not yet published
Conference article
Open Access
Reviving ConvNeXt for efficient convolutional diffusion models
Kwon Taesung, Bianchi Lorenzo, Wittke Lennart, Watine Felix, Carrara Fabio, Ye Jong Chul, Weber Romann, Azevedo Vinicius
Recent diffusion models increasingly favor Transformer backbones, motivated by the remarkable scalability of fully attentional architectures. Yet the locality bias, parameter efficiency, and hardware friendliness--the attributes that established ConvNets as the efficient vision backbone--have seen limited exploration in modern generative modeling. Here we introduce the fully convolutional diffusion model (FCDM), a model having a backbone similar to ConvNeXt, but designed for conditional diffusion modeling. We find that using only 50% of the FLOPs of DiT-XL/2, FCDM-XL achieves competitive performance with 7× and 7.5× fewer training steps at 256×256 and 512×512 resolutions, respectively. Remarkably, FCDM-XL can be trained on a 4-GPU system, highlighting the exceptional training efficiency of our architecture. Our results demonstrate that modern convolutional designs provide a competitive and highly efficient alternative for scaling diffusion models, reviving ConvNeXt as a simple yet powerful building block for efficient generative modeling.
Kwon Taesung, Bianchi Lorenzo, Wittke Lennart, Watine Felix, Carrara Fabio, Ye Jong Chul, Weber Romann, Azevedo Vinicius
Recent diffusion models increasingly favor Transformer backbones, motivated by the remarkable scalability of fully attentional architectures. Yet the locality bias, parameter efficiency, and hardware friendliness--the attributes that established ConvNets as the efficient vision backbone--have seen limited exploration in modern generative modeling. Here we introduce the fully convolutional diffusion model (FCDM), a model having a backbone similar to ConvNeXt, but designed for conditional diffusion modeling. We find that using only 50% of the FLOPs of DiT-XL/2, FCDM-XL achieves competitive performance with 7× and 7.5× fewer training steps at 256×256 and 512×512 resolutions, respectively. Remarkably, FCDM-XL can be trained on a 4-GPU system, highlighting the exceptional training efficiency of our architecture. Our results demonstrate that modern convolutional designs provide a competitive and highly efficient alternative for scaling diffusion models, reviving ConvNeXt as a simple yet powerful building block for efficient generative modeling.
not yet published
Conference article
Open Access
One patch to caption them all: a unified zero-shot captioning framework
Bianchi Lorenzo, Pacini Giacomo, Carrara Fabio, Messina Nicola, Amato Giuseppe, Falchi Fabrizio
Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on paired image-text data. To caption an image, they proceed by textually decoding a text-aligned image feature, but they limit their scope to global representations and whole-image captions. We present a unified framework for zero-shot captioning that shifts from an image-centric to a patch-centric paradigm, enabling the captioning of arbitrary regions without the need of region-level supervision. Instead of relying on global image representations, we treat individual patches as atomic captioning units and aggregate them to describe arbitrary regions, from single patches to non-contiguous areas and entire images. We analyze the key ingredients that enable current latent captioners to work in our novel proposed framework. Experiments demonstrate that backbones producing meaningful, dense visual features, such as DINO, are key to achieving state-of-the-art performance in multiple region-based captioning tasks. Compared to other baselines and state-of-the-art competitors, our models achieve better performance on zero-shot dense captioning and region-set captioning. We also introduce a new trace captioning task that further demonstrates the effectiveness of patch-wise semantic representations for flexible caption generation. Project page at https://paciosoft.com/Patch-ioner/ .Project(s): Future Artificial Intelligence Research, Italian Strengthening of ESFRI RI RESILIENCE, Social and hUman ceNtered XR, a MUltimedia platform for Content Enrichment and Search in audiovisual archives
Bianchi Lorenzo, Pacini Giacomo, Carrara Fabio, Messina Nicola, Amato Giuseppe, Falchi Fabrizio
Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on paired image-text data. To caption an image, they proceed by textually decoding a text-aligned image feature, but they limit their scope to global representations and whole-image captions. We present a unified framework for zero-shot captioning that shifts from an image-centric to a patch-centric paradigm, enabling the captioning of arbitrary regions without the need of region-level supervision. Instead of relying on global image representations, we treat individual patches as atomic captioning units and aggregate them to describe arbitrary regions, from single patches to non-contiguous areas and entire images. We analyze the key ingredients that enable current latent captioners to work in our novel proposed framework. Experiments demonstrate that backbones producing meaningful, dense visual features, such as DINO, are key to achieving state-of-the-art performance in multiple region-based captioning tasks. Compared to other baselines and state-of-the-art competitors, our models achieve better performance on zero-shot dense captioning and region-set captioning. We also introduce a new trace captioning task that further demonstrates the effectiveness of patch-wise semantic representations for flexible caption generation. Project page at https://paciosoft.com/Patch-ioner/ .Project(s): Future Artificial Intelligence Research, Italian Strengthening of ESFRI RI RESILIENCE, Social and hUman ceNtered XR, a MUltimedia platform for Content Enrichment and Search in audiovisual archives
not yet published
Conference article
Open Access
Classifying the ripeness of mangoes using image processing and deep learning
Jirapattarasakul S., Thanyacharoen S., Leone G. R., Akkaralaertsest T., Yingthawornsuk T.
Mango is a popular fruit that comes in many different varieties. Each variety has a different flavor, aroma, and texture. Selecting mangoes at the appropriate level of ripeness is therefore important to both consumers and producers. Problems frequently encountered include inconsistencies in sorting mangoes using traditional methods, which often rely on human experience and eyesight. This can affect the quality of mango product. This research focuses on developing a ripeness-rawness screening system for Okrong and Mahachanok mango varieties using Deep Learning and Image Processing techniques. Two CNN (Convolutional Neural Network) models, VGG16, MobileNetV2, and CNN1D, were used to analyze mango images and distinguish between ripe and raw levels. The test results showed that the VGG16 model achieved the highest performance in screening ripeness-rawness of both mango varieties, with an accuracy of 98%, followed by MobileNetV2 at 96% and CNN1D at 92%. For the ripeness-rawness classification of only the Okrong mango variety, the VGG16 model still achieved the highest performance, with an accuracy of 99%, followed by MobileNetV2 at 96% and CNN1D at 95%. These results indicate that CNN models, particularly VGG16, have great potential for application in developing automated mango sorting system based on ripeness-rawness levels. This proposed work can significantly improve efficiency in managing and selecting mango product.
