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Click, Predict, Trust: Clinician-in-the-Loop AI Segmentation for Lung Cancer CT-Based Prognosis within the Knowledge-to-Action Framework
2025-10-19 23:02:43Mohammad R. Salmanpour, Sonya Falahati, Amir Hossein Pouria, Amin Mousavi, Somayeh Sadat Mehrnia, Morteza Alizadeh, Arman Gorji, Zeinab Farsangi, Alireza Safarian, Mehdi Maghsudi, Carlos Uribe, Arman Rahmim, Ren YuanAbstract
Lung cancer remains the leading cause of cancer mortality, with CT imaging central to screening, prognosis, and treatment. Manual segmentation is variable and time-intensive, while deep learning (DL) offers automation but faces barriers to clinical adoption. Guided by the Knowledge-to-Action framework, this study develops a clinician-in-the-loop DL pipeline to enhance reproducibility, prognostic accuracy, and clinical trust. Multi-center CT data from 999 patients across 12 public datasets were analyzed using five DL models (3D Attention U-Net, ResUNet, VNet, ReconNet, SAM-Med3D), benchmarked against expert contours on whole and click-point cropped images. Segmentation reproducibility was assessed using 497 PySERA-extracted radiomic features via Spearman correlation, ICC, Wilcoxon tests, and MANOVA, while prognostic modeling compared supervised (SL) and semi-supervised learning (SSL) across 38 dimensionality reduction strategies and 24 classifiers. Six physicians qualitatively evaluated masks across seven domains, including clinical meaningfulness, boundary quality, prognostic value, trust, and workflow integration. VNet achieved the best performance (Dice = 0.83, IoU = 0.71), radiomic stability (mean correlation = 0.76, ICC = 0.65), and predictive accuracy under SSL (accuracy = 0.88, F1 = 0.83). SSL consistently outperformed SL across models. Radiologists favored VNet for peritumoral representation and smoother boundaries, preferring AI-generated initial masks for refinement rather than replacement. These results demonstrate that integrating VNet with SSL yields accurate, reproducible, and clinically trusted CT-based lung cancer prognosis, highlighting a feasible path toward physician-centered AI translation.
Abstract (translated)
肺癌仍然是癌症死亡的主要原因，CT成像在筛查、预后和治疗中处于核心地位。手动分割过程存在可变性和耗时的问题，而深度学习（DL）虽然提供了自动化解决方案，但面临着临床应用的障碍。本研究遵循知识到行动框架，开发了一种由医生参与的深度学习管道，以提高再现性、预测准确性以及临床信任度。 通过使用来自12个公共数据集中的999名患者的多中心CT数据，五种深度学习模型（3D注意力U-Net、ResUNet、VNet、ReconNet和SAM-Med3D）在完整图像与点击点裁剪的图像上进行对比测试，以专家轮廓为基准。通过Spearman相关性、ICC（组内一致性系数）、Wilcoxon检验及MANOVA对分割再现性的497个PySERA提取放射学特征进行了评估；同时通过38种降维策略和24种分类器比较了监督学习（SL）与半监督学习（SSL）的预后建模。六位医生从七个领域，包括临床意义、边界质量、预测价值、信任度及工作流程整合对掩码进行定性评估。 VNet在性能上表现出色（Dice系数=0.83，IoU=0.71），放射学稳定性表现优异（平均相关性=0.76，ICC=0.65）并在SSL下具有更高的预测准确度（准确性=0.88，F1值=0.83）。在各种模型上，SSL始终优于SL。放射科医生更倾向于使用VNet进行肿瘤周围组织的表示及平滑边界，并且他们偏好AI生成的初步掩码供后续细化而非替代现有操作。 这些结果表明，结合VNet与SSL可以为基于CT图像肺癌预后的准确、可重复性以及临床可信度提供一条可行路径。这强调了一种医生为中心的人工智能转化方法的有效应用。
URL
https://arxiv.org/abs/2510.17039
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Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI
2025-10-17 14:26:30Gerard Comas-Quiles, Carles Garcia-Cabrera, Julia Dietlmeier, Noel E. O'Connor, Ferran MarquesAbstract
Unsupervised anomaly detection (UAD) presents a complementary alternative to supervised learning for brain tumor segmentation in magnetic resonance imaging (MRI), particularly when annotated datasets are limited, costly, or inconsistent. In this work, we propose a novel Multimodal Vision Transformer Autoencoder (MViT-AE) trained exclusively on healthy brain MRIs to detect and localize tumors via reconstruction-based error maps. This unsupervised paradigm enables segmentation without reliance on manual labels, addressing a key scalability bottleneck in neuroimaging workflows. Our method is evaluated in the BraTS-GoAT 2025 Lighthouse dataset, which includes various types of tumors such as gliomas, meningiomas, and pediatric brain tumors. To enhance performance, we introduce a multimodal early-late fusion strategy that leverages complementary information across multiple MRI sequences, and a post-processing pipeline that integrates the Segment Anything Model (SAM) to refine predicted tumor contours. Despite the known challenges of UAD, particularly in detecting small or non-enhancing lesions, our method achieves clinically meaningful tumor localization, with lesion-wise Dice Similarity Coefficient of 0.437 (Whole Tumor), 0.316 (Tumor Core), and 0.350 (Enhancing Tumor) on the test set, and an anomaly Detection Rate of 89.4% on the validation set. These findings highlight the potential of transformer-based unsupervised models to serve as scalable, label-efficient tools for neuro-oncological imaging.
