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GTSR: Subsurface Scattering Awared 3D Gaussians for Translucent Surface Reconstruction
Abstract
Reconstructing translucent objects from multi-view images is a difficult problem. Previously, researchers have used differentiable path tracing and the neural implicit field, which require relatively large computational costs. Recently, many works have achieved good reconstruction results for opaque objects based on a 3DGS pipeline with much higher efficiency. However, such methods have difficulty dealing with translucent objects, because they do not consider the optical properties of translucent objects. In this paper, we propose a novel 3DGS-based pipeline (GTSR) to reconstruct the surface geometry of translucent objects. GTSR combines two sets of Gaussians, surface and interior Gaussians, which are used to model the surface and scattering color when lights pass translucent objects. To render the appearance of translucent objects, we introduce a method that uses the Fresnel term to blend two sets of Gaussians. Furthermore, to improve the reconstructed details of non-contour areas, we introduce the Disney BSDF model with deferred rendering to enhance constraints of the normal and depth. Experimental results demonstrate that our method outperforms baseline reconstruction methods on the NeuralTO Syn dataset while showing great real-time rendering performance. We also extend the dataset with new translucent objects of varying material properties and demonstrate our method can adapt to different translucent materials.
Abstract (translated)
从多视角图像重建半透明物体是一个难题。此前，研究者们采用可微路径追踪与神经隐式场等方法，但这些方案需要较高的计算成本。近期，许多基于3D高斯泼溅（3DGS）管线的方法在高效重建不透明物体方面取得了良好效果。然而，此类方法难以处理半透明物体，因其未考虑半透明物体的光学特性。本文提出一种基于3DGS的新管线（GTSR），用于重建半透明物体的表面几何结构。GTSR结合了两组高斯元——表面高斯元与内部高斯元，分别用于建模半透明物体表面的反射光与光线穿透后的散射颜色。为渲染半透明物体外观，我们引入基于菲涅尔项混合两组高斯元的方法。此外，为提升非轮廓区域的重建细节，我们采用迪士尼双向散射分布函数（BSDF）模型结合延迟渲染技术，增强对法线与深度的约束。实验结果表明，在NeuralTO Syn合成数据集上，本方法优于基线重建方法，同时展现出优秀的实时渲染性能。我们还扩展了包含不同材质特性的新半透明物体数据集，并验证了本方法可适应多种半透明材质。
URL
https://arxiv.org/abs/2603.22036
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SHARP: Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis
2026-03-23 10:25:45Bingxuan Zhao, Qing Zhou, Chuang Yang, Qi WangAbstract
Text-to-image generation powered by Diffusion Transformers (DiTs) has made remarkable strides, yet remote sensing (RS) synthesis lags behind due to two barriers: the absence of a domain-specialized DiT prior and the prohibitive cost of training at the large resolutions that RS applications demand. Training-free resolution promotion via Rotary Position Embedding (RoPE) rescaling offers a practical remedy, but every existing method applies a static positional scaling rule throughout the denoising process. This uniform compression is particularly harmful for RS imagery, whose substantially denser medium- and high-frequency energy encodes the fine structures critical for aerial-scene realism, such as vehicles, building contours, and road markings. Addressing both challenges requires a domain-specialized generative prior coupled with a denoising-aware positional adaptation strategy. To this end, we fine-tune FLUX on over 100,000 curated RS images to build a strong domain prior (RS-FLUX), and propose Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion (SHARP), a training-free method that introduces a rational fractional time schedule k_rs(t) into RoPE. SHARP applies strong positional promotion during the early layout-formation stage and progressively relaxes it during detail recovery, aligning extrapolation strength with the frequency-progressive nature of diffusion denoising. Its resolution-agnostic formulation further enables robust multi-scale generation from a single set of hyperparameters. Extensive experiments across six square and rectangular resolutions show that SHARP consistently outperforms all training-free baselines on CLIP Score, Aesthetic Score, and HPSv2, with widening margins at more aggressive extrapolation factors and negligible computational overhead. Code and weights are available at this https URL.
Abstract (translated)
基于扩散Transformer（DiT）的文生图技术已取得显著进展，但遥感（RS）图像合成仍面临两大瓶颈：缺乏领域专用的DiT先验模型，以及训练高分辨率RS应用所需模型的 prohibitive 成本。通过旋转位置嵌入（RoPE）重缩放实现的无训练分辨率提升提供了一种实用方案，但现有方法均在去噪全程采用静态位置缩放规则。这种均匀压缩对遥感图像尤为不利——其中高密度的中高频能量编码着对航拍场景真实性至关重要的精细结构（如车辆、建筑轮廓与道路标线）。要同时解决这两大挑战，需要领域专用的生成先验与感知去噪阶段的位置自适应策略。为此，我们在超过10万张精选遥感图像上微调FLUX模型，构建出强领域先验（RS-FLUX），并提出基于频谱感知的高动态自适应分辨率提升方法（SHARP）。该方法在RoPE中引入有理数分数时间调度k_rs(t)，在早期布局形成阶段施加强位置增强，并在细节恢复阶段逐步放松，使外推强度与扩散去噪的频率渐进特性相匹配。其分辨率无关的公式设计还能通过单一超参数集实现稳健的多尺度生成。在六种方形与矩形分辨率上的大量实验表明，SHARP在CLIP Score、美学评分与HPSv2指标上持续超越所有无训练基线方法，且在外推因子更激进时优势扩大，计算开销可忽略。代码与模型权重已公开于此https URL。
URL
https://arxiv.org/abs/2603.21783
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Boundary-Aware Instance Segmentation in Microscopy Imaging
2026-03-22 12:59:22Thomas Mendelson, Joshua Francois, Galit Lahav, Tammy Riklin-RavivAbstract
