arXiv:2511.14518v1 Announce Type: new 
Abstract: Low Dose Computed Tomography (LDCT) is widely used as an imaging solution to aid diagnosis and other clinical tasks. However, this comes at the price of a deterioration in image quality due to the low dose of radiation used to reduce the risk of secondary cancer development. While some efficient methods have been proposed to enhance LDCT quality, many overestimate noise and perform excessive smoothing, leading to a loss of critical details. In this paper, we introduce D-PerceptCT, a novel architecture inspired by key principles of the Human Visual System (HVS) to enhance LDCT images. The objective is to guide the model to enhance or preserve perceptually relevant features, thereby providing radiologists with CT images where critical anatomical structures and fine pathological details are perceptu- ally visible. D-PerceptCT consists of two main blocks: 1) a Visual Dual-path Extractor (ViDex), which integrates semantic priors from a pretrained DINOv2 model with local spatial features, allowing the network to incorporate semantic-awareness during enhancement; (2) a Global-Local State-Space block that captures long-range information and multiscale features to preserve the important structures and fine details for diagnosis. In addition, we propose a novel deep perceptual loss, designated as the Deep Perceptual Relevancy Loss Function (DPRLF), which is inspired by human contrast sensitivity, to further emphasize perceptually important features. Extensive experiments on the Mayo2016 dataset demonstrate the effectiveness of D-PerceptCT method for LDCT enhancement, showing better preservation of structural and textural information within LDCT images compared to SOTA methods.

D-PerceptCT هو هيكل جديد مصمم لتحسين جودة صور الأشعة المقطعية ذات الجرعة المنخفضة (LDCT)، والتي تُستخدم على نطاق واسع في التصوير الطبي ولكن غالبًا ما تعاني من ضعف الجودة بسبب تقليل جرعة الإشعاع. يمكن أن تؤدي طرق التحسين التقليدية إلى تلطيف مفرط وفقدان التفاصيل المهمة. يهدف D-PerceptCT إلى تحسين جودة الصورة من خلال الحفاظ على الميزات ذات الأهمية الإدراكية، مستلهمًا من النظام البصري البشري. يتضمن مستخرج ثنائي المسار البصري (ViDex) لدمج المعلومات الدلالية.

D-PerceptCT es una nueva arquitectura diseñada para mejorar la calidad de las imágenes de Tomografía Computarizada de Baja Dosis (LDCT), que se utilizan comúnmente en la imagenología médica, pero que a menudo sufren de mala calidad debido a la reducción de la dosis de radiación. Los métodos de mejora tradicionales pueden llevar a un suavizado excesivo y a la pérdida de detalles importantes. D-PerceptCT busca mejorar la calidad de las imágenes al preservar características perceptualmente relevantes, inspirándose en el Sistema Visual Humano. Incluye un Extractor Dual Visual (ViDex) para integrar…

D-PerceptCT est une nouvelle architecture conçue pour améliorer la qualité des images de tomographie par ordinateur à faible dose (LDCT), couramment utilisées en imagerie médicale mais souvent affectées par une mauvaise qualité en raison de doses de radiation réduites. Les méthodes d'amélioration traditionnelles peuvent entraîner un lissage excessif et une perte de détails importants. D-PerceptCT vise à améliorer la qualité des images en préservant les caractéristiques perceptuellement pertinentes, inspiré par le système visuel humain. Il comprend un extracteur à double chemin visuel (ViDex) p…

D-PerceptCT is a new architecture designed to enhance the quality of Low Dose Computed Tomography (LDCT) images, which are commonly used in medical imaging but often suffer from poor quality due to reduced radiation doses. Traditional enhancement methods can lead to excessive smoothing and loss of important details. D-PerceptCT aims to improve image quality by preserving perceptually relevant features, inspired by the Human Visual System. It includes a Visual Dual-path Extractor (ViDex) to integrate semantic information for better image clarity.