Jirapattarasakul S., Thanyacharoen S., Leone G. R., Akkaralaertsest T., Yingthawornsuk T.
Mango is a popular fruit that comes in many different varieties. Each variety has a different flavor, aroma, and texture. Selecting mangoes at the appropriate level of ripeness is therefore important to both consumers and producers. Problems frequently encountered include inconsistencies in sorting mangoes using traditional methods, which often rely on human experience and eyesight. This can affect the quality of mango product. This research focuses on developing a ripeness-rawness screening system for Okrong and Mahachanok mango varieties using Deep Learning and Image Processing techniques. Two CNN (Convolutional Neural Network) models, VGG16, MobileNetV2, and CNN1D, were used to analyze mango images and distinguish between ripe and raw levels. The test results showed that the VGG16 model achieved the highest performance in screening ripeness-rawness of both mango varieties, with an accuracy of 98%, followed by MobileNetV2 at 96% and CNN1D at 92%. For the ripeness-rawness classification of only the Okrong mango variety, the VGG16 model still achieved the highest performance, with an accuracy of 99%, followed by MobileNetV2 at 96% and CNN1D at 95%. These results indicate that CNN models, particularly VGG16, have great potential for application in developing automated mango sorting system based on ripeness-rawness levels. This proposed work can significantly improve efficiency in managing and selecting mango product.
See at:
gcmm2024.rmutk.ac.th 
| CNR IRIS | CNR IRIS 
| CNR IRIS
not yet published
Journal article
Open Access
A Local Lorentz invariance test with LAGEOS satellites
Lucchesi David, Visco Massimo, Peron Roberto, Rodriguez José C., Pucacco Giuseppe, Anselmo Luciano, Bassan Massimo, Appleby Graham, Cinelli Marco, Di Marco Alessandro, Lucente Marco, Magnafico Carmelo, Pardini Carmen, Sapio Feliciana
Strong theoretical arguments suggest that a breakdown of Lorentz Invariance could arise under some very particular conditions. From an experimental point of view, it is important to test the Local Lorentz Invariance with ever greater precision and in all contexts, regardless of the theoretical motivation for the possible violation. In this paper we discuss some aspects of the gravitational sector. Tests of Lorentz Invariance in the context of gravity are difficult and rare in the literature. Possible violations could arise from quantum physics applied to gravity or the presence of vector and tensor fields mediating the gravitational interaction together with the metric tensor of General Relativity. We present our results in the latter case. We analyzed the orbit of the LAGEOS and LAGEOS II satellites over a period of almost three decades. The effects of the possible preferred frame represented by the cosmic microwave background radiation on the mean argument of latitude of the satellites orbit were considered. These effects would manifest themselves mainly through the post-Newtonian parameter $α_1$, a parameter that has a null value in General Relativity. We constrain this parameterized post-Newtonian parameter down to the level of $α_1 \le 2\times10^{-5}$, improving a previous limit obtained through the Lunar Laser Ranging technique.Source: PHYSICAL REVIEW D
DOI: 10.1103/hj6p-bfyr
Metrics:
Lucchesi David, Visco Massimo, Peron Roberto, Rodriguez José C., Pucacco Giuseppe, Anselmo Luciano, Bassan Massimo, Appleby Graham, Cinelli Marco, Di Marco Alessandro, Lucente Marco, Magnafico Carmelo, Pardini Carmen, Sapio Feliciana
Strong theoretical arguments suggest that a breakdown of Lorentz Invariance could arise under some very particular conditions. From an experimental point of view, it is important to test the Local Lorentz Invariance with ever greater precision and in all contexts, regardless of the theoretical motivation for the possible violation. In this paper we discuss some aspects of the gravitational sector. Tests of Lorentz Invariance in the context of gravity are difficult and rare in the literature. Possible violations could arise from quantum physics applied to gravity or the presence of vector and tensor fields mediating the gravitational interaction together with the metric tensor of General Relativity. We present our results in the latter case. We analyzed the orbit of the LAGEOS and LAGEOS II satellites over a period of almost three decades. The effects of the possible preferred frame represented by the cosmic microwave background radiation on the mean argument of latitude of the satellites orbit were considered. These effects would manifest themselves mainly through the post-Newtonian parameter $α_1$, a parameter that has a null value in General Relativity. We constrain this parameterized post-Newtonian parameter down to the level of $α_1 \le 2\times10^{-5}$, improving a previous limit obtained through the Lunar Laser Ranging technique.Source: PHYSICAL REVIEW D
DOI: 10.1103/hj6p-bfyr
Metrics:
2026
Journal article
Open Access
Quantifying query fairness under unawareness
Jaenich Thomas, Moreo Alejandro, Fabris Alessandro, Mcdonald Graham, Esuli Andrea, Ounis Iadh, Fabrizio Sebastiani
Traditional ranking algorithms are designed to retrieve the most relevant items for a user’s query, but they often inherit biases from data that can unfairly disadvantage vulnerable groups. Fairness in information access systems (IAS) is typically assessed by comparing the distribution of groups in a ranking to a target distribution, such as the overall group distribution in the dataset. These fairness metrics depend on knowing the true group labels for each item. However, when groups are defined by demographic or sensitive attributes, these labels are often unknown, leading to a setting known as “fairness under unawareness.” To address this, group membership can be inferred using machine-learned classifiers, and group prevalence is estimated by counting the predicted labels. Unfortunately, such an estimation is known to be unreliable under dataset shift, compromising the accuracy of fairness evaluations. In this paper, we introduce a robust fairness estimator based on quantification that effectively handles multiple sensitive attributes beyond binary classifications. Our method outperforms existing baselines across various sensitive attributes and, to the best of our knowledge, is the first to establish a reliable protocol for measuring fairness under unawareness across multiple queries and groups.Source: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, vol. 85 (issue articolo 7)