Accurate delineation of individual cells in microscopy videos is essential for studying cellular dynamics, yet separating touching or overlapping instances remains a persistent challenge. Although foundation-model for segmentation such as SAM have broadened the accessibility of image segmentation, they still struggle to separate nearby cell instances in dense microscopy scenes without extensive prompting. We propose a prompt-free, boundary-aware instance segmentation framework that predicts signed distance functions (SDFs) instead of binary masks, enabling smooth and geometry-consistent modeling of cell contours. A learned sigmoid mapping converts SDFs into probability maps, yielding sharp boundary localization and robust separation of adjacent instances. Training is guided by a unified Modified Hausdorff Distance (MHD) loss that integrates region- and boundary-based terms. Evaluations on both public and private high-throughput microscopy datasets demonstrate improved boundary accuracy and instance-level performance compared to recent SAM-based and foundation-model approaches. Source code is available at: this https URL
Abstract (translated)
在显微视频中精确勾画单个细胞对于研究细胞动态至关重要，但分离接触或重叠的细胞实例仍是一项长期挑战。尽管SAM等基础分割模型已拓宽了图像分割的可及性，但在密集显微场景中，它们仍难以在无需大量提示的情况下分离邻近的细胞实例。我们提出了一种无需提示、边界感知的实例分割框架，该框架预测符号距离函数而非二值掩码，从而实现对细胞轮廓平滑且几何一致的建模。通过学习的sigmoid映射将符号距离函数转换为概率图，实现了相邻实例间清晰的边界定位与稳健分离。训练过程由统一的改进豪斯多夫距离损失指导，该损失整合了区域与边界相关项。在公共及私有高通量显微数据集上的评估表明，与近期基于SAM及基础模型的方法相比，本方法在边界精度与实例级性能上均有提升。源代码见：此https链接
URL
https://arxiv.org/abs/2603.21206
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Representation-Level Adversarial Regularization for Clinically Aligned Multitask Thyroid Ultrasound Assessment
2026-03-22 07:14:43Dina Salama, Mohamed Mahmoud, Nourhan Bayasi, David Liu, Ilker HacihalilogluAbstract
Thyroid ultrasound is the first-line exam for assessing thyroid nodules and determining whether biopsy is warranted. In routine reporting, radiologists produce two coupled outputs: a nodule contour for measurement and a TI-RADS risk category based on sonographic criteria. Yet both contouring style and risk grading vary across readers, creating inconsistent supervision that can degrade standard learning pipelines. In this paper, we address this workflow with a clinically guided multitask framework that jointly predicts the nodule mask and TI-RADS category within a single model. To ground risk prediction in clinically meaningful evidence, we guide the classification embedding using a compact TI-RADS aligned radiomics target during training, while preserving complementary deep features for discriminative performance. However, under annotator variability, naive multitask optimization often fails not because the tasks are unrelated, but because their gradients compete within the shared representation. To make this competition explicit and controllable, we introduce RLAR, a representation-level adversarial gradient regularizer. Rather than performing parameter-level gradient surgery, RLAR uses each task's normalized adversarial direction in latent space as a geometric probe of task sensitivity and penalizes excessive angular alignment between task-specific adversarial directions. On a public TI-RADS dataset, our clinically guided multitask model with RLAR consistently improves risk stratification while maintaining segmentation quality compared to single-task training and conventional multitask baselines. Code and pretrained models will be released.
Abstract (translated)
甲状腺超声是评估甲状腺结节及判断是否需要活检的首选检查。在常规报告中，放射科医生会生成两种耦合输出：用于测量的结节轮廓，以及基于超声标准的TI-RADS风险分类。然而，不同阅片者的勾画风格和风险分级存在差异，导致监督不一致，可能削弱标准学习流程的效果。本文针对这一工作流程，提出一种临床引导的多任务框架，可在单一模型中联合预测结节掩膜与TI-RADS分类。为使风险预测基于有临床意义的证据，我们在训练中使用紧凑的TI-RADS对齐影像组学目标引导分类嵌入，同时保留互补的深度特征以提升判别性能。然而，在标注者存在变异的情况下，朴素的多任务优化常会失败——并非因为任务不相关，而是因为它们的梯度在共享表征中相互竞争。为使这种竞争显式且可控，我们引入RLAR（表征层面对抗梯度正则化器）。RLAR并非执行参数层面的梯度手术，而是利用各任务在隐空间归一化的对抗方向作为任务敏感性的几何探针，并对任务特定对抗方向间过大的角度对齐进行惩罚。在一个公开的TI-RADS数据集上，我们的临床引导多任务模型结合RLAR，在保持分割质量的同时，持续提升了风险分层效果，优于单任务训练和常规多任务基线。代码与预训练模型将公开发布。
URL
https://arxiv.org/abs/2603.21095
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MEMO: Human-like Crisp Edge Detection Using Masked Edge Prediction
2026-03-21 12:21:29Jiaxin Cheng, Yue Wu, Yicong ZhouAbstract
Learning-based edge detection models trained with cross-entropy loss often suffer from thick edge predictions, which deviate from the crisp, single-pixel annotations typically provided by humans. While previous approaches to achieving crisp edges have focused on designing specialized loss functions or modifying network architectures, we show that a carefully designed training and inference strategy alone is sufficient to achieve human-like edge quality. In this work, we introduce the Masked Edge Prediction MOdel (MEMO), which produces both accurate and crisp edges using only cross-entropy loss. We first construct a large-scale synthetic edge dataset to pre-train MEMO, enhancing its generalization ability. Subsequent fine-tuning on downstream datasets requires only a lightweight module comprising 1.2\% additional parameters. During training, MEMO learns to predict edges under varying ratios of input masking. A key insight guiding our inference is that thick edge predictions typically exhibit a confidence gradient: high in the center and lower toward the boundaries. Leveraging this, we propose a novel progressive prediction strategy that sequentially finalizes edge predictions in order of prediction confidence, resulting in thinner and more precise contours. Our method achieves visually appealing, post-processing-free, human-like edge maps and outperforms prior methods on crispness-aware evaluations.
