arXiv:2511.02720v1 Announce Type: new 
Abstract: Convolutional neural networks (CNNs) underpin many modern computer vision systems. With applications ranging from common to critical areas, a need to explain and understand the model and its decisions (XAI) emerged. Prior works suggest that in the top layers of CNNs, the individual channels can be attributed to classifying human-understandable concepts. Concept relevance propagation (CRP) methods can backtrack predictions to these channels and find images that most activate these channels. However, current CRP workflows are largely manual: experts must inspect activation images to name the discovered concepts and must synthesize verbose explanations from relevance maps, limiting the accessibility of the explanations and their scalability.
  To address these issues, we introduce Large Language model EXplaIns COncept Relevance Propagation (LLEXICORP), a modular pipeline that couples CRP with a multimodal large language model. Our approach automatically assigns descriptive names to concept prototypes and generates natural-language explanations that translate quantitative relevance distributions into intuitive narratives. To ensure faithfulness, we craft prompts that teach the language model the semantics of CRP through examples and enforce a separation between naming and explanation tasks. The resulting text can be tailored to different audiences, offering low-level technical descriptions for experts and high-level summaries for non-technical stakeholders.
  We qualitatively evaluate our method on various images from ImageNet on a VGG16 model. Our findings suggest that integrating concept-based attribution methods with large language models can significantly lower the barrier to interpreting deep neural networks, paving the way for more transparent AI systems.

تقوم LLEXICORP بتحقيق تقدم في تعزيز قابلية تفسير الشبكات العصبية التلافيفية (CNN)، التي تعتبر حيوية لأنظمة الرؤية الحاسوبية الحديثة. مع تزايد الطلب على الشفافية في الذكاء الاصطناعي، يهدف عملهم على طرق انتشار أهمية المفاهيم (CRP) إلى توضيح كيفية اتخاذ CNN للقرارات، وربط توقعات النموذج بمفاهيم مفهومة.

LLEXICORP está avanzando en la mejora de la explicabilidad de las redes neuronales convolucionales (CNN), que son cruciales para los sistemas modernos de visión por computadora. A medida que crece la demanda de transparencia en la IA, su trabajo en métodos de propagación de relevancia de conceptos (CRP) busca aclarar cómo las CNN toman decisiones, vinculando las predicciones del modelo a conceptos comprensibles.

LLEXICORP fait des progrès dans l'amélioration de l'explicabilité des réseaux de neurones convolutifs (CNN), qui sont essentiels pour les systèmes de vision par ordinateur modernes. Alors que la demande de transparence dans l'IA augmente, leur travail sur les méthodes de propagation de la pertinence des concepts (CRP) vise à clarifier comment les CNN prennent des décisions, reliant les prédictions du modèle à des concepts compréhensibles.

LLEXICORP is making strides in enhancing the explainability of convolutional neural networks (CNNs), which are crucial for modern computer vision systems. As the demand for transparency in AI grows, their work on concept relevance propagation (CRP) methods aims to clarify how CNNs make decisions, linking model predictions to understandable concepts.

LLEXICORP: End-user Explainability of Convolutional Neural Networks

arXiv:2601.07982v1 Announce Type: new 
Abstract: We develop a new statistical ideal observer model that performs holistic visual search (or gist) processing in part by placing thresholds on minimum extractable image features. In this model, the ideal observer reduces the number of free parameters thereby shrinking down the system. The applications of this novel framework is in medical image perception (for optimizing imaging systems and algorithms), computer vision, benchmarking performance and enabling feature selection/evaluations. Other applications are in target detection and recognition in defense/security as well as evaluating sensors and detectors.

تم تطوير نموذج جديد للمراقب المثالي الإحصائي لتحسين معالجة البحث البصري الشامل من خلال وضع حدود على الميزات القابلة للاستخراج من الصورة. يهدف هذا النموذج إلى تبسيط النظام من خلال تقليل المعلمات الحرة، مع تطبيقات في إدراك الصور الطبية ورؤية الكمبيوتر والدفاع/الأمن.