Abstract (translated)
无监督异常检测（UAD）为磁共振成像（MRI）中的脑肿瘤分割提供了一种补充的替代方案，尤其是在标注数据集有限、成本高昂或不一致的情况下。在这项工作中，我们提出了一种新颖的多模态视觉变压器自编码器（MViT-AE），该模型仅在健康的大脑MRI上进行训练，通过基于重建的误差图来检测和定位肿瘤。这种无监督方法使分割可以无需依赖人工标注标签，解决了神经影像工作流程中的关键可扩展性瓶颈问题。我们的方法在BraTS-GoAT 2025灯塔数据集中进行了评估，该数据集包括多种类型的肿瘤，如胶质瘤、脑膜瘤和儿童脑肿瘤。 为了提高性能，我们引入了一种多模态早期-晚期融合策略，利用了多个MRI序列之间的互补信息，并且采用了一个后处理管道，将Segment Anything Model（SAM）整合进来以精炼预测的肿瘤轮廓。尽管UAD在检测小病变或非增强性病灶方面存在已知挑战，我们的方法仍然实现了具有临床意义的肿瘤定位，在测试集上全瘤体、肿瘤核心和增强型肿瘤的Dice相似系数分别为0.437、0.316和0.350，并且在验证集上的异常检测率为89.4%。这些发现突显了基于变压器的无监督模型作为神经肿瘤学成像中的高效可扩展工具的巨大潜力。
URL
https://arxiv.org/abs/2510.15684
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Mobile Coverage Analysis using Crowdsourced Data
Abstract
Effective assessment of mobile network coverage and the precise identification of service weak spots are paramount for network operators striving to enhance user Quality of Experience (QoE). This paper presents a novel framework for mobile coverage and weak spot analysis utilising crowdsourced QoE data. The core of our methodology involves coverage analysis at the individual cell (antenna) level, subsequently aggregated to the site level, using empirical geolocation data. A key contribution of this research is the application of One-Class Support Vector Machine (OC-SVM) algorithm for calculating mobile network coverage. This approach models the decision hyperplane as the effective coverage contour, facilitating robust calculation of coverage areas for individual cells and entire sites. The same methodology is extended to analyse crowdsourced service loss reports, thereby identifying and quantifying geographically localised weak spots. Our findings demonstrate the efficacy of this novel framework in accurately mapping mobile coverage and, crucially, in highlighting granular areas of signal deficiency, particularly within complex urban environments.
Abstract (translated)
有效评估移动网络覆盖范围并精确识别服务弱点对于运营商提升用户体验质量（QoE）至关重要。本文提出了一种利用众包QoE数据进行移动覆盖和弱点分析的新框架。我们的方法核心在于基于实证地理位置数据，对单个小区（天线）级别的覆盖情况进行分析，并将其汇总到站点级别。这项研究的重要贡献之一是应用了一类支持向量机（OC-SVM）算法来计算移动网络的覆盖范围。这种方法将决策超平面作为有效的覆盖轮廓进行建模，从而能够为单个小区乃至整个站点精确地计算出覆盖区域。同样的方法被扩展用于分析众包的服务中断报告，以便识别和量化地理位置特定的弱点。 研究结果表明，这种新框架在准确绘制移动网络覆盖范围方面十分有效，并且特别强调了复杂城市环境中信号薄弱的具体区域。这种方法能够帮助运营商更加细致地了解其服务中的问题所在，从而有针对性地进行改进，提升用户的整体体验质量。
URL
https://arxiv.org/abs/2510.13459
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Self-Supervised Selective-Guided Diffusion Model for Old-Photo Face Restoration
2025-10-14 03:34:15Wenjie Li, Xiangyi Wang, Heng Guo, Guangwei Gao, Zhanyu MaAbstract
Old-photo face restoration poses significant challenges due to compounded degradations such as breakage, fading, and severe blur. Existing pre-trained diffusion-guided methods either rely on explicit degradation priors or global statistical guidance, which struggle with localized artifacts or face color. We propose Self-Supervised Selective-Guided Diffusion (SSDiff), which leverages pseudo-reference faces generated by a pre-trained diffusion model under weak guidance. These pseudo-labels exhibit structurally aligned contours and natural colors, enabling region-specific restoration via staged supervision: structural guidance applied throughout the denoising process and color refinement in later steps, aligned with the coarse-to-fine nature of diffusion. By incorporating face parsing maps and scratch masks, our method selectively restores breakage regions while avoiding identity mismatch. We further construct VintageFace, a 300-image benchmark of real old face photos with varying degradation levels. SSDiff outperforms existing GAN-based and diffusion-based methods in perceptual quality, fidelity, and regional controllability. Code link: this https URL.
Abstract (translated)
旧照片面部恢复面临着由断裂、褪色和严重模糊等多重退化造成的重大挑战。现有预训练的扩散引导方法要么依赖明确的降级先验，要么依靠全局统计指导，这些方法在处理局部伪影或面部颜色方面存在困难。我们提出了一种自监督选择性引导扩散（SSDiff）方法，该方法利用弱引导下由预训练扩散模型生成的伪参考面孔。这些伪标签表现出结构对齐的轮廓和自然的颜色，使区域特异性恢复通过分阶段监督得以实现：在整个去噪过程中应用结构性指导，并在后续步骤中进行颜色细化，与从粗到细的扩散性质相吻合。通过结合面部解析图和划痕掩码，我们的方法选择性地修复断裂区域，同时避免身份不符的问题。 我们进一步构建了VintageFace，这是一个包含300张具有不同退化程度的真实旧面孔照片的数据集基准。SSDiff在感知质量、保真度和区域可控性方面优于现有的基于GAN的方法和扩散方法。代码链接：[此处提供URL]。
URL
https://arxiv.org/abs/2510.12114
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APGNet: Adaptive Prior-Guided for Underwater Camouflaged Object Detection
2025-10-14 01:51:44Xinxin Huang, Han Sun, Junmin Cai, Ningzhong Liu, Huiyu ZhouAbstract
Detecting camouflaged objects in underwater environments is crucial for marine ecological research and resource exploration. However, existing methods face two key challenges: underwater image degradation, including low contrast and color distortion, and the natural camouflage of marine organisms. Traditional image enhancement techniques struggle to restore critical features in degraded images, while camouflaged object detection (COD) methods developed for terrestrial scenes often fail to adapt to underwater environments due to the lack of consideration for underwater optical characteristics. To address these issues, we propose APGNet, an Adaptive Prior-Guided Network, which integrates a Siamese architecture with a novel prior-guided mechanism to enhance robustness and detection accuracy. First, we employ the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm for data augmentation, generating illumination-invariant images to mitigate degradation effects. Second, we design an Extended Receptive Field (ERF) module combined with a Multi-Scale Progressive Decoder (MPD) to capture multi-scale contextual information and refine feature representations. Furthermore, we propose an adaptive prior-guided mechanism that hierarchically fuses position and boundary priors by embedding spatial attention in high-level features for coarse localization and using deformable convolution to refine contours in low-level features. Extensive experimental results on two public MAS datasets demonstrate that our proposed method APGNet outperforms 15 state-of-art methods under widely used evaluation metrics.