DOI: 10.1613/jair.1.17675
Metrics:
Jaenich Thomas, Moreo Alejandro, Fabris Alessandro, Mcdonald Graham, Esuli Andrea, Ounis Iadh, Fabrizio Sebastiani
Traditional ranking algorithms are designed to retrieve the most relevant items for a user’s query, but they often inherit biases from data that can unfairly disadvantage vulnerable groups. Fairness in information access systems (IAS) is typically assessed by comparing the distribution of groups in a ranking to a target distribution, such as the overall group distribution in the dataset. These fairness metrics depend on knowing the true group labels for each item. However, when groups are defined by demographic or sensitive attributes, these labels are often unknown, leading to a setting known as “fairness under unawareness.” To address this, group membership can be inferred using machine-learned classifiers, and group prevalence is estimated by counting the predicted labels. Unfortunately, such an estimation is known to be unreliable under dataset shift, compromising the accuracy of fairness evaluations. In this paper, we introduce a robust fairness estimator based on quantification that effectively handles multiple sensitive attributes beyond binary classifications. Our method outperforms existing baselines across various sensitive attributes and, to the best of our knowledge, is the first to establish a reliable protocol for measuring fairness under unawareness across multiple queries and groups.Source: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, vol. 85 (issue articolo 7)
DOI: 10.1613/jair.1.17675
Metrics:
See at:
CNR IRIS | www.jair.org | CNR IRIS
2026
Journal article
Open Access
Experimental and numerical investigation on the seismic effective mass of an industrial steel silo tested on shaking table
Mansour Sulyman, Pellegrini Daniele, Girardi Maria, Marra Matteo, Palermo Michele, Silvestri Stefano
Silos are classified as non-building structures whose overall structural response has been extensively studied in the last years. Nevertheless, their dynamic and seismic response still presents open issues, mainly regarding the seismic effective mass of the stored product, the estimation of the fundamental frequency of the filled silo system, and the dependency of the damping ratio on the input nature and magnitude. The main objective of this work is to provide an estimation of the effective mass, taking advantage of a wide experimental shaking table campaign carried out on a full-scale flat-bottom corrugated-wall steel silo filled with soft wheat. The paper initially presents the results of Experimental Modal Analysis for the identification of the vibration frequencies and the calibration of a numerical model considering a no-tension material for the ensiled content. The calibrated model is able to well reproduce the actual response of the filled silo measured during the shaking table tests for both sinusoidal and earthquake inputs and it is thus used to retrieve information about the seismic effective mass beyond the limits of the experimental work. The reconstruction of the global overturning moment at the silo base from the numerical model indicates an average mass participation of 91 % for an industrial silo with a common filling aspect ratio (H/D = 0.9). This highlights that the 80 % value of the effective mass prescribed by Eurocode 8 may reveal to be non-conservative for silos characterised by corrugated wall sections and aspect ratios close to or greater than 1.Source: STRUCTURES, vol. 84 (issue 111020)
DOI: 10.1016/j.istruc.2025.111020
Metrics:
Mansour Sulyman, Pellegrini Daniele, Girardi Maria, Marra Matteo, Palermo Michele, Silvestri Stefano
Silos are classified as non-building structures whose overall structural response has been extensively studied in the last years. Nevertheless, their dynamic and seismic response still presents open issues, mainly regarding the seismic effective mass of the stored product, the estimation of the fundamental frequency of the filled silo system, and the dependency of the damping ratio on the input nature and magnitude. The main objective of this work is to provide an estimation of the effective mass, taking advantage of a wide experimental shaking table campaign carried out on a full-scale flat-bottom corrugated-wall steel silo filled with soft wheat. The paper initially presents the results of Experimental Modal Analysis for the identification of the vibration frequencies and the calibration of a numerical model considering a no-tension material for the ensiled content. The calibrated model is able to well reproduce the actual response of the filled silo measured during the shaking table tests for both sinusoidal and earthquake inputs and it is thus used to retrieve information about the seismic effective mass beyond the limits of the experimental work. The reconstruction of the global overturning moment at the silo base from the numerical model indicates an average mass participation of 91 % for an industrial silo with a common filling aspect ratio (H/D = 0.9). This highlights that the 80 % value of the effective mass prescribed by Eurocode 8 may reveal to be non-conservative for silos characterised by corrugated wall sections and aspect ratios close to or greater than 1.Source: STRUCTURES, vol. 84 (issue 111020)
DOI: 10.1016/j.istruc.2025.111020
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2026
Other
Open Access
Retrieval-augmented generation for predicting cellular responses to gene perturbation
Di Francesco Andrea Giuseppe, Rubbi Andrea, Lio Pietro
