arXiv:2511.10894v2 Announce Type: replace 
Abstract: This paper proposes a competitive and computationally efficient approach to probabilistic rainfall nowcasting. A video projector (V-JEPA Vision Transformer) associated to a lightweight probabilistic head is attached to a pre-trained satellite vision encoder (DINOv3-SAT493M) to map encoder tokens into a discrete empirical CDF (eCDF) over 4-hour accumulated rainfall. The projector-head is optimized end-to-end over the Ranked Probability Score (RPS). As an alternative, 3D-UNET baselines trained with an aggregate Rank Probability Score and a per-pixel Gamma-Hurdle objective are used. On the Weather4Cast 2025 benchmark, the proposed method achieved a promising performance, with a CRPS of 3.5102, which represents $\approx$ 26% in effectiveness gain against the best 3D-UNET.

يقدم هذا المقال نهجًا مبتكرًا للتنبؤ الاحتمالي بالأمطار باستخدام جهاز عرض فيديو (V-JEPA Vision Transformer) ورأس احتمالي خفيف مرتبط بمشفر رؤية الأقمار الصناعية المدرب مسبقًا (DINOv3-SAT493M). تقوم الطريقة بتحويل رموز المشفر إلى دالة توزيع تراكمي تجريبي منفصل على الأمطار المتراكمة على مدى 4 ساعات، ويتم تحسينها باستخدام درجة الاحتمالية المرتبة. أظهرت الطريقة المقترحة نتائج واعدة في معيار Weather4Cast 2025، حيث حققت CRPS قدره 3.5102، مما يدل على تحسين كبير مقارنة بالنماذج الحالية.

Este artículo presenta un enfoque novedoso para la predicción probabilística de lluvias utilizando un proyector de video (V-JEPA Vision Transformer) y una cabeza probabilística ligera conectada a un codificador de visión satelital preentrenado (DINOv3-SAT493M). El método mapea los tokens del codificador en una función de distribución acumulativa empírica discreta sobre la lluvia acumulada durante 4 horas y se optimiza utilizando el Puntaje de Probabilidad Clasificada. El método propuesto mostró resultados prometedores en el benchmark Weather4Cast 2025, alcanzando un CRPS de 3.5102, lo que indi…

Cet article propose une nouvelle approche pour le prévision probabiliste des pluies, utilisant un projecteur vidéo (V-JEPA Vision Transformer) associé à une tête probabiliste légère, connectée à un encodeur de vision satellite pré-entraîné (DINOv3-SAT493M). La méthode cartographie les tokens de l'encodeur en une fonction de distribution cumulative empirique discrète sur les pluies accumulées sur 4 heures et est optimisée à l'aide du Score de Probabilité Rangée. La méthode a montré des résultats prometteurs sur le benchmark Weather4Cast 2025, atteignant un CRPS de 3.5102, ce qui représente une …

This paper presents a novel approach to probabilistic rainfall nowcasting using a combination of a video projector (V-JEPA Vision Transformer) and a lightweight probabilistic head attached to a pre-trained satellite vision encoder (DINOv3-SAT493M). The method maps encoder tokens into a discrete empirical CDF over 4-hour accumulated rainfall and is optimized using the Ranked Probability Score. The proposed method demonstrated promising results on the Weather4Cast 2025 benchmark, achieving a CRPS of 3.5102, indicating a significant improvement over existing models.

DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting

arXiv:2601.08078v1 Announce Type: new 
Abstract: Deep learning-based automatic medical image segmentation plays a critical role in clinical diagnosis and treatment planning but remains challenging in few-shot scenarios due to the scarcity of annotated training data. Recently, self-supervised foundation models such as DINOv3, which were trained on large natural image datasets, have shown strong potential for dense feature extraction that can help with the few-shot learning challenge. Yet, their direct application to medical images is hindered by domain differences. In this work, we propose DINO-AugSeg, a novel framework that leverages DINOv3 features to address the few-shot medical image segmentation challenge. Specifically, we introduce WT-Aug, a wavelet-based feature-level augmentation module that enriches the diversity of DINOv3-extracted features by perturbing frequency components, and CG-Fuse, a contextual information-guided fusion module that exploits cross-attention to integrate semantic-rich low-resolution features with spatially detailed high-resolution features. Extensive experiments on six public benchmarks spanning five imaging modalities, including MRI, CT, ultrasound, endoscopy, and dermoscopy, demonstrate that DINO-AugSeg consistently outperforms existing methods under limited-sample conditions. The results highlight the effectiveness of incorporating wavelet-domain augmentation and contextual fusion for robust feature representation, suggesting DINO-AugSeg as a promising direction for advancing few-shot medical image segmentation. Code and data will be made available on https://github.com/apple1986/DINO-AugSeg.