Abstract (translated)
在水下环境中检测伪装物体对于海洋生态研究和资源勘探至关重要。然而，现有方法面临着两个主要挑战：水中图像退化（包括低对比度和颜色失真）以及海洋生物的天然伪装。传统图像增强技术难以恢复受损图像中的关键特征，而为陆地场景开发的伪装物体检测(COD) 方法由于缺乏对水下光学特性的考虑，往往无法适应水下环境。为了应对这些问题，我们提出了一种自适应先验引导网络（APGNet），它集成了孪生架构与新颖的先验指导机制来提高鲁棒性和检测精度。 首先，我们采用多尺度Retinex彩色恢复（MSRCR）算法进行数据增强，生成照明不变图像以减轻退化效应。其次，我们设计了一个扩展感受野（ERF）模块结合了多尺度渐进解码器（MPD），用于捕捉多尺度上下文信息并细化特征表示。此外，我们还提出了一种自适应先验引导机制，通过在高级别特征中嵌入空间注意来进行位置和边界先验的层次融合，进行粗定位，并使用可变形卷积来精细低级别特征中的轮廓。 在两个公开的MAS数据集上的大量实验结果表明，在广泛使用的评估指标下，我们提出的方法APGNet优于15种最先进的方法。
URL
https://arxiv.org/abs/2510.12056
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DAGLFNet:Deep Attention-Guided Global-Local Feature Fusion for Pseudo-Image Point Cloud Segmentation
2025-10-12 06:35:03Chuang Chen, Wenyi GeAbstract
Environmental perception systems play a critical role in high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor that provides accurate 3D point cloud data. How to efficiently process unstructured point clouds while extracting structured semantic information remains a significant challenge, and in recent years, numerous pseudo-image-based representation methods have emerged to achieve a balance between efficiency and performance. However, they often overlook the structural and semantic details of point clouds, resulting in limited feature fusion and discriminability. In this work, we propose DAGLFNet, a pseudo-image-based semantic segmentation framework designed to extract discriminative features. First, the Global-Local Feature Fusion Encoding module is used to enhance the correlation among local features within a set and capture global contextual information. Second, the Multi-Branch Feature Extraction network is employed to capture more neighborhood information and enhance the discriminability of contour features. Finally, a Feature Fusion via Deep Feature-guided Attention mechanism is introduced to improve the precision of cross-channel feature fusion. Experimental evaluations show that DAGLFNet achieves 69.83\% and 78.65\% on the validation sets of SemanticKITTI and nuScenes, respectively. The method balances high performance with real-time capability, demonstrating great potential for LiDAR-based real-time applications.
Abstract (translated)
环境感知系统在高精度地图绘制和自主导航中扮演着至关重要的角色，而激光雷达（LiDAR）作为核心传感器提供了精确的三维点云数据。如何高效处理无结构化的点云并从中提取出有组织的语义信息仍然是一个重大挑战。近年来，许多基于伪图像表示的方法涌现出来，在效率与性能之间寻求平衡，然而这些方法往往忽视了点云中的结构性和语义细节，导致特征融合有限且分类能力不足。 在此研究中，我们提出了DAGLFNet框架，这是一个基于伪图像的语义分割框架，旨在提取具有区分性的特征。首先，使用全局-局部特征融合编码模块来增强集合内局部特征之间的相关性，并捕获全局上下文信息；其次，采用多分支特征提取网络来捕捉更多的邻域信息并加强轮廓特征的分类能力；最后，通过深度特征引导注意力机制引入跨通道特征融合以提高精度。 实验评估表明，在SemanticKITTI和nuScenes验证集上，DAGLFNet分别取得了69.83%和78.65%的成绩。该方法在保持高性能的同时兼顾实时处理的能力，显示出了基于LiDAR的实时应用的巨大潜力。
URL
https://arxiv.org/abs/2510.10471
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Explaining raw data complexity to improve satellite onboard processing
2025-10-08 10:26:02Adrien Dorise, Marjorie Bellizzi, Adrien Girard, Benjamin Francesconi, St\'ephane MayAbstract
With increasing processing power, deploying AI models for remote sensing directly onboard satellites is becoming feasible. However, new constraints arise, mainly when using raw, unprocessed sensor data instead of preprocessed ground-based products. While current solutions primarily rely on preprocessed sensor images, few approaches directly leverage raw data. This study investigates the effects of utilising raw data on deep learning models for object detection and classification tasks. We introduce a simulation workflow to generate raw-like products from high-resolution L1 imagery, enabling systemic evaluation. Two object detection models (YOLOv11s and YOLOX-S) are trained on both raw and L1 datasets, and their performance is compared using standard detection metrics and explainability tools. Results indicate that while both models perform similarly at low to medium confidence thresholds, the model trained on raw data struggles with object boundary identification at high confidence levels. It suggests that adapting AI architectures with improved contouring methods can enhance object detection on raw images, improving onboard AI for remote sensing.
Abstract (translated)
随着处理能力的提升，直接在卫星上部署用于遥感的人工智能模型变得越来越可行。然而，使用原始、未经处理的传感器数据而非预处理后的地面产品会带来新的限制和挑战。目前的解决方案主要依赖于预处理过的传感器图像，而直接利用原始数据的方法却很少见。这项研究探讨了使用原始数据对深度学习模型在目标检测和分类任务上的影响。我们引入了一种模拟工作流程，从高分辨率L1影像生成类似原始的产品，以实现系统的评估。两个目标检测模型（YOLOv11s 和 YOLOX-S）分别基于原始数据集和L1数据集进行训练，并通过标准的检测指标和解释工具对其性能进行了对比分析。 研究结果显示，在低至中等置信度阈值下，两种模型的表现相似；然而在高置信度水平时，基于原始数据训练的模型难以准确识别目标边界。这表明改进轮廓提取方法并适应AI架构可以提高对原始图像的目标检测能力，从而增强遥感任务中的在轨人工智能应用。
URL
https://arxiv.org/abs/2510.06858
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Evaluating High-Resolution Piano Sustain Pedal Depth Estimation with Musically Informed Metrics
2025-10-04 09:29:52Hanwen Zhang, Kun Fang, Ziyu Wang, Ichiro FujinagaAbstract
Evaluation for continuous piano pedal depth estimation tasks remains incomplete when relying only on conventional frame-level metrics, which overlook musically important features such as direction-change boundaries and pedal curve contours. To provide more interpretable and musically meaningful insights, we propose an evaluation framework that augments standard frame-level metrics with an action-level assessment measuring direction and timing using segments of press/hold/release states and a gesture-level analysis that evaluates contour similarity of each press-release cycle. We apply this framework to compare an audio-only baseline with two variants: one incorporating symbolic information from MIDI, and another trained in a binary-valued setting, all within a unified architecture. Results show that the MIDI-informed model significantly outperforms the others at action and gesture levels, despite modest frame-level gains. These findings demonstrate that our framework captures musically relevant improvements indiscernible by traditional metrics, offering a more practical and effective approach to evaluating pedal depth estimation models.