Predicting how cells respond to genetic perturbations is fundamental to understanding gene function, disease mechanisms, and therapeutic development. While recent deep learning approaches have shown promise in modeling single-cell perturbation responses, they struggle to generalize across cell types and perturbation contexts due to limited contextual information during generation. We introduce PT-RAG (Perturbation-aware Two-stage Retrieval-Augmented Generation), a novel framework that extends Retrieval-Augmented Generation beyond traditional language-model applications to cellular biology. Unlike standard RAG systems designed for text retrieval with pre-trained LLMs, perturbation retrieval lacks established similarity metrics and requires learning what constitutes relevant context, making differentiable retrieval essential. PT-RAG addresses this through a two-stage pipeline: first, retrieving candidate perturbations K using GenePT embeddings, then adaptively refining the selection through Gumbel-Softmax discrDOI: 10.48550/arxiv.2603.07233
Metrics:
Di Francesco Andrea Giuseppe, Rubbi Andrea, Lio Pietro
Predicting how cells respond to genetic perturbations is fundamental to understanding gene function, disease mechanisms, and therapeutic development. While recent deep learning approaches have shown promise in modeling single-cell perturbation responses, they struggle to generalize across cell types and perturbation contexts due to limited contextual information during generation. We introduce PT-RAG (Perturbation-aware Two-stage Retrieval-Augmented Generation), a novel framework that extends Retrieval-Augmented Generation beyond traditional language-model applications to cellular biology. Unlike standard RAG systems designed for text retrieval with pre-trained LLMs, perturbation retrieval lacks established similarity metrics and requires learning what constitutes relevant context, making differentiable retrieval essential. PT-RAG addresses this through a two-stage pipeline: first, retrieving candidate perturbations K using GenePT embeddings, then adaptively refining the selection through Gumbel-Softmax discrDOI: 10.48550/arxiv.2603.07233
Metrics:
2026
Journal article
Open Access
Enhancing token boundary detection in disfluent speech
Srivastava Manu, Ferro Marcello, Pirrelli Vito, Coro Gianpaolo
This paper presents an open-source Automatic Speech Recognition (ASR) pipeline optimised for disfluent Italian read speech, designed to enhance both transcription accuracy and token boundary precision in low-resource settings. The study aims to address the difficulty that conventional ASR systems face in capturing the temporal irregularities of disfluent reading, which are crucial for psycholinguistic and clinical analyses of fluency. Building upon the WhisperX framework, the proposed system replaces the neural Voice Activity Detection module with an energy-based segmentation algorithm designed to preserve prosodic cues such as pauses and hesitations. A dual-alignment strategy integrates two complementary phoneme-level ASR models to correct onset–offset asymmetries, while a bias-compensation post-processing step mitigates systematic timing errors. Evaluation on the READLET (child read speech) and CLIPS (adult read speech) corpora shows consistent improvements over baseline systems, confirming enhanced robustness in boundary detection and transcription under disfluent conditions. The results demonstrate that the proposed architecture provides a general, language-independent framework for accurate alignment and disfluency-aware ASR. The approach can support downstream analyses of reading fluency and speech planning, contributing to both computational linguistics and clinical speech research.Source: INTELLIGENT SYSTEMS WITH APPLICATIONS, vol. 29
DOI: 10.1016/j.iswa.2025.200614
Project(s): READLET
Metrics:
Srivastava Manu, Ferro Marcello, Pirrelli Vito, Coro Gianpaolo
This paper presents an open-source Automatic Speech Recognition (ASR) pipeline optimised for disfluent Italian read speech, designed to enhance both transcription accuracy and token boundary precision in low-resource settings. The study aims to address the difficulty that conventional ASR systems face in capturing the temporal irregularities of disfluent reading, which are crucial for psycholinguistic and clinical analyses of fluency. Building upon the WhisperX framework, the proposed system replaces the neural Voice Activity Detection module with an energy-based segmentation algorithm designed to preserve prosodic cues such as pauses and hesitations. A dual-alignment strategy integrates two complementary phoneme-level ASR models to correct onset–offset asymmetries, while a bias-compensation post-processing step mitigates systematic timing errors. Evaluation on the READLET (child read speech) and CLIPS (adult read speech) corpora shows consistent improvements over baseline systems, confirming enhanced robustness in boundary detection and transcription under disfluent conditions. The results demonstrate that the proposed architecture provides a general, language-independent framework for accurate alignment and disfluency-aware ASR. The approach can support downstream analyses of reading fluency and speech planning, contributing to both computational linguistics and clinical speech research.Source: INTELLIGENT SYSTEMS WITH APPLICATIONS, vol. 29
DOI: 10.1016/j.iswa.2025.200614
Project(s): READLET
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2026
Journal article
Open Access
Navigation solutions for blind and visually impaired persons: a state-of-the-art survey
Belli Dimitri, Barsocchi Paolo, Lombardi Giuseppe, Coffrini Alberto, Furfari Francesco, Crivello Antonino
This survey provides a comprehensive overview of navigation solutions designed for Blind and Visually Impaired (BVI) individuals, analyzing 75 systematically selected papers and focusing on three critical aspects. First, it identifies a significant gap in the literature, highlighting the lack of seamless indoor/outdoor navigation systems that can support uninterrupted mobility for users in different environments. Secondly, it highlights the limited attention given to inclusivity factors such as usability, accessibility, user experience, and co-design when developing these solutions. Finally, it assesses the technological readiness of current navigation systems by evaluating their ability to effectively meet the needs of BVI persons in real-world scenarios. Furthermore, this study provides a complete open-access repository of the analyzed data to support reproducibility. The results of this study are intended to guide future research and development efforts toward creating more comprehensive, user-centered navigation solutions.Source: INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, pp. 1-30
