraints for precise localization. Specifically, the
geometric constraints is established by modeling each pedestrian as a vertical cylinder whose center lies on the BEV plane and whose projection casts a rectangular box in the image views. These queries function as shape anchors that directly extract 2D features from the intact image-view features using camera projection, eliminating projection-induced distortions. The 3D cylindrical query enables the unification of BEV and ImV localization into a single task: 3D cylinder position and shape refinement. Code is available at: https://github.com/UARK-AICV/MVDGC
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All authors Thinh Phan, Hao Vo, Khoa Vo, Thanh Ngo, Cuong Pham, Ngan Le
Computer Vision
Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification
Afshar Shamsi, Xiao-Yu Guo, Hamid Alinejad-Rokny et al. (+3 more)
Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data. However, in the absence of supervision,
entropy-based adaptation is fundamentally underconstrained: multiple distinct parameter updates can achieve similarly low
entropy while inducing drastically different decision boundaries. This phenomenon, known as underspecification, renders standard TTA brittle and prone to collapse into spurious modes. In this work, we reinterpret TTA through a posterior-inspired lens induced by
entropy minimization, where low-
entropy solutions define a pseudo-likelihood over parameters. Instead of committing to a single point estimate, we introduce a particle-based diversification framework that explores multiple plausible adaptation trajectories simultaneously. Our method can be viewed as a structured exploration of multiple plausible adaptation solutions, implemented through multi-level diversification at the output, parameter, optimizer, and input levels. Crucially, the framework acts as a plug-and-play wrapper compatible with existing TTA methods. Extensive experiments on challenging
benchmarks demonstrate consistent gains in stability and robustness, achieving improvements of 3-4% under mixed shifts, 2-3% with batch size one, and 1-2.5% under label shifts, outperforming state-of-the-art baselines. Our results suggest that treating TTA as a multi-hypothesis inference problem, rather than a single-point optimization task, is key to mitigating underspecification and enabling reliable real-world deployment.
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All authors Afshar Shamsi, Xiao-Yu Guo, Hamid Alinejad-Rokny, Arash Mohammadi, Damien Teney, Ehsan Abbasnejad
Computer Vision
Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring
Samira Malek, Haichuan Zhang, Chul Lee et al. (+1 more)
While most image deblurring techniques directly restore the spatial image variable, we propose an amplitude and phase decomposition recognizing the importance of accurate phase estimation in recovering sharp image details. To that end, we first develop novel linear minimum mean squared (LMMSE)
estimators of the amplitude and phase of the blurred, noisy image observation. An iterative optimization
algorithm follows that recovers the sharp image using the aforementioned LMMSE
estimators. Finally,
matrix parameters that are statistically determined and fixed in the iterative
algorithm are now learned using a training
dataset of clean and degraded observations. Our deblurring engine is dubbed UPADNet (Unrolled Phase and Amplitude Decomposition Network), such that each iteration of the underlying phase and amplitude recovery
algorithm is parameterized and trained end-to-end. Experiments over
benchmark evaluation
datasets such as GoPro, RealBlur and COCO
datasets confirm that UPADNet outperforms state of the art deep networks including those based on
algorithm unrolling in the image domain. The benefits of UPADNet are even more pronounced in high noise and limited training data regimes.
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All authors Samira Malek, Haichuan Zhang, Chul Lee, Vishal Monga
Computer Vision · Cross-listed from cs.CL
LV-ROVER-MLT: Low-Resource Maltese OCR by Synthetic Fine-Tuning and Multi-Stream Arbitration
Adam Darmanin
Maltese has substantial text corpora and pretrained
language models, but paragraph-scale OCR training data remains scarce; NOMOCRAT provides 57 verified annotated pages. LV-ROVER-MLT combines synthetic fine-tuning of Tesseract~5 with five complementary recognition streams and lexicon-gated word-level arbitration adapted to Maltese diacritics and hyphenation. In the DocEng~2026 Maltese OCR competition, the system placed first with held-out CER 0.0074; the next-ranked submission scored 0.0161 and NOMOCRAT scored 0.0163. The same approach produced a significant improvement over stock Tesseract on Luxembourgish, while the Hungarian result was inconclusive. A 36,803-pair Maltese OCR corpus constructed from EUR-Lex and Wikipedia provides an additional paragraph-level resource. Code, model weights, and corpus data are public.
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Computer Vision
Does Your ViT Still Need U-Net for Segmentation?
Xin Li, Wenhui Zhu, Xuanzhao Dong et al. (+6 more)
Medical image
segmentation is dominated by U-Net-style encoder-decoder architectures.