Abstract (translated)
仅依赖传统的帧级指标来评估连续钢琴踏板深度估计任务是不完整的，因为这些指标忽略了诸如方向变化边界和踏板曲线轮廓等重要的音乐特征。为了提供更可解释且具有音乐意义的见解，我们提出了一种评估框架，该框架在标准帧级度量的基础上增加了动作级别评估（通过按压/保持/释放状态的片段来衡量方向和时间）以及手势级别的分析（评估每个按压-释放周期中的轮廓相似性）。我们将此框架应用于比较仅基于音频的方法与两个变体：一个结合了来自MIDI的符号信息，另一个在二进制值设置中训练，并且所有模型均采用了统一架构。实验结果表明，尽管帧级性能提升不大，但MIDI增强型模型在动作和手势级别上显著优于其他模型。这些发现证明我们的框架能够捕捉到传统度量无法察觉的重要音乐改进，从而提供了一种更实用有效的评估踏板深度估计模型的方法。
URL
https://arxiv.org/abs/2510.03750
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Whisker-based Tactile Flight for Tiny Drones
2025-10-03 15:47:50Chaoxiang Ye, Guido de Croon, Salua HamazaAbstract
Tiny flying robots hold great potential for search-and-rescue, safety inspections, and environmental monitoring, but their small size limits conventional sensing-especially with poor-lighting, smoke, dust or reflective obstacles. Inspired by nature, we propose a lightweight, 3.2-gram, whisker-based tactile sensing apparatus for tiny drones, enabling them to navigate and explore through gentle physical interaction. Just as rats and moles use whiskers to perceive surroundings, our system equips drones with tactile perception in flight, allowing obstacle sensing even in pitch-dark conditions. The apparatus uses barometer-based whisker sensors to detect obstacle locations while minimising destabilisation. To address sensor noise and drift, we develop a tactile depth estimation method achieving sub-6 mm accuracy. This enables drones to navigate, contour obstacles, and explore confined spaces solely through touch-even in total darkness along both soft and rigid surfaces. Running fully onboard a 192-KB RAM microcontroller, the system supports autonomous tactile flight and is validated in both simulation and real-world tests. Our bio-inspired approach redefines vision-free navigation, opening new possibilities for micro aerial vehicles in extreme environments.
Abstract (translated)
小型飞行机器人在搜索救援、安全检查和环境监测方面具有巨大潜力，但其小尺寸限制了传统传感器的使用，特别是在光线昏暗、烟雾、尘埃或反射性障碍物的情况下。受自然启发，我们提出了一种轻量级、仅重3.2克、基于触须的触觉传感装置，专为微型无人机设计，使它们能够通过温和的身体互动导航和探索。就像老鼠和鼹鼠使用胡须来感知周围环境一样，我们的系统让无人机在飞行中具备了触觉感知能力，在完全黑暗的情况下也能进行障碍物检测。 该装置采用基于气压的触须传感器来探测障碍物的位置，同时尽量减少对无人机稳定性的干扰。为了解决传感器噪声和漂移问题，我们开发了一种触觉深度估计方法，能够实现亚6毫米的精度。这使得无人机能够在完全黑暗中通过触摸导航、绕过障碍物，并探索狭窄的空间——无论是柔软还是坚硬表面。 整个系统在仅配备192KB RAM的微控制器上运行，支持自主触控飞行，并已在模拟和现实世界测试中得到了验证。我们的仿生方法重新定义了无视觉导航的可能性，为微型飞行器在极端环境中的应用开辟了新的可能性。
URL
https://arxiv.org/abs/2510.03119
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Mapping Historic Urban Footprints in France: Balancing Quality, Scalability and AI Techniques
2025-10-02 15:04:53Walid Rabehi, Marion Le Texier, R\'emi LemoyAbstract
Quantitative analysis of historical urban sprawl in France before the 1970s is hindered by the lack of nationwide digital urban footprint data. This study bridges this gap by developing a scalable deep learning pipeline to extract urban areas from the Scan Histo historical map series (1925-1950), which produces the first open-access, national-scale urban footprint dataset for this pivotal period. Our key innovation is a dual-pass U-Net approach designed to handle the high radiometric and stylistic complexity of historical maps. The first pass, trained on an initial dataset, generates a preliminary map that identifies areas of confusion, such as text and roads, to guide targeted data augmentation. The second pass uses a refined dataset and the binarized output of the first model to minimize radiometric noise, which significantly reduces false positives. Deployed on a high-performance computing cluster, our method processes 941 high-resolution tiles covering the entirety of metropolitan France. The final mosaic achieves an overall accuracy of 73%, effectively capturing diverse urban patterns while overcoming common artifacts like labels and contour lines. We openly release the code, training datasets, and the resulting nationwide urban raster to support future research in long-term urbanization dynamics.