DOI: 10.1080/10447318.2026.2643358
Metrics:
Belli Dimitri, Barsocchi Paolo, Lombardi Giuseppe, Coffrini Alberto, Furfari Francesco, Crivello Antonino
This survey provides a comprehensive overview of navigation solutions designed for Blind and Visually Impaired (BVI) individuals, analyzing 75 systematically selected papers and focusing on three critical aspects. First, it identifies a significant gap in the literature, highlighting the lack of seamless indoor/outdoor navigation systems that can support uninterrupted mobility for users in different environments. Secondly, it highlights the limited attention given to inclusivity factors such as usability, accessibility, user experience, and co-design when developing these solutions. Finally, it assesses the technological readiness of current navigation systems by evaluating their ability to effectively meet the needs of BVI persons in real-world scenarios. Furthermore, this study provides a complete open-access repository of the analyzed data to support reproducibility. The results of this study are intended to guide future research and development efforts toward creating more comprehensive, user-centered navigation solutions.Source: INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, pp. 1-30
DOI: 10.1080/10447318.2026.2643358
Metrics:
See at:
International Journal of Human-Computer Interaction | CNR IRIS | www.tandfonline.com | CNR IRIS
2026
Book
Open Access
Preface to Journeys between formal methods and the railway industry. Essays dedicated to Alessandro Fantechi on the occasion of his 70th birthday
Ter Beek Maurice Henri, Gnesi Stefania, Haxthausen Anne E., Semini Laura
This Festschrift contains 18 contributions by collaborators, colleagues and friends of Alessandro Fantechi to celebrate his 70th birthday.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. i-xvi
DOI: 10.1007/978-3-032-12484-5
Project(s): Sustainable Mobility National Research Center
Metrics:
Ter Beek Maurice Henri, Gnesi Stefania, Haxthausen Anne E., Semini Laura
This Festschrift contains 18 contributions by collaborators, colleagues and friends of Alessandro Fantechi to celebrate his 70th birthday.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. i-xvi
DOI: 10.1007/978-3-032-12484-5
Project(s): Sustainable Mobility National Research Center
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS
2026
Journal article
Open Access
Detecting fishing areas from navigation radar detector data
Coro Gianpaolo, Bove Pasquale, Ellenbroek Anton
Detecting global fishing activity is essential for sustainable ocean governance, yet systems based on vessel-transmitted information, such as Automatic Identification System (AIS) and Vessel Monitoring Systems, are limited by access issues, coverage gaps, and the inability to detect non-cooperative vessels. To overcome these issues, this paper presents Point-to-Fishing (P2F), an AI-driven workflow to detect fishing areas and estimate fishing hours from Navigation Radar Detector (NRD) data of satellite or terrestrial systems, complemented with currents and bathymetry data from Copernicus and GEBCO. P2F integrates analytical components based on statistical analysis, machine learning, and deep learning to conduct vessel behaviour analysis, spatial feature extraction (vessel abundance, recurrence, current-driven interpolation, and bathymetric suitability), and anomaly detection. The workflow operates effectively with or without vessel identifiers, enabling the detection of fishing areas in data-sparse or AIS-denied regions, even using one satellite only. P2F is validated on data covering the North Sea, the Western Norwegian Sea, and the North Atlantic. The validation cases utilise terrestrial and satellite NRD data alternately, with the Global Fishing Watch fishing effort distributions as a validation reference. P2F achieves a consistent ~75% agreement in relevant fishing area classification and intense-fishing area identification, and ~93% accuracy in total fishing effort estimation.Source: INTERNATIONAL JOURNAL OF DIGITAL EARTH, vol. 19 (issue 1)
DOI: 10.1080/17538947.2026.2617004
DOI: https://doi.org/10.1080/17538947.2026.2617004
Project(s): Accordo di Collaborazione N. 403036 del 25/10/2024 tra la Norwegian Defence Research Establishment (FFI) e il CNR-ISTI.
Metrics:
Coro Gianpaolo, Bove Pasquale, Ellenbroek Anton
Detecting global fishing activity is essential for sustainable ocean governance, yet systems based on vessel-transmitted information, such as Automatic Identification System (AIS) and Vessel Monitoring Systems, are limited by access issues, coverage gaps, and the inability to detect non-cooperative vessels. To overcome these issues, this paper presents Point-to-Fishing (P2F), an AI-driven workflow to detect fishing areas and estimate fishing hours from Navigation Radar Detector (NRD) data of satellite or terrestrial systems, complemented with currents and bathymetry data from Copernicus and GEBCO. P2F integrates analytical components based on statistical analysis, machine learning, and deep learning to conduct vessel behaviour analysis, spatial feature extraction (vessel abundance, recurrence, current-driven interpolation, and bathymetric suitability), and anomaly detection. The workflow operates effectively with or without vessel identifiers, enabling the detection of fishing areas in data-sparse or AIS-denied regions, even using one satellite only. P2F is validated on data covering the North Sea, the Western Norwegian Sea, and the North Atlantic. The validation cases utilise terrestrial and satellite NRD data alternately, with the Global Fishing Watch fishing effort distributions as a validation reference. P2F achieves a consistent ~75% agreement in relevant fishing area classification and intense-fishing area identification, and ~93% accuracy in total fishing effort estimation.Source: INTERNATIONAL JOURNAL OF DIGITAL EARTH, vol. 19 (issue 1)
DOI: 10.1080/17538947.2026.2617004
DOI: https://doi.org/10.1080/17538947.2026.2617004
Project(s): Accordo di Collaborazione N. 403036 del 25/10/2024 tra la Norwegian Defence Research Establishment (FFI) e il CNR-ISTI.