Abstract (translated)
基于交叉熵损失实现精准锐利的边缘检测，一直是计算机视觉领域的一项挑战。本文提出了MEMO模型，仅通过交叉熵损失即可生成既准确又锐利的边缘。我们首先构建了一个大规模合成边缘数据集用于预训练MEMO，以增强其泛化能力。随后在各类下游数据集上的微调仅需一个轻量级模块，该模块仅增加1.2%的参数。在训练过程中，MEMO学习在不同比例的输入掩码下预测边缘。其推理过程的核心思想是：粗边缘预测通常呈现置信度梯度——中心区域置信度高，向边缘边界逐渐降低。基于此，我们提出一种新颖的渐进式预测策略，该策略按预测置信度顺序逐步确定边缘，从而得到更细、更精确的轮廓。我们的方法能够生成无需后处理、视觉上媲美人工标注的优美边缘图，并在锐利度感知的评估指标上超越了以往方法。
URL
https://arxiv.org/abs/2603.20782
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Gesture2Speech: How Far Can Hand Movements Shape Expressive Speech?
2026-03-20 10:17:10Lokesh Kumar, Nirmesh Shah, Ashishkumar P. Gudmalwar, Pankaj WasnikAbstract
Human communication seamlessly integrates speech and bodily motion, where hand gestures naturally complement vocal prosody to express intent, emotion, and emphasis. While recent text-to-speech (TTS) systems have begun incorporating multimodal cues such as facial expressions or lip movements, the role of hand gestures in shaping prosody remains largely underexplored. We propose a novel multimodal TTS framework, Gesture2Speech, that leverages visual gesture cues to modulate prosody in synthesized speech. Motivated by the observation that confident and expressive speakers coordinate gestures with vocal prosody, we introduce a multimodal Mixture-of-Experts (MoE) architecture that dynamically fuses linguistic content and gesture features within a dedicated style extraction module. The fused representation conditions an LLM-based speech decoder, enabling prosodic modulation that is temporally aligned with hand movements. We further design a gesture-speech alignment loss that explicitly models their temporal correspondence to ensure fine-grained synchrony between gestures and prosodic contours. Evaluations on the PATS dataset show that Gesture2Speech outperforms state-of-the-art baselines in both speech naturalness and gesture-speech synchrony. To the best of our knowledge, this is the first work to utilize hand gesture cues for prosody control in neural speech synthesis. Demo samples are available at this https URL
Abstract (translated)
人类交流无缝融合了言语与肢体动作，其中手势自然地与语音韵律相辅相成，以表达意图、情感和强调。尽管近期文本转语音（TTS）系统已开始整合面部表情或唇部动作等多模态线索，但手势在塑造韵律方面的作用仍 largely 未被探索。我们提出了一种新颖的多模态 TTS 框架 Gesture2Speech，该框架利用视觉手势线索来调节合成语音的韵律。受自信且富有表现力的演讲者会协调手势与语音韵律这一观察的启发，我们引入了一种多模态混合专家（MoE）架构，可在专用的风格提取模块内动态融合语言内容与手势特征。融合后的表征条件着一个基于大语言模型的语音解码器，从而实现与手部运动时间对齐的韵律调节。我们还设计了一种手势-语音对齐损失，显式建模它们的时间对应关系，以确保手势与韵律轮廓之间的细粒度同步。在 PATS 数据集上的评估表明，Gestuer2Speech 在语音自然度和手势-语音同步性两方面均优于最新基线方法。据我们所知，这是首个利用手部手势线索进行神经语音合成中韵律控制的工作。演示样本可在此 https URL 获取。
URL
https://arxiv.org/abs/2603.19831
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Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly
2026-03-18 14:58:17Qihao Lin, Borui Chen, Yuping Zhou, Jianing Wu, Yulan Guo, Weishi Zheng, Chongkun XiaAbstract
The contour estimation of transparent fragments is very important for autonomous reassembly, especially in the fields of precision optical instrument repair, cultural relic restoration, and identification of other precious device broken accidents. Different from general intact transparent objects, the contour estimation of transparent fragments face greater challenges due to strict optical properties, irregular shapes and edges. To address this issue, a general transparent fragments contour estimation framework based on visual-tactile fusion is proposed in this paper. First, we construct the transparent fragment dataset named TransFrag27K, which includes a multiscene synthetic data of broken fragments from multiple types of transparent objects, and a scalable synthetic data generation pipeline. Secondly, we propose a visual grasping position detection network named TransFragNet to identify, locate and segment the sampling grasping position. And, we use a two-finger gripper with Gelsight Mini sensors to obtain reconstructed tactile information of the lateral edge of the fragments. By fusing this tactile information with visual cues, a visual-tactile fusion material classifier is proposed. Inspired by the way humans estimate a fragment's contour combining vision and touch, we introduce a general transparent fragment contour estimation framework based on visual-tactile fusion, demonstrates strong performance in real-world validation. Finally, a multi-dimensional similarity metrics based contour matching and reassembly algorithm is proposed, providing a reproducible benchmark for evaluating visual-tactile contour estimation and fragment reassembly. The experimental results demonstrate the validity of the proposed framework. The dataset and codes are available at this https URL.