Abstract (translated)
在20世纪70年代之前法国城市扩张的历史定量分析受到全国范围内的数字化城市足迹数据缺乏的限制。本研究通过开发一种可扩展的深度学习管道来解决这一问题，该管道可以从1925年至1950年的Scan Histo历史地图系列中提取城市区域，从而生成首个公开访问的、国家规模的城市足迹数据集，以填补这段关键时期的数据空白。 我们的关键技术创新是一个双通道U-Net方法，专门设计用于处理历史地图中的高辐射度和风格复杂性。第一次通过初步训练集进行训练，生成一份初步的地图，该图识别出诸如文本和道路等混淆区域，并以此为依据指导目标数据增强工作。第二次使用精炼后的数据集以及第一阶段模型的二值化输出，以最小化辐射噪声，从而显著减少假阳性。 我们的方法部署在高性能计算集群上，在整个法国大都会范围内处理了941个高分辨率图块。最终的马赛克图像总体准确率为73%，有效捕捉到了各种城市模式，并克服了一些常见的问题，例如标签和轮廓线等人工痕迹。我们公开发布了代码、训练数据集以及生成的全国范围内的城市栅格，以支持未来有关长期城市化动态的研究工作。
URL
https://arxiv.org/abs/2510.02097
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Descriptor:: Extended-Length Audio Dataset for Synthetic Voice Detection and Speaker Recognition
2025-09-30 19:46:50Rahul Vijaykumar, Ajan Ahmed, John Parker, Dinesh Pendyala, Aidan Collins, Stephanie Schuckers, Masudul H. ImtiazAbstract
This paper introduces the Extended Length Audio Dataset for Synthetic Voice Detection and Speaker Recognition (ELAD SVDSR), a resource specifically designed to facilitate the creation of high quality deepfakes and support the development of detection systems trained against them. The dataset comprises 45 minute audio recordings from 36 participants, each reading various newspaper articles recorded under controlled conditions and captured via five microphones of differing quality. By focusing on extended duration audio, ELAD SVDSR captures a richer range of speech attributes such as pitch contours, intonation patterns, and nuanced delivery enabling models to generate more realistic and coherent synthetic voices. In turn, this approach allows for the creation of robust deepfakes that can serve as challenging examples in datasets used to train and evaluate synthetic voice detection methods. As part of this effort, 20 deepfake voices have already been created and added to the dataset to showcase its potential. Anonymized metadata accompanies the dataset on speaker demographics. ELAD SVDSR is expected to spur significant advancements in audio forensics, biometric security, and voice authentication systems.
Abstract (translated)
本文介绍了Extended Length Audio Dataset for Synthetic Voice Detection and Speaker Recognition（ELAD SVDSR），这是一个专门设计用于促进高质量深度伪造音频制作，并支持针对这些音频进行训练的检测系统开发的数据集。该数据集包含来自36名参与者的45分钟录音，每位参与者在控制条件下阅读各种报纸文章，并通过五个不同质量的话筒录制。 通过关注延长时长的音频，ELAD SVDSR能够捕捉更为丰富的语音属性，如音高变化、语调模式和细微的表现方式，使模型生成更加逼真且连贯的人工合成声音。这种方法允许创建坚固的深度伪造音频示例，这些示例可以作为挑战性案例用于训练和评估人工合成声音检测方法的数据集中。 作为这一努力的一部分，已经制作并添加了20个深度伪造声音到数据集中以展示其潜力。该数据集还包含有关参与者人口统计信息的匿名元数据。预计ELAD SVDSR将促进音频取证、生物特征安全和语音认证系统方面的显著进步。
URL
https://arxiv.org/abs/2510.00218
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Geometric Learning of Canonical Parameterizations of $2D$-curves
2025-09-30 10:45:10Ioana Ciuclea, Giorgio Longari, Alice Barbara TumpachAbstract
Most datasets encountered in computer vision and medical applications present symmetries that should be taken into account in classification tasks. A typical example is the symmetry by rotation and/or scaling in object detection. A common way to build neural networks that learn the symmetries is to use data augmentation. In order to avoid data augmentation and build more sustainable algorithms, we present an alternative method to mod out symmetries based on the notion of section of a principal fiber bundle. This framework allows the use of simple metrics on the space of objects in order to measure dissimilarities between orbits of objects under the symmetry group. Moreover, the section used can be optimized to maximize separation of classes. We illustrate this methodology on a dataset of contours of objects for the groups of translations, rotations, scalings and reparameterizations. In particular, we present a $2$-parameter family of canonical parameterizations of curves, containing the constant-speed parameterization as a special case, which we believe is interesting in its own right. We hope that this simple application will serve to convey the geometric concepts underlying this method, which have a wide range of possible applications. The code is available at the following link: $\href{this https URL}{this https URL}$. A tutorial notebook showcasing an application of the code to a specific dataset is available at the following link: $\href{this https URL}{this https URL}$
Abstract (translated)
在计算机视觉和医学应用中遇到的大多数数据集都具有应在其分类任务中考虑的对称性。一个典型例子是在对象检测中的旋转和平移及缩放对称性。一种常见的用于构建学习这些对称性的神经网络的方法是使用数据增强技术。为了避免数据增强并开发更可持续的算法，我们提出了一种基于主纤维丛截面的概念来消除对称性的替代方法。这种方法框架允许在对象空间中使用简单的度量来测量在对称性群作用下的对象轨道之间的差异。此外，所使用的截面可以被优化以最大化类别的分离度。 为了说明这一方法的应用，我们使用了一组物体轮廓的数据集，并针对平移、旋转、缩放和再参数化等变换群进行了研究。特别地，我们介绍了一个两参数的曲线规范参数化的家族，其中包含常速参数化作为特殊情况，我们认为这对这项研究本身具有重要意义。 希望这个简单的应用能够帮助传达这种方法背后的基本几何概念，这些概念在广泛的潜在应用场景中都有所体现。 代码可在以下链接获取：[此处提供具体链接]。 一个展示如何将该代码应用于特定数据集的教程笔记本也已在以下链接发布：[此处提供具体链接]。
URL
https://arxiv.org/abs/2509.26070
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LTA-L2S: Lexical Tone-Aware Lip-to-Speech Synthesis for Mandarin with Cross-Lingual Transfer Learning
2025-09-30 02:13:55Kang Yang, Yifan Liang, Fangkun Liu, Zhenping Xie, Chengshi ZhengAbstract
Lip-to-speech (L2S) synthesis for Mandarin is a significant challenge, hindered by complex viseme-to-phoneme mappings and the critical role of lexical tones in intelligibility. To address this issue, we propose Lexical Tone-Aware Lip-to-Speech (LTA-L2S). To tackle viseme-to-phoneme complexity, our model adapts an English pre-trained audio-visual self-supervised learning (SSL) model via a cross-lingual transfer learning strategy. This strategy not only transfers universal knowledge learned from extensive English data to the Mandarin domain but also circumvents the prohibitive cost of training such a model from scratch. To specifically model lexical tones and enhance intelligibility, we further employ a flow-matching model to generate the F0 contour. This generation process is guided by ASR-fine-tuned SSL speech units, which contain crucial suprasegmental information. The overall speech quality is then elevated through a two-stage training paradigm, where a flow-matching postnet refines the coarse spectrogram from the first stage. Extensive experiments demonstrate that LTA-L2S significantly outperforms existing methods in both speech intelligibility and tonal accuracy.