Vision Transformers (ViTs) overcome the limited receptive field of convolutional networks through self-
attention, enabling modeling of long-range dependencies. Early ViT-based
segmentation methods typically retained U-Net-style decoders because pretrained ViT representations were insufficient to support accurate dense prediction. Recent advances in large-scale pretraining have redefined the representation capability of ViTs, reducing the reliance on U-Net-style decoder architectures in modern
vision models. This
prompts two questions: Is the U-Net paradigm still necessary for medical image
segmentation? If not, how should an encoder-only
segmentation framework be designed? Motivated by these questions, we explore key architectural choices for encoder-only medical image
segmentation based on modern ViT backbones and establish a query-based encoder-only design with multi-level query modeling and learnable block fusion, realized in Encoder-only
Segmentation (EoSeg). Extensive experiments across seven
benchmark datasets spanning CT, MRI, histopathology, endoscopy, and dermoscopy validate the effectiveness of the proposed design across diverse medical imaging modalities, including mDice scores of 85.50% on Synapse, 91.73% on ACDC, and 93.27% on GlaS. The results demonstrate that a U-Net-style decoder is no longer necessary for medical image
segmentation with modern ViT backbones and further show that EoSeg provides an effective encoder-only design. Code is available at: https://github.com/Retinal-Research/EoSeg
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All authors Xin Li, Wenhui Zhu, Xuanzhao Dong, Xiwen Chen, Yanxi Chen, Yujian Xiong, Hao Wang, Oana M. Dumitrascu, Yalin Wang
Computer Vision
EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards
Siddhant Panpatil, Arth Singh, Mijin Koo et al. (+3 more)
Vision-
language models (VLMs) are increasingly proposed as runtime safety guards for
embodied agents in homes and factories. A deployable guard must catch genuinely unsafe situations while avoiding unnecessary intervention on routine but superficially alarming activity, a distinction obscured by binary safety
benchmarks. We introduce EgoSafetyBench, an
egocentric video benchmark of 1,200
robot-view scenarios annotated at half-second granularity, with two evaluation tracks. The situational track (800 scenarios) spans routine, safe-but-suspicious, obvious-hazard, and contextual-hazard scenes. The visual-channel track (400 scenarios) tests whether misleading in-scene text corrupts physical-safety judgments, using matched truthful controls. Both tracks use contrastive ladders: near-identical scenarios differing in a single visible deciding cue, forcing predictions to hinge on that cue. Across ten open- and closed-source VLMs, we find that guards often recognize
videos containing hazards yet miss the specific hazardous moments, especially for contextual hazards. Misleading in-scene signs further degrade all tested guards: vulnerable models miss up to a third of hazards, while seemingly robust models often over-intervene on safe content. Matched controls show that apparent robustness can reflect indiscriminate alarming rather than true physical
reasoning. A 20-clip real-
video sanity check further shows that model rankings transfer beyond synthetic rendering, with Spearman h̊o} = 0.87 for hazard miss rate and 0.94 for visual-channel mismatch recall.
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All authors Siddhant Panpatil, Arth Singh, Mijin Koo, Chaeyun Kim, Haon Park, Dasol Choi
Computer Vision
Trust the Prior (or Not): Uncertainty-Aware Abdominal Aortic Aneurysm Segmentation
Erich Robbi, Daniele Ravanelli and Andrea Passerini
Robust
segmentation of intraluminal thrombus is critical for risk assessment in Abdominal Aortic Aneurysm, yet it remains challenging due to heterogeneous thrombus features and low contrast with surrounding non-enhanced tissues. Domain shifts induced by different Computed
Tomography Angiography (CTA) protocols further inhibit multi-center generalization of deep learning models. To address these challenges, we propose a patient-specific framework that integrates discriminative learning with anatomically informed priors. Our approach introduces two key components: (1) a patient-specific intensity normalization based on a Gaussian Mixture Model of local anatomy, and (2) an Uncertainty-Gated Anatomical
Attention module that incorporates spatial priors while adaptively modulating their influence according to voxel-wise confidence. This design allows for anatomical guidance in ambiguous regions while suppressing unreliable priors. The proposed method achieves state-of-the-art performance on in-distribution test data and substantially outperforms existing alternatives in generalization to external multi-center CTA data, while remaining interpretable through an explicit separation of visual and anatomical evidence.