Abstract (translated)
透明碎片的轮廓估计对于自主重组至关重要，尤其在精密光学仪器修复、文物复原及其他珍贵设备破损事故鉴定等领域。与一般完好的透明物体不同，透明碎片的轮廓估计面临更大挑战，因其具有严格的光学特性、不规则的形状与边缘。针对此问题，本文提出一种基于视觉-触觉融合的通用透明碎片轮廓估计框架。首先，我们构建了名为TransFrag27K的透明碎片数据集，该数据集包含多类型透明物体破碎碎片的多场景合成数据，以及一个可扩展的合成数据生成流程。其次，我们提出了名为TransFragNet的视觉抓取位置检测网络，用于识别、定位并分割采样抓取位置。同时，我们使用配备Gelsight Mini传感器的双指夹爪获取碎片侧边的重建触觉信息。通过融合该触觉信息与视觉线索，提出了一种视觉-触觉融合材料分类器。受人类结合视觉与触觉估计碎片轮廓的方式启发，我们引入了基于视觉-触觉融合的通用透明碎片轮廓估计框架，并在实际验证中展现出强劲性能。最后，提出了一种基于多维相似度度量的轮廓匹配与重组算法，为评估视觉-触觉轮廓估计与碎片重组提供了可复现的基准。实验结果表明了所提框架的有效性。数据集与代码已公开于此https URL。
URL
https://arxiv.org/abs/2603.20290
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Geometric and Topological Deep Learning for Predicting Thermo-mechanical Performance in Cold Spray Deposition Process Modeling
2026-03-15 16:46:05Akshansh MishraAbstract
This study presents a geometric deep learning framework for predicting cold spray particle impact responses using finite element simulation data. A parametric dataset was generated through automated Abaqus simulations spanning a systematic range of particle velocity, particle temperature, and friction coefficient, yielding five output targets including maximum equivalent plastic strain, average contact plastic strain, maximum temperature, maximum von Mises stress, and deformation ratio. Four novel algorithms i.e. a GraphSAGE-style inductive graph neural network, a Chebyshev spectral graph convolution network, a topological data analysis augmented multilayer perceptron, and a geometric attention network were implemented and evaluated. Each input sample was treated as a node in a k-nearest-neighbour feature-space graph, enabling the models to exploit spatial similarity between process conditions during training. Three-dimensional feature space visualisations and two-dimensional contour projections confirmed the highly non-linear and velocity-dominated nature of the input-output relationships. Quantitative evaluation demonstrated that GraphSAGE and GAT consistently achieved R-square values exceeding 0.93 across most targets, with GAT attaining peak performance of R-square equal to 0.97 for maximum plastic strain. ChebSpectral and TDA-MLP performed considerably worse, yielding negative R-square values for several targets. These findings establish spatial graph-based neighbourhood aggregation as a robust and physically interpretable surrogate modelling strategy for cold spray process optimisation.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14478
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Echoes Across Centuries: Phonetic Signatures of Persian Poets
2026-03-15 15:41:21Kourosh Shahnazari, Seyed Moein Ayyoubzadeh, Mohammadali KeshtparvarAbstract
This study examines phonetic texture in Persian poetry as a literary-historical phenomenon rather than a by-product of meter or a feature used only for classification. The analysis draws on a large corpus of 1,116,306 mesras from 31,988 poems written by 83 poets, restricted to five major classical meters to enable controlled comparison. Each line is converted into a grapheme-to-phoneme representation and analyzed using six phonetic metrics: hardness, sonority, sibilance, vowel ratio, phoneme entropy, and consonant-cluster ratio. Statistical models estimate poet-level differences while controlling for meter, poetic form, and line length. The results show that although meter and form explain a substantial portion of phonetic variation, they do not eliminate systematic differences between poets. Persian poetic sound therefore appears as conditioned variation within shared prosodic structures rather than as either purely individual style or simple metrical residue. A multidimensional stylistic map reveals several recurrent phonetic profiles, including high-sonority lyric styles, hardness-driven rhetorical or epic styles, sibilant mystical contours, and high-entropy complex textures. Historical analysis indicates that phonetic distributions shift across centuries, reflecting changes in genre prominence, literary institutions, and performance contexts rather than abrupt stylistic breaks. The study establishes a corpus-scale framework for phonetic analysis in Persian poetry and demonstrates how computational phonetics can contribute to literary-historical interpretation while remaining attentive to the formal structures that shape Persian verse.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14443
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Deep EM with Hierarchical Latent Label Modelling for Multi-Site Prostate Lesion Segmentation
2026-03-15 15:03:20Wen Yan, Yipei Wang, Shiqi Huang, Natasha Thorley, Mark Emberton, Vasilis Stavrinides, Yipeng Hu, Dean BarrattAbstract
Label variability is a major challenge for prostate lesion segmentation. In multi-site datasets, annotations often reflect centre-specific contouring protocols, causing segmentation networks to overfit to local styles and generalise poorly to unseen sites in inference. We treat each observed annotation as a noisy observation of an underlying latent 'clean' lesion mask, and propose a hierarchical expectation-maximisation (HierEM) framework that alternates between: (1) inferring a voxel-wise posterior distribution over the latent mask, and (2) training a CNN using this posterior as a soft target and estimate site-specific sensitivity and specificity under a hierarchical prior. This hierarchical prior decomposes label-quality into a global mean with site- and case-level deviations, reducing site-specific bias by penalising the likelihood term contributed only by site deviations. Experiments on three cohorts demonstrate that the proposed hierarchical EM framework enhances cross-site generalisation compared to state-of-the-art methods. For pooled-dataset evaluation, the per-site mean DSC ranges from 29.50% to 39.69%; for leave-one-site-out generalisation, it ranges from 27.91% to 32.67%, yielding statistically significant improvements over comparison methods (p<0.039). The method also produces interpretable per-site latent label-quality estimates (sensitivity alpha ranges from 31.5% to 47.3% at specificity beta approximates 0.99), supporting post-hoc analyses of cross-site annotation variability. These results indicate that explicitly modelling site-dependent annotation can improve cross-site generalisation.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14418