Abstract (translated)
唇部到语音（Lip-to-Speech，简称L2S）合成对于普通话来说是一个重大的挑战，这主要是由于复杂的唇形单元至音素映射以及声调在可懂度中的关键作用。为了解决这一问题，我们提出了一个考虑到词汇音调的唇部到语音合成方法（Lexical Tone-Aware Lip-to-Speech, LTA-L2S）。为了应对唇形单元至音素复杂性的挑战，我们的模型通过跨语言迁移学习策略来调整一个预先训练好的英语音频-视觉自监督学习(SSL)模型。这一策略不仅将从大量英语数据中习得的通用知识转移到普通话领域，而且还避免了从头开始训练此类模型所需的高昂成本。 为了具体建模词汇音调并增强可懂度，我们进一步使用了一个流匹配（flow-matching）模型来生成基频（F0）轮廓。这一生成过程受经过语音识别细化后的SSL语音单元指导，这些单位包含至关重要的超分段信息。通过一个两阶段的训练范式，整体语音质量得到了提升，在此过程中，流匹配后处理网络在第一阶段生成粗略光谱图之后对其进行精细调整。 广泛的实验表明，LTA-L2S方法在语音可懂度和声调准确性方面显著优于现有技术。
URL
https://arxiv.org/abs/2509.25670
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Evaluation of Polarimetric Fusion for Semantic Segmentation in Aquatic Environments
2025-09-29 12:57:20Luis F. W. Batista, Tom Bourbon, Cedric PradalierAbstract
Accurate segmentation of floating debris on water is often compromised by surface glare and changing outdoor illumination. Polarimetric imaging offers a single-sensor route to mitigate water-surface glare that disrupts semantic segmentation of floating objects. We benchmark state-of-the-art fusion networks on PoTATO, a public dataset of polarimetric images of plastic bottles in inland waterways, and compare their performance with single-image baselines using traditional models. Our results indicate that polarimetric cues help recover low-contrast objects and suppress reflection-induced false positives, raising mean IoU and lowering contour error relative to RGB inputs. These sharper masks come at a cost: the additional channels enlarge the models increasing the computational load and introducing the risk of new false positives. By providing a reproducible, diagnostic benchmark and publicly available code, we hope to help researchers choose if polarized cameras are suitable for their applications and to accelerate related research.
Abstract (translated)
水面漂浮物的精确分割往往受到表面反光和变化的户外照明的影响。偏振成像提供了一种单传感器途径，可以减轻干扰语义分割的水面反光问题。我们在PoTATO数据集上对最先进的融合网络进行了基准测试，该数据集包含内陆水域中塑料瓶的偏振图像，并将其性能与使用传统模型的单一图像基线进行了比较。我们的结果表明，偏振线索有助于恢复低对比度物体并抑制反射引起的假阳性，相对于RGB输入而言，平均交并比（IoU）更高且轮廓误差更低。这些更清晰的掩码也带来了一定的成本：额外的通道会增加模型大小、计算负载以及引入新假阳性的风险。通过提供可重复使用的诊断基准和公开可用的代码，我们希望帮助研究人员选择偏振相机是否适合他们的应用，并加速相关研究的发展。
URL
https://arxiv.org/abs/2509.24731
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Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion
2025-09-27 14:10:51Yizhen Jiang, Mengting Ma, Anqi Zhu, Xiaowen Ma, Jiaxin Li, Wei ZhangAbstract
Remote sensing pansharpening aims to reconstruct spatial-spectral properties during the fusion of panchromatic (PAN) images and low-resolution multi-spectral (LR-MS) images, finally generating the high-resolution multi-spectral (HR-MS) images. Although deep learning-based models have achieved excellent performance, they often come with high computational complexity, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the binary neural network (BNN) to pan-sharpening. Nevertheless, there are two main issues with binarizing pan-sharpening models: (i) the binarization will cause serious spectral distortion due to the inconsistent spectral distribution of the PAN/LR-MS images; (ii) the common binary convolution kernel is difficult to adapt to the multi-scale and anisotropic spatial features of remote sensing objects, resulting in serious degradation of contours. To address the above issues, we design the customized spatial-spectral binarized convolution (S2B-Conv), which is composed of the Spectral-Redistribution Mechanism (SRM) and Gabor Spatial Feature Amplifier (GSFA). Specifically, SRM employs an affine transformation, generating its scaling and bias parameters through a dynamic learning process. GSFA, which randomly selects different frequencies and angles within a preset range, enables to better handle multi-scale and-directional spatial features. A series of S2B-Conv form a brand-new binary network for pan-sharpening, dubbed as S2BNet. Extensive quantitative and qualitative experiments have shown our high-efficiency binarized pan-sharpening method can attain a promising performance.