Metrics:
2026
Contribution to book
Open Access
Towards dynamic classification in domain modeling with Jjodel
Ter Beek Maurice Henri, Bucchiarone Antonio, Pierantonio Alfonso, Selic Bran
This work addresses the limitations of traditional object-oriented classification in representing evolving systems. Conventional classification enforces rigid hierarchies that hinder dynamic reclassification, concurrent viewpoints, and transient or overlapping states, thus limiting their usefulness for systems that exhibit change, context sensitivity, or adaptation. We propose a modeling notation that extends UML class diagrams with declarative behavioral dynamics directly embedded in structural information. Unlike approaches that separate structure and behavior, our notation unifies them in a framework suitable for both conceptual/domain modeling and evolutionary, context-aware dynamics. The notation is fully defined, including abstract syntax, diagrammatic syntax, semantics, and simulation, within the Jjodel platform, ensuring rigor and tool support. Its structural nature enables the declarative specification of dynamics without imperative constructs. Moreover, the notation can be connected to frameworks for formal verification and model checking, enabling analysis of dynamic properties. Applicability is illustrated through a context-aware scenario in which enriched structural models can be simulated, reasoned about, and eventually verified.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. 193-215
DOI: 10.1007/978-3-032-12484-5_11
Metrics:
Ter Beek Maurice Henri, Bucchiarone Antonio, Pierantonio Alfonso, Selic Bran
This work addresses the limitations of traditional object-oriented classification in representing evolving systems. Conventional classification enforces rigid hierarchies that hinder dynamic reclassification, concurrent viewpoints, and transient or overlapping states, thus limiting their usefulness for systems that exhibit change, context sensitivity, or adaptation. We propose a modeling notation that extends UML class diagrams with declarative behavioral dynamics directly embedded in structural information. Unlike approaches that separate structure and behavior, our notation unifies them in a framework suitable for both conceptual/domain modeling and evolutionary, context-aware dynamics. The notation is fully defined, including abstract syntax, diagrammatic syntax, semantics, and simulation, within the Jjodel platform, ensuring rigor and tool support. Its structural nature enables the declarative specification of dynamics without imperative constructs. Moreover, the notation can be connected to frameworks for formal verification and model checking, enabling analysis of dynamic properties. Applicability is illustrated through a context-aware scenario in which enriched structural models can be simulated, reasoned about, and eventually verified.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. 193-215
DOI: 10.1007/978-3-032-12484-5_11
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2026
Conference article
Open Access
A conversational assistant for geoscientists in virtual research environments
Peccerillo Biagio, Oliviero Alfredo, Procaccini Marco, Candela Leonardo, Frosini Luca, Mangiacrapa Francesco, Panichi Giancarlo, Assante Massimiliano, Pagano Pasquale
D4Science provides web-based Virtual Research Environments (VREs) that support FAIR, open, and reproducible science across multiple research domains, including Earth science. These environments integrate data access, computation, and collaboration services, offering powerful capabilities to researchers and enabling complex, data-intensive scientific activities within a shared digital infrastructure. This contribution introduces a conversational intelligent assistant integrated into D4Science VREs, designed to support Earth scientists in their research activity. The assistant provides a natural language interface that helps users interact with D4Science VREs' services, locate relevant datasets and research items, obtain guidance on common tasks, and support exploratory and operational activities within the VRE. The assistant is designed with a modular approach. The user interacts with a coordinator agent that orchestrates a multi-agent system, where specialized AI agents collaborate to perform a variety of tasks. This architecture allows the assistant to handle heterogeneous requests and to support users across different phases of their research activities, while also facilitating maintenance and extensibility. The conversational agent adopts a Retrieval-Augmented Generation (RAG) approach that leverages the knowledge already captured by the VRE through its regular use by research communities. In fact, as VREs naturally accumulate updated knowledge created and curated by researchers over time, the assistant's knowledge base evolves incorporating new information. This way, the assistant can ground its responses in domain-specific and up-to-date information, effectively acting as a domain-aware expert embedded within the research environment. By serving as an accessible entry point to the VRE, the assistant complements existing interfaces without altering established workflows. The presentation discusses the motivation, design choices, and integration strategy. It also presents various concrete use cases relevant to Earth scientists, demonstrating how the conversational assistant can be effectively employed to support their research activity.DOI: 10.5194/egusphere-egu26-13138
Metrics:
Peccerillo Biagio, Oliviero Alfredo, Procaccini Marco, Candela Leonardo, Frosini Luca, Mangiacrapa Francesco, Panichi Giancarlo, Assante Massimiliano, Pagano Pasquale
D4Science provides web-based Virtual Research Environments (VREs) that support FAIR, open, and reproducible science across multiple research domains, including Earth science. These environments integrate data access, computation, and collaboration services, offering powerful capabilities to researchers and enabling complex, data-intensive scientific activities within a shared digital infrastructure. This contribution introduces a conversational intelligent assistant integrated into D4Science VREs, designed to support Earth scientists in their research activity. The assistant provides a natural language interface that helps users interact with D4Science VREs' services, locate relevant datasets and research items, obtain guidance on common tasks, and support exploratory and operational activities within the VRE. The assistant is designed with a modular approach. The user interacts with a coordinator agent that orchestrates a multi-agent system, where specialized AI agents collaborate to perform a variety of tasks. This architecture allows the assistant to handle heterogeneous requests and to support users across different phases of their research activities, while also facilitating maintenance and extensibility. The conversational agent adopts a Retrieval-Augmented Generation (RAG) approach that leverages the knowledge already captured by the VRE through its regular use by research communities. In fact, as VREs naturally accumulate updated knowledge created and curated by researchers over time, the assistant's knowledge base evolves incorporating new information. This way, the assistant can ground its responses in domain-specific and up-to-date information, effectively acting as a domain-aware expert embedded within the research environment. By serving as an accessible entry point to the VRE, the assistant complements existing interfaces without altering established workflows. The presentation discusses the motivation, design choices, and integration strategy. It also presents various concrete use cases relevant to Earth scientists, demonstrating how the conversational assistant can be effectively employed to support their research activity.DOI: 10.5194/egusphere-egu26-13138