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Walking Further: Semantic-aware Multimodal Gait Recognition Under Long-Range Conditions
2026-03-15 02:32:27Zhiyang Lu, Wen Jiang, Tianren Wu, Zhichao Wang, Changwang Zhang, Siqi Shen, Ming ChengAbstract
Gait recognition is an emerging biometric technology that enables non-intrusive and hard-to-spoof human identification. However, most existing methods are confined to short-range, unimodal settings and fail to generalize to long-range and cross-distance scenarios under real-world conditions. To address this gap, we present \textbf{LRGait}, the first LiDAR-Camera multimodal benchmark designed for robust long-range gait recognition across diverse outdoor distances and environments. We further propose \textbf{EMGaitNet}, an end-to-end framework tailored for long-range multimodal gait recognition. To bridge the modality gap between RGB images and point clouds, we introduce a semantic-guided fusion pipeline. A CLIP-based Semantic Mining (SeMi) module first extracts human body-part-aware semantic cues, which are then employed to align 2D and 3D features via a Semantic-Guided Alignment (SGA) module within a unified embedding space. A Symmetric Cross-Attention Fusion (SCAF) module hierarchically integrates visual contours and 3D geometric features, and a Spatio-Temporal (ST) module captures global gait dynamics. Extensive experiments on various gait datasets validate the effectiveness of our method.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14189
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Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interaction
2026-03-11 18:24:07Jingxing Zhong, Qingtao Pan, Xuchang Zhou, Jiazhen Lin, Xinguo ZhuangAbstract
Breast cancer is one of the most common causes of death among women worldwide, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods have limitations in accurately locating tumor contours due to the challenge of low contrast between cancer and normal areas and blurred boundaries. Leveraging text prompt information holds promise in ameliorating tumor segmentation effect by delineating segmentation regions. Inspired by this, we propose text-guided Breast Tumor Segmentation model (TextBCS) with stage-divided vision-language interaction and evidential learning. Specifically, the proposed stage-divided vision-language interaction facilitates information mutual between visual and text features at each stage of down-sampling, further exerting the advantages of text prompts to assist in locating lesion areas in low contrast scenarios. Moreover, the evidential learning is adopted to quantify the segmentation uncertainty of the model for blurred boundary. It utilizes the variational Dirichlet to characterize the distribution of the segmentation probabilities, addressing the segmentation uncertainties of the boundaries. Extensive experiments validate the superiority of our TextBCS over other segmentation networks, showcasing the best breast tumor segmentation performance on publicly available datasets.
Abstract (translated)
乳腺癌是全球女性最常见的死亡原因之一，每年有数百万人因此丧生。磁共振成像（MRI）能够提供多种序列来表征肿瘤形态和内部模式，并已成为检测和诊断乳腺肿瘤的有效工具。然而，基于深度学习的肿瘤分割方法由于癌症区域与正常区域对比度低以及边界模糊的问题，在准确定位肿瘤轮廓方面存在局限性。利用文本提示信息有望通过勾勒出分割区域来改善肿瘤分割效果。受此启发，我们提出了一个由分阶段视觉-语言交互和证据学习引导的文字辅助乳腺肿瘤分割模型（TextBCS）。具体来说，所提出的分阶段视觉-语言交互促进了在每次下采样过程中视觉特征与文本特征之间的信息交流，在低对比度场景中进一步发挥了文本提示定位病灶区域的优势。此外，采用证据学习来量化模糊边界处的分割不确定性。它利用变分狄利克雷来刻画分割概率分布，从而解决了边界的分割不确定性问题。广泛的实验验证了我们的TextBCS模型相较于其他分割网络的优越性，并在公开数据集上展示了最佳的乳腺肿瘤分割性能。
URL
https://arxiv.org/abs/2603.11206
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A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation
2026-03-10 10:00:01Yoon Jo Kim, Wonyoung Cho, Jongmin Lee, Han Joo Chae, Hyunki Park, Sang Hoon Seo, Noh Jae Myung, Kyungmi Yang, Dongryul Oh, Jin Sung KimAbstract
Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers. While deep learning models automate this process, their rigid reliance on expert-annotated data requires costly retraining whenever clinical guidelines update. To overcome this limitation, we introduce OncoAgent, a novel guideline-aware AI agent framework that seamlessly converts textual clinical guidelines into three-dimensional target contours in a training-free manner. Evaluated on esophageal cancer cases, the agent achieves a zero-shot Dice similarity coefficient of 0.842 for the CTV and 0.880 for the planning target volume, demonstrating performance highly comparable to a fully supervised nnU-Net baseline. Notably, in a blinded clinical evaluation, physicians strongly preferred OncoAgent over the supervised baseline, rating it higher in guideline compliance, modification effort, and clinical acceptability. Furthermore, the framework generalizes zero-shot to alternative esophageal guidelines and other anatomical sites (e.g., prostate) without any retraining. Beyond mere volumetric overlap, our agent-based paradigm offers near-instantaneous adaptability to alternative guidelines, providing a scalable and transparent pathway toward interpretability in radiotherapy treatment planning.