D-PerceptCT: Deep Perceptual Enhancement for Low-Dose CT Images

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arXiv:2511.15468v1 Announce Type: new 
Abstract: Purse seiners play a crucial role in tuna fishing, as approximately 69% of the world's tropical tuna is caught using this gear. All tuna Regional Fisheries Management Organizations have established minimum standards to use electronic monitoring (EM) in fisheries in addition to traditional observers. The EM systems produce a massive amount of video data that human analysts must process. Integrating artificial intelligence (AI) into their workflow can decrease that workload and improve the accuracy of the reports. However, species identification still poses significant challenges for AI, as achieving balanced performance across all species requires appropriate training data. Here, we quantify the difficulty experts face to distinguish bigeye tuna (BET, Thunnus Obesus) from yellowfin tuna (YFT, Thunnus Albacares) using images captured by EM systems. We found inter-expert agreements of 42.9% $\pm$ 35.6% for BET and 57.1% $\pm$ 35.6% for YFT. We then present a multi-stage pipeline to estimate the species composition of the catches using a reliable ground-truth dataset based on identifications made by observers on board. Three segmentation approaches are compared: Mask R-CNN, a combination of DINOv2 with SAM2, and a integration of YOLOv9 with SAM2. We found that the latest performs the best, with a validation mean average precision of 0.66 $\pm$ 0.03 and a recall of 0.88 $\pm$ 0.03. Segmented individuals are tracked using ByteTrack. For classification, we evaluate a standard multiclass classification model and a hierarchical approach, finding a superior generalization by the hierarchical. All our models were cross-validated during training and tested on fishing operations with fully known catch composition. Combining YOLOv9-SAM2 with the hierarchical classification produced the best estimations, with 84.8% of the individuals being segmented and classified with a mean average error of 4.5%.

تعتبر شباك السحب ضرورية في صيد التونة، حيث تمثل حوالي 69% من إجمالي صيد التونة الاستوائية في العالم. لتعزيز المراقبة، وضعت منظمات إدارة مصايد الأسماك الإقليمية معايير دنيا لاستخدام المراقبة الإلكترونية (EM) بالإضافة إلى المراقبين التقليديين. ومع ذلك، لا تزال عملية تحديد الأنواع تمثل تحديًا كبيرًا للذكاء الاصطناعي، حيث تتطلب بيانات تدريب متوازنة. تسلط هذه الدراسة الضوء على الصعوبات التي يواجهها الخبراء في تمييز التونة ذات العين الكبيرة من التونة ذات الزعانف الصفراء باستخدام الصور الملتقطة بواسطة أنظمة EM.

Los cerqueros son esenciales en la pesca de atún, representando aproximadamente el 69% de la captura mundial de atún tropical. Para mejorar la supervisión, las organizaciones regionales de gestión pesquera han establecido el uso de monitoreo electrónico (ME) junto con observadores tradicionales. Sin embargo, la identificación de especies de atún sigue siendo un desafío para los sistemas de IA, que requieren datos de entrenamiento equilibrados. Este estudio destaca las dificultades que enfrentan los expertos para distinguir entre el atún ojo de buey y el atún de aleta amarilla utilizando imágen…

Les senneurs jouent un rôle crucial dans la pêche au thon, représentant environ 69 % de la capture mondiale de thon tropical. Pour améliorer la surveillance, les organisations régionales de gestion des pêches ont imposé l'utilisation de la surveillance électronique (SE) en plus des observateurs traditionnels. Cependant, l'identification des espèces de thon reste un défi pour les systèmes d'IA, qui nécessitent des données d'entraînement équilibrées. Cette étude met en lumière les difficultés rencontrées par les experts pour distinguer le thon obèse du thon jaune à l'aide d'images capturées par …

Purse seiners are essential in tuna fishing, accounting for about 69% of the global catch of tropical tuna. To enhance monitoring, Regional Fisheries Management Organizations have mandated the use of electronic monitoring (EM) alongside traditional observers. However, the identification of tuna species remains challenging for AI systems, which require balanced training data. This study highlights the difficulties experts face in distinguishing between bigeye tuna and yellowfin tuna using EM-captured images.