Metrics:
See at:
CNR IRIS | www.egu26.eu | CNR IRIS
2026
Journal article
Open Access
The OpenAIRE Graph: enabling open research intelligence using open data
Manghi Paolo
The OpenAIRE Graph provides an open, community-governed data infrastructure for research intelligence, enabling transparent and auditable use of scholarly data beyond proprietary systems. The global research ecosystem is calling for a structural transition from proprietary, opaque systems for research intelligence to open, community-governed infrastructures. At the centre of this shift is the need to reclaim how scholarly data is collected, connected, and used to inform research evaluation and policy. The OpenAIRE Graph addresses this challenge by providing a large-scale, openly accessible scholarly knowledge graph that treats publications, data, and software as first-class research outputs. As a community-governed infrastructure, it establishes a transparent and auditable foundation for Open Research Intelligence.Source: ERCIM NEWS, vol. 144, pp. 8-9
Manghi Paolo
The OpenAIRE Graph provides an open, community-governed data infrastructure for research intelligence, enabling transparent and auditable use of scholarly data beyond proprietary systems. The global research ecosystem is calling for a structural transition from proprietary, opaque systems for research intelligence to open, community-governed infrastructures. At the centre of this shift is the need to reclaim how scholarly data is collected, connected, and used to inform research evaluation and policy. The OpenAIRE Graph addresses this challenge by providing a large-scale, openly accessible scholarly knowledge graph that treats publications, data, and software as first-class research outputs. As a community-governed infrastructure, it establishes a transparent and auditable foundation for Open Research Intelligence.Source: ERCIM NEWS, vol. 144, pp. 8-9
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ercim-news.ercim.eu | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
A Survey on SAR ship classification using deep learning
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Emanuele Salerno
Deep learning (DL) has become a central approach for ship classification using synthetic aperture radar (SAR) imagery. This survey reviews 74 representative studies selected from 187 publications, categorizing them into a taxonomy with four main dimensions: (i) DL architectures, (ii) datasets, (iii) image augmentation, and (iv) learning techniques. We analyze how approaches such as handcrafted feature integration, data augmentation, fine-tuning, and transfer learning influence classification performance, and summarize the use of public benchmarks including OpenSARShip and FUSARShip. This survey highlights key challenges: limited data availability, class imbalance, lack of standardized metrics, and limited interpretability of DL models. Future research directions include the development of SAR-specific DL architectures, advanced augmentation and generative approaches, integration of handcrafted and deep features, interpretable DL, and stronger interdisciplinary collaboration. By addressing these challenges, DL-based SAR ship classification can achieve greater robustness, accuracy, and transparency, ultimately strengthening maritime surveillance and operational monitoring.Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
DOI: 10.1109/jstars.2026.3695704
Metrics:
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Emanuele Salerno
Deep learning (DL) has become a central approach for ship classification using synthetic aperture radar (SAR) imagery. This survey reviews 74 representative studies selected from 187 publications, categorizing them into a taxonomy with four main dimensions: (i) DL architectures, (ii) datasets, (iii) image augmentation, and (iv) learning techniques. We analyze how approaches such as handcrafted feature integration, data augmentation, fine-tuning, and transfer learning influence classification performance, and summarize the use of public benchmarks including OpenSARShip and FUSARShip. This survey highlights key challenges: limited data availability, class imbalance, lack of standardized metrics, and limited interpretability of DL models. Future research directions include the development of SAR-specific DL architectures, advanced augmentation and generative approaches, integration of handcrafted and deep features, interpretable DL, and stronger interdisciplinary collaboration. By addressing these challenges, DL-based SAR ship classification can achieve greater robustness, accuracy, and transparency, ultimately strengthening maritime surveillance and operational monitoring.Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
DOI: 10.1109/jstars.2026.3695704
Metrics:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2026
Book
Open Access
Design manual for accessibility and the inclusive use of cultural heritage: from human functioning to the functioning of cultural places
Bernacchia Monica, Biocca Luigi, Buzzi Marina, Capirci Olga, Caponera Federica, Carella Giuseppina, Ceccarani Patrizia, Cetorelli Gabriella, Contardi Anna, Della Fina Valentina, Di Renzo Alessio, Filetici Maria Grazia, Galesi Giulio, Genta Chiara, Gervasio Carmine Fernando, Grassini Aldo, Guido Manuel Roberto, Leporini Barbara, Luongo Mariajosè, Maglorio Massimo, Marino Carmen, Mezzelani Alessandra, Montuschi Carla, Pagano Alfonsina, Papi Luca, Pennacchi Barbara, Pietroni Eva, Ricci Enrico, Rossini Mauro, Russo Francesco Paolo, Schiavone Elisabetta, Schivo Flavia, Scianna Andrea, Trasatti Annalisa, Zanut Stefano
The English-language version of the Manual, presented in an expanded edition in March 2026, represents an update of the volume with two appendices that are part of the institutional series of Policy Briefs of the Department of Human and Social Sciences, Cultural Heritage (DSU) of the National Research Council (CNR). This new edition, in addition to gathering the salient aspects of "universal design," starting from the concept of the functioning of individuals to the functioning of cultural places, presents additional current aspects through documents that summarize the results of research on relevant topics and their implications for policy and social debate. In the first addendum, the choice was to explore the issues of appropriate, respectful, and correct language regarding accessibility based on Legislative Decree 62/2024, centered on a "Person First" vision, as enshrined by the World Health Organization (2001) and the Convention on the Rights of Persons with Disabilities (2006). The second document proposes an integrated approach to accessible trails, understood as landscape experiences to be enjoyed independently and safely, capable of meeting the needs of each individual
Bernacchia Monica, Biocca Luigi, Buzzi Marina, Capirci Olga, Caponera Federica, Carella Giuseppina, Ceccarani Patrizia, Cetorelli Gabriella, Contardi Anna, Della Fina Valentina, Di Renzo Alessio, Filetici Maria Grazia, Galesi Giulio, Genta Chiara, Gervasio Carmine Fernando, Grassini Aldo, Guido Manuel Roberto, Leporini Barbara, Luongo Mariajosè, Maglorio Massimo, Marino Carmen, Mezzelani Alessandra, Montuschi Carla, Pagano Alfonsina, Papi Luca, Pennacchi Barbara, Pietroni Eva, Ricci Enrico, Rossini Mauro, Russo Francesco Paolo, Schiavone Elisabetta, Schivo Flavia, Scianna Andrea, Trasatti Annalisa, Zanut Stefano