Abstract (translated)
在放射治疗中，划定临床靶区体积（CTV）涉及根据肿瘤位置和解剖障碍来确定复杂的边界。尽管深度学习模型能够自动化这一过程，但它们对专家标注数据的严格依赖意味着每次临床指南更新时都需要进行昂贵的数据再训练。为了解决这个问题，我们引入了OncoAgent，这是一种新型的基于指南的人工智能代理框架，它可以在无需培训的情况下将文本形式的临床指南转换成三维目标轮廓。在针对食管癌病例的评估中，该代理实现了CTV和计划靶区体积（PTV）上的零样本Dice相似系数分别为0.842和0.880，显示出了与全监督nnU-Net基线相比高度相当的表现。值得注意的是，在盲法临床评价中，医生们更倾向于选择OncoAgent而不是监督学习的基准模型，他们认为该代理在遵守指南、修改努力以及临床接受度方面优于其他方法。此外，该框架能够在没有再训练的情况下零样本泛化到不同的食管指南以及其他解剖部位（如前列腺）上。除了体积重叠之外，我们的基于代理的方法还提供了近乎即时地适应不同指南的能力，为放射治疗计划的可解释性提供了一条可扩展且透明的道路。
URL
https://arxiv.org/abs/2603.09448
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Structure and Progress Aware Diffusion for Medical Image Segmentation
2026-03-09 02:05:07Siyuan Song, Guyue Hu, Chenglong Li, Dengdi Sun, Zhe Jin, Jin TangAbstract
Medical image segmentation is crucial for computer-aided diagnosis, which necessitates understanding both coarse morphological and semantic structures, as well as carving fine boundaries. The morphological and semantic structures in medical images are beneficial and stable clues for target understanding. While the fine boundaries of medical targets (like tumors and lesions) are usually ambiguous and noisy since lesion overlap, annotation uncertainty, and so on, making it not reliable to serve as early supervision. However, existing methods simultaneously learn coarse structures and fine boundaries throughout the training process. In this paper, we propose a structure and progress-aware diffusion (SPAD) for medical image segmentation, which consists of a semantic-concentrated diffusion (ScD) and a boundary-centralized diffusion (BcD) modulated by a progress-aware scheduler (PaS). Specifically, the semantic-concentrated diffusion introduces anchor-preserved target perturbation, which perturbs pixels within a medical target but preserves unaltered areas as semantic anchors, encouraging the model to infer noisy target areas from the surrounding semantic context. The boundary-centralized diffusion introduces progress-aware boundary noise, which blurs unreliable and ambiguous boundaries, thus compelling the model to focus on coarse but stable anatomical morphology and global semantics. Furthermore, the progress-aware scheduler gradually modulates noise intensity of the ScD and BcD forming a coarse-to-fine diffusion paradigm, which encourage focusing on coarse morphological and semantic structures during early target understanding stages and gradually shifting to fine target boundaries during later contour adjusting stages.
Abstract (translated)
医学图像分割对于计算机辅助诊断至关重要，这需要理解粗略的形态结构和语义结构，并且能够精细地划分边界。在医学图像中的形态和语义结构是理解和定位目标的重要而稳定的线索。然而，医疗目标（如肿瘤和病变）的细小边界通常模糊不清并且带有噪声，由于病灶重叠、标注不确定等因素，这些信息不足以作为早期监督使用。尽管如此，现有的方法在整个训练过程中同时学习粗略结构与精细边界。 在本文中，我们提出了一种基于结构和进度感知的扩散（SPAD）方法用于医学图像分割，该方法由一个语义集中的扩散（ScD）、边界中心化的扩散（BcD），以及一个进度感知调度器（PaS）组成。具体来说： - 语义集中的扩散通过保持锚点不变的目标扰动引入，这种扰动只影响目标内部的像素而不改变背景区域作为语义锚点，从而鼓励模型从周围的语义上下文中推断噪声较大的目标区域。 - 边界中心化的扩散则引入进度感知边界噪声，模糊不明确和不可靠的边界，促使模型专注于粗略但稳定的解剖结构及全局语义。 此外，进度感知调度器逐步调整ScD和BcD的噪声强度形成一个从粗到细的扩散范式。这在早期目标理解阶段鼓励模型关注粗糙的形态学和语义结构，并逐渐过渡到后期轮廓调整阶段专注于细致的目标边界。 这种方法通过分阶段的学习策略解决了现有方法中同时学习粗略结构与精细边界的挑战，提高了医学图像分割任务中的精度和鲁棒性。
URL
https://arxiv.org/abs/2603.07889
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Efficient RGB-D Scene Understanding via Multi-task Adaptive Learning and Cross-dimensional Feature Guidance
2026-03-08 10:09:26Guodong Sun, Junjie Liu, Gaoyang Zhang, Bo Wu, Yang ZhangAbstract
Scene understanding plays a critical role in enabling intelligence and autonomy in robotic systems. Traditional approaches often face challenges, including occlusions, ambiguous boundaries, and the inability to adapt attention based on task-specific requirements and sample variations. To address these limitations, this paper presents an efficient RGB-D scene understanding model that performs a range of tasks, including semantic segmentation, instance segmentation, orientation estimation, panoptic segmentation, and scene classification. The proposed model incorporates an enhanced fusion encoder, which effectively leverages redundant information from both RGB and depth inputs. For semantic segmentation, we introduce normalized focus channel layers and a context feature interaction layer, designed to mitigate issues such as shallow feature misguidance and insufficient local-global feature representation. The instance segmentation task benefits from a non-bottleneck 1D structure, which achieves superior contour representation with fewer parameters. Additionally, we propose a multi-task adaptive loss function that dynamically adjusts the learning strategy for different tasks based on scene variations. Extensive experiments on the NYUv2, SUN RGB-D, and Cityscapes datasets demonstrate that our approach outperforms existing methods in both segmentation accuracy and processing speed.
Abstract (translated)
场景理解在增强机器人系统的智能和自主性方面扮演着关键角色。传统方法经常面临遮挡、边界模糊以及无法根据任务特定需求和样本变化调整注意力等问题的挑战。为了克服这些限制，本文提出了一种高效的RGB-D场景理解模型，该模型能够执行一系列任务，包括语义分割、实例分割、姿态估计、全景分割和场景分类。所提出的模型包含了一个增强融合编码器，可以有效利用来自RGB和深度输入数据的冗余信息。 对于语义分割任务，我们引入了标准化关注通道层和上下文特征交互层，旨在解决浅层特征误导以及局部-全局特征表示不足等问题。在实例分割任务中，我们的模型采用了一种非瓶颈1D结构，在减少参数的同时实现了更优的轮廓表示。此外，我们还提出了一种多任务自适应损失函数，可以根据场景变化动态调整不同的学习策略。 通过在NYUv2、SUN RGB-D和Cityscapes数据集上的大量实验验证，我们的方法不仅在分割准确性上超越了现有技术，而且在处理速度方面也表现出色。
URL
https://arxiv.org/abs/2603.07570
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Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis
2026-03-08 09:07:26Carlos Franzreb, Francisco Teixeira, Ben Luks, Sebastian M\"oller, Alberto AbadAbstract
Automatic detection of Parkinson's disease (PD) from speech is a promising non-invasive diagnostic tool, but it raises significant privacy concerns. Speaker anonymization mitigates these risks, but it may suppress the pathological information necessary for PD detection. We assess the trade-off between privacy and PD detection for two anonymizers (STT-TTS and kNN-VC) using two Spanish datasets. STT-TTS provides better privacy but severely degrades PD detection by eradicating prosodic information. kNN-VC preserves macro-prosodic features such as duration and F0 contours, achieving F1 scores only 3-7\% lower than original baselines, demonstrating that privacy-preserving PD detection is viable when using appropriate anonymization. Finally, an acoustic distortion analysis characterizes specific weaknesses in kNN-VC, offering insights for designing anonymizers that better preserve PD information.