Deep Learning for Accurate Vision-based Catch Composition in Tropical Tuna Purse Seiners

arXiv:2504.15669v3 Announce Type: replace 
Abstract: Few-shot semantic segmentation has attracted growing interest for its ability to generalize to novel object categories using only a few annotated samples. To address data scarcity, recent methods incorporate multiple foundation models to improve feature transferability and segmentation performance. However, they often rely on dual-branch architectures that combine pre-trained encoders to leverage complementary strengths, a design that limits flexibility and efficiency. This raises a fundamental question: can we build a unified model that integrates knowledge from different foundation architectures? Achieving this is, however, challenging due to the misalignment between class-agnostic segmentation capabilities and fine-grained discriminative representations. To this end, we present UINO-FSS, a novel framework built on the key observation that early-stage DINOv2 features exhibit distribution consistency with SAM's output embeddings. This consistency enables the integration of both models' knowledge into a single-encoder architecture via coarse-to-fine multimodal distillation. In particular, our segmenter consists of three core components: a bottleneck adapter for embedding alignment, a meta-visual prompt generator that leverages dense similarity volumes and semantic embeddings, and a mask decoder. Using hierarchical cross-model distillation, we effectively transfer SAM's knowledge into the segmenter, further enhanced by Mamba-based 4D correlation mining on support-query pairs. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ show that UINO-FSS achieves new state-of-the-art results under the 1-shot setting, with mIoU of 80.6 (+3.8%) on PASCAL-5$^i$ and 64.5 (+4.1%) on COCO-20$^i$, demonstrating the effectiveness of our unified approach.

UINO-FSS هو إطار جديد مصمم لتجزئة المعاني القليلة، مما يسمح بالتعميم على فئات جديدة من الكائنات باستخدام عدد قليل فقط من العينات المعلّمة. يتناول نقص البيانات من خلال دمج المعرفة من نماذج أساسية متعددة، متجاوزًا قيود الهياكل ذات الفرعين. يستفيد الإطار من ميزات DINOv2 في المراحل المبكرة التي تتماشى جيدًا مع تمثيلات الإخراج لنموذج Segment Anything (SAM).

UINO-FSS es un nuevo marco diseñado para la segmentación semántica de pocos disparos, que permite la generalización a nuevas categorías de objetos utilizando solo unas pocas muestras anotadas. Aborda la escasez de datos al integrar conocimientos de varios modelos fundamentales, superando las limitaciones de las arquitecturas de doble rama. El marco aprovecha las características tempranas de DINOv2 que se alinean bien con las incrustaciones de salida del Segment Anything Model (SAM).

UINO-FSS est un nouveau cadre conçu pour la segmentation sémantique à faible échantillonnage, permettant la généralisation à de nouvelles catégories d'objets avec un minimum d'échantillons annotés. Il répond à la rareté des données en intégrant des connaissances provenant de divers modèles fondamentaux, surmontant les limitations des architectures à double branche. Le cadre exploite les caractéristiques précoces de DINOv2 qui s'alignent bien avec les embeddings de sortie du Segment Anything Model (SAM).

UINO-FSS is a novel framework designed for few-shot semantic segmentation, which allows for generalization to new object categories using minimal annotated samples. It addresses data scarcity by integrating knowledge from various foundation models, overcoming limitations of dual-branch architectures. The framework leverages early-stage DINOv2 features that align well with the output embeddings of the Segment Anything Model (SAM).

UINO-FSS: Unifying Representation Learning and Few-shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation

arXiv:2511.15406v1 Announce Type: cross 
Abstract: Reliable semantic segmentation is essential for clinical decision making, yet deep models rarely provide explicit statistical guarantees on their errors. We introduce a simple post-hoc framework that constructs confidence masks with distribution-free, image-level control of false-positive predictions. Given any pretrained segmentation model, we define a nested family of shrunken masks obtained either by increasing the score threshold or by applying morphological erosion. A labeled calibration set is used to select a single shrink parameter via conformal prediction, ensuring that, for new images that are exchangeable with the calibration data, the proportion of false positives retained in the confidence mask stays below a user-specified tolerance with high probability. The method is model-agnostic, requires no retraining, and provides finite-sample guarantees regardless of the underlying predictor. Experiments on a polyp-segmentation benchmark demonstrate target-level empirical validity. Our framework enables practical, risk-aware segmentation in settings where over-segmentation can have clinical consequences. Code at https://github.com/deel-ai-papers/conseco.