تم اقتراح إطار عمل جديد يسمى DINO-AugSeg لتحسين تقسيم الصور الطبية في سيناريوهات قليلة الأمثلة من خلال الاستفادة من ميزات ذاتية الإشراف تعتمد على DINOv3. يتناول هذا النهج تحدي ندرة بيانات التدريب المعلّمة في البيئات السريرية، باستخدام تعزيز الميزات على مستوى الموجات ودمج المعلومات السياقية لتحسين دقة التقسيم عبر مختلف طرق التصوير مثل التصوير بالرنين المغناطيسي والأشعة المقطعية.

Se ha propuesto un nuevo marco llamado DINO-AugSeg para mejorar la segmentación de imágenes médicas en escenarios de pocos ejemplos, aprovechando características auto-supervisadas basadas en DINOv3. Este enfoque aborda el desafío de la escasez de datos de entrenamiento anotados en entornos clínicos, utilizando una augmentación de características a nivel de wavelet y una fusión guiada por información contextual para mejorar la precisión de la segmentación en diversas modalidades de imagen como MRI y CT.

Un nouveau cadre nommé DINO-AugSeg a été proposé pour améliorer la segmentation d'images médicales en quelques exemples en s'appuyant sur des caractéristiques auto-supervisées basées sur DINOv3. Cette approche répond au défi du manque de données d'entraînement annotées dans les milieux cliniques, en utilisant une augmentation des caractéristiques au niveau des ondelettes et une fusion guidée par des informations contextuelles pour améliorer la précision de la segmentation à travers diverses modalités d'imagerie telles que l'IRM et le CT.

A novel framework named DINO-AugSeg has been proposed to enhance few-shot medical image segmentation by leveraging DINOv3-based self-supervised features. This approach addresses the challenge of limited annotated training data in clinical settings, utilizing wavelet-based feature-level augmentation and contextual information-guided fusion to improve segmentation accuracy across various imaging modalities such as MRI and CT.

Exploiting DINOv3-Based Self-Supervised Features for Robust Few-Shot Medical Image Segmentation

arXiv:2601.00705v2 Announce Type: replace 
Abstract: We introduce RGS-SLAM, a robust Gaussian-splatting SLAM framework that replaces the residual-driven densification stage of GS-SLAM with a training-free correspondence-to-Gaussian initialization. Instead of progressively adding Gaussians as residuals reveal missing geometry, RGS-SLAM performs a one-shot triangulation of dense multi-view correspondences derived from DINOv3 descriptors refined through a confidence-aware inlier classifier, generating a well-distributed and structure-aware Gaussian seed prior to optimization. This initialization stabilizes early mapping and accelerates convergence by roughly 20\%, yielding higher rendering fidelity in texture-rich and cluttered scenes while remaining fully compatible with existing GS-SLAM pipelines. Evaluated on the TUM RGB-D and Replica datasets, RGS-SLAM achieves competitive or superior localization and reconstruction accuracy compared with state-of-the-art Gaussian and point-based SLAM systems, sustaining real-time mapping performance at up to 925 FPS. Project page:https://breeze1124.github.io/rgs-slam-project-page/

يمثل تقديم RGS-SLAM تقدمًا كبيرًا في تكنولوجيا التوطين ورسم الخرائط المتزامنة (SLAM)، حيث يحل محل مرحلة التكثيف التقليدية المعتمدة على المتبقيين بنهج بدء تشغيل كثيف لمرة واحدة. يستخدم هذا الإطار الجديد موصّفات DINOv3 ومصنفًا واعيًا للثقة لتوليد بذور غاوسية قوية للت优化، مما يعزز استقرار رسم الخرائط وسرعة التقارب بنسبة تقارب 20%.

La introducción de RGS-SLAM marca un avance significativo en la tecnología de localización y mapeo simultáneos (SLAM), reemplazando la etapa de densificación tradicional por un enfoque de inicialización densa de un solo disparo. Este nuevo marco utiliza descriptores DINOv3 y un clasificador de inliers consciente de la confianza para generar una semilla gaussiana robusta para la optimización, mejorando la estabilidad del mapeo y la velocidad de convergencia en aproximadamente un 20%.

L'introduction de RGS-SLAM représente une avancée significative dans la technologie de la localisation et de la cartographie simultanées (SLAM), remplaçant l'étape de densification traditionnelle par une approche d'initialisation dense en une seule fois. Ce nouveau cadre utilise des descripteurs DINOv3 et un classificateur d'inliers conscient de la confiance pour générer une graine gaussienne robuste pour l'optimisation, améliorant la stabilité de la cartographie et la vitesse de convergence d'environ 20 %.

The introduction of RGS-SLAM marks a significant advancement in simultaneous localization and mapping (SLAM) technology, replacing the traditional residual-driven densification stage with a one-shot dense initialization approach. This new framework utilizes DINOv3 descriptors and a confidence-aware inlier classifier to generate a robust Gaussian seed for optimization, enhancing mapping stability and convergence speed by approximately 20%.

RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization

Betron.io is a blockchain powered prediction and gaming platform where people can forecast market moves, compete in skill based games, and interact with crypto in new ways.

DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting

Was this article worth reading? Share it

Betron.io

LucidQuery AI

Deptho.ai

AiReelGenerator.com

Videotok

Attentive AI

Ready to build your own newsroom?