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Computer Vision · Cross-listed from cs.RO
HydraCollab: Adaptive Collaborative-Perception for Distributed Autonomous Systems
Luke Chen, Cheng-Ju Wu, David R. Martin et al. (+3 more)
Collaborative-perception enables multi-
robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative-perception systems face an inherent trade-off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better perception results at the cost of increased communication overhead. However, real-world communication networks impose bandwidth constraints that require minimizing communication overhead without sacrificing perception performance. To address this challenge, we propose HydraCollab, an adaptive collaborative-perception framework that (i) selectively transmits the most informative sensor features and (ii) dynamically employs collaboration strategies (intermediate or late) based on spatial confidence maps. Extensive evaluations on the V2X-R, V2X-Radar and
UAV3D-mini
datasets demonstrate that HydraCollab achieves the best overall trade-off between accuracy and communication cost among existing collaborative-perception methods. Relative to SOTA Where2comm, HydraCollab uses only 41% of the bandwidth on V2X-R and 26% on V2X-Radar while improving performance by 0.78% and 0.75% respectively. Our code and models are available at https://github.com/AICPS/HydraCollab.
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All authors Luke Chen, Cheng-Ju Wu, David R. Martin, Qilin Ye, Pramod Khargonekar, Mohammad Abdullah Al Faruque
Computer Vision
VOCA: Visual Odometry with Codec Awareness
Nouri Alexander Hilscher, Mateo de Mayo, Dominik Muhle et al. (+2 more)
Camera pose estimation from image streams is a critical component of spatial world models that integrate perception into planning and decision-making. Nearly all Visual Odometry (VO) and Simultaneous Localization and Mapping (V-SLAM) systems have focused on
datasets containing raw, uncompressed
videos. Many working systems instead use ubiquitous hardware units to efficiently compress and decode
video streams, saving orders of magnitude in storage and bandwidth. However, this lossy compression introduces visual artifacts that hinder the performance of traditional tracking systems. We present VOCA, a causal stereo visual-odometry method that exploits codec information to improve tracking performance. We achieve state-of-the-art performance on causal VO for relative trajectory error, efficiency, and absolute trajectory error on compressed streams. This work highlights the potential of leveraging widely available
video codec information for
vision tasks.
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All authors Nouri Alexander Hilscher, Mateo de Mayo, Dominik Muhle, Christoph Otten genannt Hermes, Daniel Cremers
Computer Vision
DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation
Héctor Laria, Yiping Han, Julian D. Santamaria et al. (+4 more)
Adapting pre-trained text-to-image
diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone. We argue this framing is incomplete. Through sparse autoencoder analysis and zero-shot classification, we demonstrate that adaptation systematically damages semantically unrelated concepts in ways that aggregate metrics structurally cannot surface: when damage is severe enough for FID and KID to respond, the model is already nearly unusable; when the model remains functional, FID and KID stay flat while specific classes silently suffer worst-case zero-shot accuracy drops of up to 18.9 points and concept-level distributions shift dramatically. This pattern appears at both ends of the adaptation spectrum (concept customization and concept unlearning), suggesting it is a systematic consequence of weight-level modification rather than an artifact of any particular method. To surface this hidden drift before deployment, we introduce DriftScope, a
prompt-level diagnostic tool that takes any two model checkpoints and returns a ranked list of
tokens whose visual concepts have shifted most between them. DriftScope optimizes a soft
prompt to attribute drift at the
token level without requiring access to real data or model internals. The result is an interpretable, concept-level audit that aggregate evaluation cannot provide.