تم تقديم إطار عمل جديد للتحكم في الإيجابيات الكاذبة في تقسيم الصور، مما يعزز موثوقية التقسيم الدلالي في اتخاذ القرارات السريرية. تستخدم هذه الطريقة المستقلة عن النموذج التنبؤ المتوافق لإنشاء أقنعة ثقة تحافظ على مستوى محدد مسبقًا من الإيجابيات الكاذبة، دون الحاجة إلى إعادة التدريب. تُظهر الطريقة ضمانات عالية الاحتمالية للصور الجديدة، مما يمثل تقدمًا كبيرًا في التصوير الطبي.

Se ha introducido un nuevo marco para controlar los falsos positivos en la segmentación de imágenes, mejorando la fiabilidad de la segmentación semántica en la toma de decisiones clínicas. Este enfoque independiente del modelo utiliza la predicción conformal para crear máscaras de confianza que mantienen una tolerancia definida por el usuario para los falsos positivos, sin necesidad de reentrenamiento. El método demuestra garantías de alta probabilidad para nuevas imágenes, representando un avance significativo en la imagen médica.

Un nouveau cadre pour contrôler les faux positifs dans la segmentation d'images a été introduit, améliorant la fiabilité de la segmentation sémantique dans la prise de décision clinique. Cette approche indépendante du modèle utilise la prédiction conforme pour créer des masques de confiance qui maintiennent une tolérance définie par l'utilisateur pour les faux positifs, sans nécessiter de réentraînement. La méthode démontre des garanties de haute probabilité pour de nouvelles images, représentant une avancée significative dans l'imagerie médicale.

A new framework for controlling false positives in image segmentation has been introduced, enhancing the reliability of semantic segmentation in clinical decision-making. This model-agnostic approach utilizes conformal prediction to create confidence masks that maintain a user-defined tolerance for false positives, without requiring retraining. The method demonstrates high probability guarantees for new images, making it a significant advancement in medical imaging.

Controlling False Positives in Image Segmentation via Conformal Prediction

arXiv:2504.07503v2 Announce Type: replace 
Abstract: Event cameras asynchronously capture brightness changes with microsecond latency, offering exceptional temporal precision but suffering from severe noise and signal inconsistencies. Unlike conventional signals, events carry state information through polarities and process information through inter-event time intervals. However, existing event filters often ignore the latter, producing outputs that are sparser than the raw input and limiting the reconstruction of continuous irradiance dynamics. We propose the Event Density Flow Filter (EDFilter), a framework that models event generation as threshold-crossing probability fluxes arising from the stochastic diffusion of irradiance trajectories. EDFilter performs nonparametric, kernel-based estimation of probability flux and reconstructs the continuous event density flow using an O(1) recursive solver, enabling real-time processing. The Rotary Event Dataset (RED), featuring microsecond-resolution ground-truth irradiance flow under controlled illumination is also presented for event quality evaluation. Experiments demonstrate that EDFilter achieves high-fidelity, physically interpretable event denoising and motion reconstruction.

تقدم الورقة فلاتر تدفق كثافة الأحداث (EDFilter)، وهو إطار مبتكر لتصفية تدفقات الأحداث يستخدم تقدير تدفق الاحتمالات لتحسين معالجة بيانات كاميرات الأحداث. تتناول هذه الطريقة قيود الفلاتر الحالية من خلال نمذجة توليد الأحداث كتيارات احتمالية تتجاوز العتبات، مما يسمح بإعادة بناء تدفق كثافة الأحداث المستمر في الوقت الحقيقي. يهدف الإطار إلى تحسين دقة وكفاءة أنظمة الرؤية المعتمدة على الأحداث.

El artículo presenta el Filtro de Flujo de Densidad de Eventos (EDFilter), un marco novedoso para el filtrado de flujos de eventos que utiliza la estimación de flujo de probabilidad para mejorar el procesamiento de datos de cámaras de eventos. Este enfoque aborda las limitaciones de los filtros de eventos existentes al modelar la generación de eventos como flujos de probabilidad que cruzan umbrales, permitiendo la reconstrucción en tiempo real del flujo de densidad de eventos continuo. El marco busca mejorar la precisión y eficiencia de los sistemas de visión basados en eventos.