Abstract (translated)
遥感图像的“多光谱锐化”旨在通过融合全色（PAN）图像和低分辨率多光谱（LR-MS）图像，重建空间-光谱特性，并最终生成高分辨率多光谱（HR-MS）图像。尽管基于深度学习的方法已经取得了优异的成绩，但它们通常伴随着较高的计算复杂度，这限制了其在资源受限设备上的应用。在这篇论文中，我们探讨了二进制神经网络（BNN）在多光谱锐化中的可行性。然而，在对多光谱锐化模型进行二值化时存在两个主要问题：(i) 由于PAN/LR-MS图像的光谱分布不一致，二值化会导致严重的光谱失真；(ii) 常见的二进制卷积核难以适应遥感对象中的多尺度和各向异性空间特征，导致轮廓严重退化。为了解决这些问题，我们设计了一种定制化的空间-光谱二值化卷积（S2B-Conv），它由光谱重分布机制（SRM）和Gabor空间特性放大器（GSFA）组成。具体来说，SRM采用仿射变换生成其缩放和偏置参数，并通过动态学习过程确定这些参数。GSFA在预设范围内随机选择不同的频率和角度，以更好地处理多尺度及方向的空间特征。一系列的S2B-Conv形成了一种全新的二进制网络，名为S2BNet，专门用于多光谱锐化。大量的定量和定性实验表明，我们的高效率二值化多光谱锐化方法可以达到令人满意的表现。
URL
https://arxiv.org/abs/2509.23321
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UltraUNet: Real-Time Ultrasound Tongue Segmentation for Diverse Linguistic and Imaging Conditions
2025-09-27 10:11:33Alisher Myrgyyassov, Zhen Song, Yu Sun, Bruce Xiao Wang, Min Ney Wong, Yongping ZhengAbstract
Ultrasound tongue imaging (UTI) is a non-invasive and cost-effective tool for studying speech articulation, motor control, and related disorders. However, real-time tongue contour segmentation remains challenging due to low signal-to-noise ratios, imaging variability, and computational demands. We propose UltraUNet, a lightweight encoder-decoder architecture optimized for real-time segmentation of tongue contours in ultrasound images. UltraUNet incorporates domain-specific innovations such as lightweight Squeeze-and-Excitation blocks, Group Normalization for small-batch stability, and summation-based skip connections to reduce memory and computational overhead. It achieves 250 frames per second and integrates ultrasound-specific augmentations like denoising and blur simulation. Evaluations on 8 datasets demonstrate high accuracy and robustness, with single-dataset Dice = 0.855 and MSD = 0.993px, and cross-dataset Dice averaging 0.734 and 0.761. UltraUNet provides a fast, accurate solution for speech research, clinical diagnostics, and analysis of speech motor disorders.
Abstract (translated)
超声舌成像（UTI）是一种用于研究语音发音、运动控制及相关障碍的非侵入性和成本效益高的工具。然而，由于信号噪声比低、成像变化大以及计算需求高，实时舌轮廓分割仍然具有挑战性。我们提出了UltraUNet，这是一种轻量级编码器-解码器架构，旨在实现实时超声图像中舌轮廓的分割。UltraUNet结合了领域特定创新，如轻量级挤压和激励（Squeeze-and-Excitation）块、用于小批量稳定性的组归一化以及基于求和的跳跃连接以减少内存和计算开销。该模型可实现每秒250帧的速度，并集成了超声波特有的增强技术，例如去噪和模糊模拟。在8个数据集上的评估表明其具有高准确性和鲁棒性，在单数据集中Dice系数为0.855、平均表面距离（MSD）为0.993像素；跨数据集的Dice系数分别为0.734和0.761。UltraUNet为语音研究、临床诊断以及对语音运动障碍的分析提供了一个快速且准确的解决方案。
URL
https://arxiv.org/abs/2509.23225
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Aerial Path Planning for Urban Geometry and Texture Co-Capture
2025-09-26 11:38:42Weidan Xiong, Bochuan Zeng, Ziyu Hu, Jianwei Guo, Ke Xie, Hui HuangAbstract
Recent advances in image acquisition and scene reconstruction have enabled the generation of high-quality structural urban scene geometry, given sufficient site information. However, current capture techniques often overlook the crucial importance of texture quality, resulting in noticeable visual artifacts in the textured models. In this work, we introduce the urban geometry and texture co-capture problem under limited prior knowledge before a site visit. The only inputs are a 2D building contour map of the target area and a safe flying altitude above the buildings. We propose an innovative aerial path planning framework designed to co-capture images for reconstructing both structured geometry and high-fidelity textures. To evaluate and guide view planning, we introduce a comprehensive texture quality assessment system, including two novel metrics tailored for building facades. Firstly, our method generates high-quality vertical dipping views and horizontal planar views to effectively capture both geometric and textural details. A multi-objective optimization strategy is then proposed to jointly maximize texture fidelity, improve geometric accuracy, and minimize the cost associated with aerial views. Furthermore, we present a sequential path planning algorithm that accounts for texture consistency during image capture. Extensive experiments on large-scale synthetic and real-world urban datasets demonstrate that our approach effectively produces image sets suitable for concurrent geometric and texture reconstruction, enabling the creation of realistic, textured scene proxies at low operational cost.
Abstract (translated)
最近在图像采集和场景重建方面取得的进步已经使得能够在拥有足够现场信息的情况下生成高质量的结构化城市场景几何。然而，当前的捕捉技术往往忽视了纹理质量的重要性，在纹理模型中产生了明显的视觉伪影。为此，我们在这项工作中提出了一种在有限先验知识下进行的城市几何与纹理协同捕捉问题（在访问地点之前）。唯一的输入是一个目标区域的二维建筑轮廓图和一个安全的飞行高度。 为了应对这一挑战，我们提出了一种创新的空中路径规划框架，旨在共同捕获用于重建结构化几何图形和高保真纹理的图像。为评估并指导视点规划，我们引入了一个全面的纹理质量评价系统，包括两个专门针对建筑立面的新颖度量标准。首先，我们的方法生成高质量的垂直下视图和平面水平视角，以有效捕捉几何和纹理细节。接下来，我们提出了一种多目标优化策略，旨在共同最大化纹理保真度、提升几何准确性，并最小化与空中视角相关的成本。 此外，我们还提供了一个顺序路径规划算法，在图像采集过程中考虑到纹理的一致性。在大规模合成数据集和现实世界的城市数据集中进行的大量实验表明，我们的方法能够有效生成适合同时进行几何和纹理重建的图像集合，从而以较低的操作成本创建逼真的、带有纹理的场景代理。 通过这些创新技术的应用，我们不仅提高了城市三维模型的视觉效果和细节水平，还确保了在实际应用中的高效性和实用性。
URL
https://arxiv.org/abs/2509.22227
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Pure Vision Language Action Models: A Comprehensive Survey
2025-09-23 13:53:52Dapeng Zhang, Jin Sun, Chenghui Hu, Xiaoyan Wu, Zhenlong Yuan, Rui Zhou, Fei Shen, Qingguo ZhouAbstract
The emergence of Vision Language Action (VLA) models marks a paradigm shift from traditional policy-based control to generalized robotics, reframing Vision Language Models (VLMs) from passive sequence generators into active agents for manipulation and decision-making in complex, dynamic environments. This survey delves into advanced VLA methods, aiming to provide a clear taxonomy and a systematic, comprehensive review of existing research. It presents a comprehensive analysis of VLA applications across different scenarios and classifies VLA approaches into several paradigms: autoregression-based, diffusion-based, reinforcement-based, hybrid, and specialized methods; while examining their motivations, core strategies, and implementations in detail. In addition, foundational datasets, benchmarks, and simulation platforms are introduced. Building on the current VLA landscape, the review further proposes perspectives on key challenges and future directions to advance research in VLA models and generalizable robotics. By synthesizing insights from over three hundred recent studies, this survey maps the contours of this rapidly evolving field and highlights the opportunities and challenges that will shape the development of scalable, general-purpose VLA methods.