Se ha desarrollado un nuevo modelo estadístico de observador ideal para mejorar el procesamiento de búsqueda visual holística al establecer umbrales sobre las características de imagen mínimas extraíbles. Este modelo busca optimizar el sistema reduciendo los parámetros libres, con aplicaciones en percepción de imágenes médicas, visión por computadora y defensa/seguridad.

Un nouveau modèle d'observateur idéal statistique a été développé pour améliorer le traitement de la recherche visuelle holistique en établissant des seuils sur les caractéristiques d'image minimales extractibles. Ce modèle vise à rationaliser le système en réduisant les paramètres libres, avec des applications dans la perception d'images médicales, la vision par ordinateur et la défense/sécurité.

A new statistical ideal observer model has been developed to enhance holistic visual search processing by establishing thresholds on minimum extractable image features. This model aims to streamline the system by reducing free parameters, with applications in medical image perception, computer vision, and defense/security.

Likelihood ratio for a binary Bayesian classifier under a noise-exclusion model

arXiv:2601.07976v1 Announce Type: cross 
Abstract: This study advances task-based image quality assessment by developing an anthropomorphic thresholded visual-search model observer. The model is an ideal observer for thresholded data inspired by the human visual system, allowing selective processing of high-salience features to improve discrimination performance. By filtering out irrelevant variability, the model enhances diagnostic accuracy and computational efficiency.
  The observer employs a two-stage framework: candidate selection and decision-making. Using thresholded data during candidate selection refines regions of interest, while stage-specific feature processing optimizes performance. Simulations were conducted to evaluate the effects of thresholding on feature maps, candidate localization, and multi-feature scenarios. Results demonstrate that thresholding improves observer performance by excluding low-salience features, particularly in noisy environments. Intermediate thresholds often outperform no thresholding, indicating that retaining only relevant features is more effective than keeping all features.
  Additionally, the model demonstrates effective training with fewer images while maintaining alignment with human performance. These findings suggest that the proposed novel framework can predict human visual search performance in clinically realistic tasks and provide solutions for model observer training with limited resources. Our novel approach has applications in other areas where human visual search and detection tasks are modeled such as in computer vision, machine learning, defense and security image analysis.

قدمت دراسة حديثة نموذجًا للمراقب البصري الأنثروبومورفي المعتمد على العتبة، مما يعزز تقييم جودة الصورة القائم على المهام من خلال تقليد النظام البصري البشري. يقوم هذا النموذج بمعالجة الميزات ذات الأهمية العالية بشكل انتقائي، مما يحسن أداء التمييز والدقة التشخيصية بينما يقوم بتصفية التباين غير ذي الصلة.

Un estudio reciente ha presentado un modelo de observador visual antropomórfico umbralizado, mejorando la evaluación de la calidad de imagen basada en tareas al imitar el sistema visual humano. Este modelo procesa selectivamente características de alta saliencia, mejorando el rendimiento de discriminación y la precisión diagnóstica mientras filtra la variabilidad irrelevante.

Une étude récente a introduit un modèle d'observateur visuel anthropomorphique à seuil, améliorant l'évaluation de la qualité d'image basée sur des tâches en imitant le système visuel humain. Ce modèle traite sélectivement des caractéristiques de haute saillance, améliorant la performance de discrimination et la précision diagnostique tout en filtrant la variabilité non pertinente.

A recent study has introduced an anthropomorphic thresholded visual-search model observer, enhancing task-based image quality assessment by mimicking the human visual system. This model selectively processes high-salience features, improving discrimination performance and diagnostic accuracy while filtering out irrelevant variability.

Application of Ideal Observer for Thresholded Data in Search Task

One More Thing in AI – Your Shortcut to AI Mastery

LLEXICORP: End-user Explainability of Convolutional Neural Networks

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