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All authors Héctor Laria, Yiping Han, Julian D. Santamaria, Kai Wang, Bogdan Raducanu, Joost van de Weijer, Alexandra Gomez-Villa
Computer Vision
PRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment
Aymeric Fleith, Julian Zirbel, Daniel Cremers et al. (+1 more)
We introduce PRISM-VO, a novel pure optimization-based sparse photometric visual odometry framework for focused plenoptic cameras. The core of PRISM-VO is a novel photometric plenoptic bundle adjustment which jointly optimizes camera poses and inverse depth values of points in a sliding window. By combining
geometric depth from a single plenoptic image with temporal multi-view constraints, PRISM-VO achieves accurate and drift-resilient motion estimation. Through explicit modeling of the plenoptic projection, PRISM-VO provides reliable metric-scale reconstructions, overcoming the scale ambiguity of
monocular SLAM
algorithms. Importantly, our approach relies solely on a single plenoptic sensor and avoids complex initialization, as depth priors are computed directly from plenoptic imaging. Experiments show that PRISM-VO outperforms the current state-of-the-art plenoptic visual odometry method on indoor and outdoor scenes. The proposed approach rivals other optimization- and learning-based methods while accurately and reliably recovering a metric scale of the scene. Project page: https://prism-vo.github.io/
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All authors Aymeric Fleith, Julian Zirbel, Daniel Cremers, Niclas Zeller
Computer Vision
Steal the Patch Size: Adversarially Manipulate Vision-Language Models
Kai Hu, Akash Bharadwaj, Weichen Yu et al. (+1 more)
We present a black-box model-stealing attack that recovers private
vision-tokenizer configurations of deployed
vision-
language models (VLMs), including the visual patch size and input preprocessing pipeline. The key idea is a task-level side channel induced by ViT-style patchification: when a synthetic grid image is aligned with the hidden patch grid, boundary cues are erased at tokenization, causing periodic accuracy drop. By sweeping the grid
cell size and measuring these collapses, we infer the patch size; by introducing padding and a consistency-check test, we further identify whether preprocessing is dynamic- or fixed-resolution and recover the target resize resolution. Across open-source Qwen-VL variants and proprietary models including GPT and Claude, we reliably recover tokenizer-related parameters. Finally, we show that such leakage enables preprocessing-aware transfer attacks and model-targeted
adversarial manipulation.
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All authors Kai Hu, Akash Bharadwaj, Weichen Yu, Matt Fredrikson
Computer Vision · Cross-listed from cs.CL
Identifying and Resolving Pitfalls of Knowledge-Based VQA Benchmarks: Auditing, Repairing, and Augmenting
Qian Ma, S M Rayeed, Charles V. Stewart et al. (+2 more)
Knowledge-Based Visual Question Answering (KB-VQA) aims to evaluate whether Visual
Language Models (VLMs) can retrieve, ground, and reason over external structured knowledge beyond visual evidence. In practice, answer accuracy is widely adopted as the primary evaluation metric, implicitly treating correctness as a proxy for knowledge-grounded
reasoning. However, for existing KB-VQA
benchmarks, this proxy relies on critical assumptions that are often overlooked and rendered unreliable by
benchmark issues: annotated answer must be derivable from the associated knowledge base, question must be well-posed with sufficient constraints, and visual setting must meaningfully require grounded disambiguation. In this work, we show that these assumptions are systematically violated in existing KB-VQA
benchmarks. Our audit reveals substantial instances with missing or contradicted answers and underspecified questions that render accuracy a misleading metric. Furthermore, we find that existing
datasets rely on visually trivial, single-entity scenes that bypass the need for sophisticated visual-to-knowledge mapping. We demonstrate that even with controlled architectures, these flaws lead to distorted model rankings and overestimations of
reasoning capabilities. To address this, we introduce (1) a principled audit-and-repair protocol that restores answer derivability and question clarity, and (2) a controlled multi-entity augmentation protocol that introduces visual ambiguity to challenge initial
retrieval and grounded
reasoning. Re-evaluation under corrected and augmented settings yields markedly different performance trends. Our findings call for rethinking evaluation protocols and designing more interaction-aware KB-VQA
benchmarks that prioritize verifiable
reasoning over simple matching.
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All authors Qian Ma, S M Rayeed, Charles V. Stewart, Qiong Wu, Yao Ma
Computer Vision
Progressive Pose-Guided 4D Animal Reconstruction from Monocular Video
Siyuan Li, Weiying Chen, Yilin Wang et al. (+3 more)
Reconstructing 4D animals from
monocular videos is challenging due to large inter-species variation, complex articulations, and the lack of reliable templates. Existing approaches typically rely on either strict category-specific priors that restrict generalization, or unconstrained generative models that sacrifice input fidelity. To bridge this gap, we present a progressive test-time optimization framework built on 3D Gaussian Splatting for high-fidelity 4D animal reconstruction from a single
video. Our key insight is that a coarse shape prior suffices when coupled with a progressive strategy that disentangles articulated pose from non-rigid deformation. Specifically, we employ a
symmetry-aware temporal encoding that exploits bilateral cues while absorbing camera estimation drift and a part-conditioned deformation mechanism guided by learnable part anchors and a learnable skinning field. Extensive experiments demonstrate that our approach generalizes robustly across diverse species, achieving superior
geometric accuracy, temporal consistency, and visual fidelity compared to existing baselines, even under severe prior mismatch.
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All authors Siyuan Li, Weiying Chen, Yilin Wang, Xinxin Zuo, Xingyu Li, Li Cheng