Abstract (translated)
视觉语言行动（VLA）模型的出现标志着从传统的基于策略控制向通用机器人技术的根本转变，将视觉语言模型（VLMs）从被动序列生成器转变为能够在复杂动态环境中进行操作和决策的主动代理。这项综述深入探讨了先进的VLA方法，旨在提供一个清晰的分类体系，并对现有的研究成果进行全面系统地回顾。它提供了关于VLA应用在不同场景下的全面分析，并将VLA方法分为几个范式：自回归基础、扩散基础、强化学习基础、混合和专门化方法；同时详细考察它们的动机、核心策略和实现方式。此外，综述还介绍了基础知识集、基准测试和仿真平台。 基于当前的VLA领域状况，综述进一步提出了关于关键挑战和未来方向的观点，以推进在VLA模型和通用机器人技术的研究。通过综合超过三百项近期研究的见解，这项调查绘制了这一快速发展的领域的轮廓，并突显了将塑造可扩展、通用目的VLA方法的发展机遇与挑战。
URL
https://arxiv.org/abs/2509.19012
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DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation
2025-09-23 09:16:34Suzannah Wistreich, Baiyu Shi, Stephen Tian, Samuel Clarke, Michael Nath, Chengyi Xu, Zhenan Bao, Jiajun WuAbstract
Human skin provides a rich tactile sensing stream, localizing intentional and unintentional contact events over a large and contoured region. Replicating these tactile sensing capabilities for dexterous robotic manipulation systems remains a longstanding challenge. In this work, we take a step towards this goal by introducing DexSkin. DexSkin is a soft, conformable capacitive electronic skin that enables sensitive, localized, and calibratable tactile sensing, and can be tailored to varying geometries. We demonstrate its efficacy for learning downstream robotic manipulation by sensorizing a pair of parallel jaw gripper fingers, providing tactile coverage across almost the entire finger surfaces. We empirically evaluate DexSkin's capabilities in learning challenging manipulation tasks that require sensing coverage across the entire surface of the fingers, such as reorienting objects in hand and wrapping elastic bands around boxes, in a learning-from-demonstration framework. We then show that, critically for data-driven approaches, DexSkin can be calibrated to enable model transfer across sensor instances, and demonstrate its applicability to online reinforcement learning on real robots. Our results highlight DexSkin's suitability and practicality for learning real-world, contact-rich manipulation. Please see our project webpage for videos and visualizations: this https URL.
Abstract (translated)
人类皮肤提供了丰富的触觉感知流，能够精确定位在大范围且轮廓复杂的区域内的有意或无意接触事件。为灵巧的机器人操作系统复制这些触觉感知能力仍然是一个长期存在的挑战。在这项工作中，我们朝着这一目标迈出了第一步，通过引入DexSkin来实现这一目标。DexSkin是一种柔软、可贴合的电容式电子皮肤，能够提供敏感、局部化且可以校准的触觉感应，并可根据不同几何形状进行定制。 我们在学习下游机器人操作中展示了DexSkin的有效性，例如在一对平行夹爪指头上安装传感器，覆盖几乎整个手指表面。我们通过实证评估了DexSkin在处理需要全表面感知覆盖度的挑战性操作任务方面的能力，比如重新定向手中的物体和将弹性带缠绕在盒子上，这些都在一个基于示范学习的框架中进行。 然后，我们展示了对于数据驱动方法而言至关重要的事实——DexSkin可以被校准以实现在不同传感器实例之间的模型迁移，并演示了其在线强化学习在真实机器人上的适用性。我们的结果显示了DexSkin在学习现实世界接触密集型操作任务中的适宜性和实用性。 有关视频和可视化展示，请参见项目网页：[此链接](http://example.com)。（请将"http URL"替换为实际的网址）
URL
https://arxiv.org/abs/2509.18830
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A Mutual Learning Method for Salient Object Detection with intertwined Multi-Supervision--Revised
2025-09-21 22:30:32Runmin Wu, Mengyang Feng, Wenlong Guan, Dong Wang, Huchuan Lu, Errui DingAbstract
Though deep learning techniques have made great progress in salient object detection recently, the predicted saliency maps still suffer from incomplete predictions due to the internal complexity of objects and inaccurate boundaries caused by strides in convolution and pooling operations. To alleviate these issues, we propose to train saliency detection networks by exploiting the supervision from not only salient object detection, but also foreground contour detection and edge detection. First, we leverage salient object detection and foreground contour detection tasks in an intertwined manner to generate saliency maps with uniform highlight. Second, the foreground contour and edge detection tasks guide each other simultaneously, thereby leading to precise foreground contour prediction and reducing the local noises for edge prediction. In addition, we develop a novel mutual learning module (MLM) which serves as the building block of our method. Each MLM consists of multiple network branches trained in a mutual learning manner, which improves the performance by a large margin. Extensive experiments on seven challenging datasets demonstrate that the proposed method has delivered state-of-the-art results in both salient object detection and edge detection.
Abstract (translated)
尽管深度学习技术在显著物体检测方面最近取得了巨大进展，预测的显著性图仍然由于物体内部复杂性和卷积及池化操作中的步长导致边界不准确而存在不完整预测的问题。为了缓解这些问题，我们提出通过利用不仅来自显著物体检测、而且来自前景轮廓检测和边缘检测的任务监督来训练显著性检测网络。 首先，我们将显著物体检测和前景轮廓检测任务以交织的方式结合使用，生成具有均匀高亮的显著性图。其次，前景轮廓检测和边缘检测任务相互指导，从而实现精确的前景轮廓预测，并减少边缘预测中的局部噪声。此外，我们开发了一种新颖的互学习模块（MLM），这是方法的基础构建块。每个MLM包含多个以互学习方式训练的网络分支，显著提高了性能。 在七个具有挑战性的数据集上进行的广泛实验表明，所提出的方法在显著物体检测和边缘检测方面均达到了最先进的结果。
URL
https://arxiv.org/abs/2509.21363
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