L'article présente le filtre de flux de densité d'événements (EDFilter), un cadre novateur pour le filtrage de flux d'événements qui utilise l'estimation de flux de probabilité pour améliorer le traitement des données des caméras d'événements. Cette approche répond aux limitations des filtres d'événements existants en modélisant la génération d'événements comme des flux de probabilité franchissant des seuils, permettant ainsi la reconstruction en temps réel du flux de densité d'événements continu. Le cadre vise à améliorer la précision et l'efficacité des systèmes de vision basés sur des événe…

The paper introduces the Event Density Flow Filter (EDFilter), a novel framework for event stream filtering that utilizes probability flux estimation to enhance the processing of event camera data. This approach addresses the limitations of existing event filters by modeling event generation as threshold-crossing probability fluxes, allowing for real-time reconstruction of continuous event density flow. The framework aims to improve the accuracy and efficiency of event-based vision systems.

Event Stream Filtering via Probability Flux Estimation

arXiv:2511.15499v1 Announce Type: new 
Abstract: Autoregressive models have recently shown great promise in visual generation by leveraging discrete token sequences akin to language modeling. However, existing approaches often suffer from inefficiency, either due to token-by-token decoding or the complexity of multi-scale representations. In this work, we introduce Expanding Autoregressive Representation (EAR), a novel generation paradigm that emulates the human visual system's center-outward perception pattern. EAR unfolds image tokens in a spiral order from the center and progressively expands outward, preserving spatial continuity and enabling efficient parallel decoding. To further enhance flexibility and speed, we propose a length-adaptive decoding strategy that dynamically adjusts the number of tokens predicted at each step. This biologically inspired design not only reduces computational cost but also improves generation quality by aligning the generation order with perceptual relevance. Extensive experiments on ImageNet demonstrate that EAR achieves state-of-the-art trade-offs between fidelity and efficiency on single-scale autoregressive models, setting a new direction for scalable and cognitively aligned autoregressive image generation.

تقدم الورقة مفهوم تمثيل التوليد التلقائي الموسع (EAR)، وهو نموذج جديد لتوليد الصور يحاكي نظام الرؤية البشري من خلال إدراكه من المركز إلى الخارج. تعمل هذه الطريقة على تحسين الكفاءة من خلال نشر رموز الصورة بترتيب حلزوني، مما يسمح بفك تشفير متوازي ويحافظ على الاستمرارية المكانية. بالإضافة إلى ذلك، يتم اقتراح استراتيجية فك تشفير قابلة للتكيف مع الطول لتعزيز المرونة والسرعة، مما يقلل من التكاليف الحسابية ويحسن جودة التوليد.

El artículo presenta la Representación Autoregresiva Expandida (EAR), un nuevo paradigma para la generación visual que imita el sistema visual humano al percibir de manera centrípeta. Este método mejora la eficiencia al desplegar tokens de imagen en un orden espiral, permitiendo la decodificación paralela y preservando la continuidad espacial. Además, se propone una estrategia de decodificación adaptativa en longitud para mejorar la flexibilidad y velocidad, reduciendo así los costos computacionales y mejorando la calidad de generación.

Cet article présente la Représentation Autoregressive Élargie (EAR), un nouveau paradigme pour la génération visuelle qui imite le système visuel humain en percevant de manière centripète. Cette méthode améliore l'efficacité en déroulant les tokens d'image dans un ordre spiralé, permettant un décodage parallèle et préservant la continuité spatiale. De plus, une stratégie de décodage adaptative en longueur est proposée pour améliorer la flexibilité et la rapidité, réduisant ainsi les coûts computationnels et améliorant la qualité de génération.

The paper introduces Expanding Autoregressive Representation (EAR), a new paradigm for visual generation that mimics the human visual system's center-outward perception. This method improves efficiency by unfolding image tokens in a spiral order, allowing for parallel decoding and preserving spatial continuity. Additionally, a length-adaptive decoding strategy is proposed to enhance flexibility and speed, ultimately reducing computational costs and improving generation quality.

D-PerceptCT: Deep Perceptual Enhancement for Low-Dose CT Images

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