Abstract (translated)
从语音中自动检测帕金森病（PD）是一种有前景的非侵入性诊断工具，但这种方法会引发重大的隐私问题。说话人匿名化技术可以减轻这些风险，但它可能会抑制用于PD检测所需的关键病理信息。我们使用两个西班牙语数据集评估了两种匿名化方法（STT-TTS和kNN-VC）在保护隐私与PD检测准确性之间的权衡。 STT-TTS提供了更好的隐私保护，但这种方法会严重降低PD的检测能力，因为它消除了语音中的韵律特征。另一方面，kNN-VC能够保持音长、基频轮廓等宏观韵律特性，在两个数据集中实现的F1得分仅比原始基准低3%-7%，这表明在使用适当的匿名化技术时，可以实现既保护隐私又具有较高检测准确性的PD诊断。 最后，声学失真分析揭示了kNN-VC的一些特定弱点，并为设计能够在保持PD相关信息的同时更好地保护隐私的技术提供了见解。
URL
https://arxiv.org/abs/2603.07544
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AI End-to-End Radiation Treatment Planning Under One Second
2026-03-06 14:45:44Simon Arberet, Riqiang Gao, Martin Kraus, Florin C. Ghesu, Wilko Verbakel, Mamadou Diallo, Anthony Magliari, Venkatesan Karuppusamy, Sushil Beriwal, REQUITE Consortium, Ali Kamen, Dorin ComaniciuAbstract
Artificial intelligence-based radiation therapy (RT) planning has the potential to reduce planning time and inter-planner variability, improving efficiency and consistency in clinical workflows. Most existing automated approaches rely on multiple dose evaluations and corrections, resulting in plan generation times of several minutes. We introduce AIRT (Artificial Intelligence-based Radiotherapy), an end-to-end deep-learning framework that directly infers deliverable treatment plans from CT images and structure contours. AIRT generates single-arc VMAT prostate plans, from imaging and anatomical inputs to leaf sequencing, in under one second on a single Nvidia A100 GPU. The framework includes a differentiable dose feedback, an adversarial fluence map shaping, and a plan generation augmentation to improve plan quality and robustness. The model was trained on more than 10,000 intact prostate cases. Non-inferiority to RapidPlan Eclipse was demonstrated across target coverage and OAR sparing metrics. Target homogeneity (HI = 0.10 $\pm$ 0.01) and OAR sparing were similar to reference plans when evaluated using AcurosXB. These results represent a significant step toward ultra-fast standardized RT planning and a streamlined clinical workflow.
Abstract (translated)
基于人工智能的放射治疗（RT）计划有可能减少规划时间并降低不同规划者之间的差异，从而提高临床工作流程中的效率和一致性。大多数现有的自动化方法依赖于多次剂量评估和修正，导致生成方案的时间长达几分钟。我们介绍了AIRT（基于人工智能的放射治疗），这是一种端到端深度学习框架，可以直接从CT图像和结构轮廓中推断出可交付的治疗计划。AIRT能够在一个Nvidia A100 GPU上，在不到一秒的时间内，根据影像学及解剖学输入生成单弧VMAT前列腺治疗方案，并进行叶片序列设计。该框架包括一个可微剂量反馈、对抗性束流分布整形以及一种用于提高计划质量和鲁棒性的计划生成增强方法。模型是在超过10,000例完整前列腺病例上训练出来的。AIRT在靶区覆盖和器官保护指标方面与RapidPlan Eclipse表现无显著差异。使用AcurosXB评估时，靶区均匀度（HI = 0.10 ± 0.01）和组织保护措施与参考方案相似。 这些结果代表了向超快速标准化放射治疗计划和精简临床工作流程迈出的重要一步。
URL
https://arxiv.org/abs/2603.06338
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Privacy-Aware Camera 2.0 Technical Report
2026-03-05 03:46:20Huan Song, Shuyu Tian, Ting Long, Jiang Liu, Cheng Yuan, Zhenyu Jia, Jiawei Shao, Xuelong LiAbstract
With the increasing deployment of intelligent sensing technologies in highly sensitive environments such as restrooms and locker rooms, visual surveillance systems face a profound privacy-security paradox. Existing privacy-preserving approaches, including physical desensitization, encryption, and obfuscation, often compromise semantic understanding or fail to ensure mathematically provable irreversibility. Although Privacy Camera 1.0 eliminated visual data at the source to prevent leakage, it provided only textual judgments, leading to evidentiary blind spots in disputes. To address these limitations, this paper proposes a novel privacy-preserving perception framework based on the AI Flow paradigm and a collaborative edge-cloud architecture. By deploying a visual desensitizer at the edge, raw images are transformed in real time into abstract feature vectors through nonlinear mapping and stochastic noise injection under the Information Bottleneck principle, ensuring identity-sensitive information is stripped and original images are mathematically unreconstructable. The abstract representations are transmitted to the cloud for behavior recognition and semantic reconstruction via a "dynamic contour" visual language, achieving a critical balance between perception and privacy while enabling illustrative visual reference without exposing raw images.
Abstract (translated)
随着智能传感技术在如卫生间和更衣室等高度敏感环境中的部署日益增加，视觉监控系统面临着一个深刻的隐私与安全矛盾。现有的隐私保护方法，包括物理去敏感化、加密和混淆处理，在提供语义理解方面往往存在缺陷，或者无法确保数学意义上的不可逆性。尽管Privacy Camera 1.0通过在源头消除视觉数据来防止泄露，但它仅提供了文本判断结果，这在争议中导致了证据盲点的出现。为解决这些限制，本文提出了一种基于AI流范式和协作边缘-云架构的新颖隐私保护感知框架。 该方案通过在边缘部署视觉去敏感化器，在非线性映射和信息瓶颈原则下注入随机噪声，将原始图像实时转换为抽象特征向量。这样确保了身份相关的信息被剥离且原始图像无法从数学上重建。接着，这些抽象表示传输到云端进行行为识别及语义重构，通过“动态轮廓”视觉语言实现感知与隐私之间的关键平衡，同时支持可视化参考而不暴露原始图像。
URL
https://arxiv.org/abs/2603.04775
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Perception-Aware Time-Optimal Planning for Quadrotor Waypoint Flight
2026-03-04 17:22:16Chao Qin, Jiaxu Xing, Rudolf Reiter, Angel Romero, Yifan Lin, Hugh H. -T. Liu, Davide ScaramuzzaAbstract
Agile quadrotor flight pushes the limits of control, actuation, and onboard perception. While time-optimal trajectory planning has been extensively studied, existing approaches typically neglect the tight coupling between vehicle dynamics, environmental geometry, and the visual requirements of onboard state estimation. As a result, trajectories that are dynamically feasible may fail in closed-loop execution due to degraded visual quality. This paper introduces a unified time-optimal trajectory optimization framework for vision-based quadrotors that explicitly incorporates perception constraints alongside full nonlinear dynamics, rotor actuation limits, aerodynamic effects, camera field-of-view constraints, and convex geometric gate representations. The proposed formulation solves minimum-time lap trajectories for arbitrary racetracks with diverse gate shapes and orientations, while remaining numerically robust and computationally efficient. We derive an information-theoretic position uncertainty metric to quantify visual state-estimation quality and integrate it into the planner through three perception objectives: position uncertainty minimization, sequential field-of-view constraints, and look-ahead alignment. This enables systematic exploration of the trade-offs between speed and perceptual reliability. To accurately track the resulting perception-aware trajectories, we develop a model predictive contouring tracking controller that separates lateral and progress errors. Experiments demonstrate real-world flight speeds up to 9.8 m/s with 0.07 m average tracking error, and closed-loop success rates improved from 55% to 100% on a challenging Split-S course. The proposed system provides a scalable benchmark for studying the fundamental limits of perception-aware, time-optimal autonomous flight.
Abstract (translated)
本文介绍了基于视觉的四旋翼无人机时间最优轨迹优化框架，该框架明确地将感知约束与完整的非线性动力学、电机操作限制、气动效应、摄像机视场约束以及凸几何门表示结合在一起。现有的方法通常忽略了车辆动态特性、环境几何形状和车载状态估计所需的视觉需求之间的紧密联系，导致虽然某些轨迹在动力学上可行，但在闭环执行中由于视觉质量下降而失败。 该提出的框架能够为具有各种门形状和方向的任意赛道生成最小时间圈迹，同时保持数值稳健性和计算效率。我们引入了一个信息论位置不确定性度量来量化基于视觉的状态估计质量，并通过三个感知目标将其集成到规划器中：减少位置不确定性、顺序视场约束以及前瞻对齐。这使系统能够有条不紊地探索速度与感知可靠性的权衡。 为了准确跟踪这些感知敏感轨迹，我们开发了一种模型预测轮廓追踪控制器，该控制器将横向和进度误差分开处理。实验表明，在具有挑战性的“Split-S”赛道上，闭环成功几率从55%提高到了100%，同时实现了高达9.8米/秒的现实世界飞行速度，并且平均跟踪误差为0.07米。 所提出的系统为研究感知意识的时间最优自主飞行的基本限制提供了可扩展的基准。
URL
https://arxiv.org/abs/2603.04305
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High-Resolution Image Reconstruction with Unsupervised Learning and Noisy Data Applied to Ion-Beam Dynamics for Particle Accelerators
2026-03-04 10:20:44Francis Osswald (IPHC), Mohammed Chahbaoui (UNISTRA), Xinyi Liang (SU)Abstract
Image reconstruction in the presence of severe degradation remains a challenging inverse problem, particularly in beam diagnostics for high-energy physics accelerators. As modern facilities demand precise detection of beam halo structures to control losses, traditional analysis tools have reached their performance limits. This work reviews existing image-processing techniques for data cleaning, contour extraction, and emittance reconstruction, and introduces a novel approach based on convolutional filtering and neural networks with optimized early-stopping strategies in order to control overfitting. Despite the absence of training datasets, the proposed unsupervised framework achieves robust denoising and high-fidelity reconstruction of beam emittance images under low signal-to-noise conditions. The method extends measurable amplitudes beyond seven standard deviations, enabling unprecedented halo resolution.
Abstract (translated)
在严重退化情况下进行图像重建仍然是一个具有挑战性的逆向问题，特别是在高能物理加速器的束流诊断中。随着现代设施对精确检测束晕结构以控制损失的需求日益增加，传统的分析工具已达到其性能极限。本文回顾了现有的数据清洗、轮廓提取和发射度重建的图像处理技术，并提出了一种基于卷积滤波和优化早期停止策略的神经网络的新方法，以控制过拟合问题。尽管缺乏训练数据集，所提出的无监督框架在低信噪比条件下实现了对束流发射度图像的稳健去噪和高保真重构。该方法将可测量幅度扩展到七个标准差以上，从而实现前所未有的束晕分辨率。
URL
https://arxiv.org/abs/2603.06689
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