The English-language version of the Manual, presented in an expanded edition in March 2026, represents an update of the volume with two appendices that are part of the institutional series of Policy Briefs of the Department of Human and Social Sciences, Cultural Heritage (DSU) of the National Research Council (CNR). This new edition, in addition to gathering the salient aspects of "universal design," starting from the concept of the functioning of individuals to the functioning of cultural places, presents additional current aspects through documents that summarize the results of research on relevant topics and their implications for policy and social debate. In the first addendum, the choice was to explore the issues of appropriate, respectful, and correct language regarding accessibility based on Legislative Decree 62/2024, centered on a "Person First" vision, as enshrined by the World Health Organization (2001) and the Convention on the Rights of Persons with Disabilities (2006). The second document proposes an integrated approach to accessible trails, understood as landscape experiences to be enjoyed independently and safely, capable of meeting the needs of each individual
See at:
CNR IRIS | www.dsu.cnr.it | CNR IRIS
2026
Journal article
Open Access
Charting the landscape of Italian Diamond Open Access publishing
Angioni Simone, Baglioni Miriam, Bardi Alessia, Manghi Paolo, Mannocci Andrea, Pavone Gina
Diamond Open Access (DOA) is a non-commercial model of scholarly publishing that removes financial barriers for authors and readers. While international studies have outlined the global uptake of DOA, this paper investigates the presence and characteristics of the DOA landscape in Italy. We conducted a quantitative analysis on the 168 Italian journals classified as Diamond by EZB, and we studied their publishing volume, disciplinary distribution and citation impact by integrating information from the following open resources: DOAJ, OpenAIRE, ROAD, SCImago Journal Rank, and the ANVUR classification used in the Italian Research Assessment Framework (VQR). Key findings include the significant growth of DOA journals, particularly in the social sciences and humanities, as well as the high level of international citations, indicating strong global relevance. The study also highlights challenges such as the need for better indexing and comprehensive data to fully capture the DOA landscape.Source: QUANTITATIVE SCIENCE STUDIES
DOI: 10.1162/qss.a.466
Metrics:
Angioni Simone, Baglioni Miriam, Bardi Alessia, Manghi Paolo, Mannocci Andrea, Pavone Gina
Diamond Open Access (DOA) is a non-commercial model of scholarly publishing that removes financial barriers for authors and readers. While international studies have outlined the global uptake of DOA, this paper investigates the presence and characteristics of the DOA landscape in Italy. We conducted a quantitative analysis on the 168 Italian journals classified as Diamond by EZB, and we studied their publishing volume, disciplinary distribution and citation impact by integrating information from the following open resources: DOAJ, OpenAIRE, ROAD, SCImago Journal Rank, and the ANVUR classification used in the Italian Research Assessment Framework (VQR). Key findings include the significant growth of DOA journals, particularly in the social sciences and humanities, as well as the high level of international citations, indicating strong global relevance. The study also highlights challenges such as the need for better indexing and comprehensive data to fully capture the DOA landscape.Source: QUANTITATIVE SCIENCE STUDIES
DOI: 10.1162/qss.a.466
Metrics:
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direct.mit.edu | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Biological reservoir computing: harnessing living neurons for AI
Ciampi Luca, Iannello Ludovico, Amato Giuseppe, Cremisi Federico, Tonelli Fabrizio
This work demonstrates that living neuronal networks can serve as effective computational substrates within a reservoir computing framework. By leveraging the intrinsic dynamics of biological systems, we open a promising pathway towards neuromorphic architectures that combine energy efficiency, adaptability, and biological plausibility. Our experiments on static pattern recognition tasks confirm the feasibility of this approach and highlight its potential for future applications in AI and neuroscience. Notably, our preliminary study on this topic, presented at the ICCV 2025 Workshop “2nd Workshop on Human-inspired Computer Vision”, received a Best Paper Award, and subsequent work was published at ICONIP 2025, underscoring the originality and scientific relevance of this research. Moving forward, we aim to extend BRC to more complex tasks and explore learning mechanisms within the biological reservoir, paving the way for adaptive bio-hybrid system.Source: ERCIM NEWS, vol. 143
Ciampi Luca, Iannello Ludovico, Amato Giuseppe, Cremisi Federico, Tonelli Fabrizio
This work demonstrates that living neuronal networks can serve as effective computational substrates within a reservoir computing framework. By leveraging the intrinsic dynamics of biological systems, we open a promising pathway towards neuromorphic architectures that combine energy efficiency, adaptability, and biological plausibility. Our experiments on static pattern recognition tasks confirm the feasibility of this approach and highlight its potential for future applications in AI and neuroscience. Notably, our preliminary study on this topic, presented at the ICCV 2025 Workshop “2nd Workshop on Human-inspired Computer Vision”, received a Best Paper Award, and subsequent work was published at ICONIP 2025, underscoring the originality and scientific relevance of this research. Moving forward, we aim to extend BRC to more complex tasks and explore learning mechanisms within the biological reservoir, paving the way for adaptive bio-hybrid system.Source: ERCIM NEWS, vol. 143
See at:
ercim-news.ercim.eu | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Enhancing randomized recurrent neural networks with explainable attribution methods
Spinnato Francesco, Ceni Andrea, Cossu Andrea, Guidotti Riccardo, Gallicchio Claudio, Bacciu Davide
Recurrent Neural Networks (RNNs) are well-suited for temporal data modeling but remain limited by their high training computational cost. As a lightweight alternative, randomized RNNs mitigate this issue by employing a fixed, randomly initialized recurrent layer combined with a simple, trainable output layer. To classify a given input sequence, randomized RNNs usually rely on the final reservoir state, which can be suboptimal when relevant temporal information is sparse or masked by noise. In this work, we investigate how explainable attribution methods can improve the performance of randomized RNNs in classification tasks. In particular, we adopt gradient-based attribution explainability techniques to weigh reservoir states according to their relevance to the final prediction. We theoretically justify the effectiveness of our approach through linear stability analysis, offering geometric intuition via an estimation of the variability of the recurrent dynamics by means of explainability techniques. Our experimental evaluation spans 30 binary and 10 multiclass time series classification tasks, comparing several randomized recurrent models. Results show that explainability-guided weighting can improve classification performance in noisy scenarios.Source: NEUROCOMPUTING, vol. 666
DOI: 10.1016/j.neucom.2025.132318
Metrics:
Spinnato Francesco, Ceni Andrea, Cossu Andrea, Guidotti Riccardo, Gallicchio Claudio, Bacciu Davide
Recurrent Neural Networks (RNNs) are well-suited for temporal data modeling but remain limited by their high training computational cost. As a lightweight alternative, randomized RNNs mitigate this issue by employing a fixed, randomly initialized recurrent layer combined with a simple, trainable output layer. To classify a given input sequence, randomized RNNs usually rely on the final reservoir state, which can be suboptimal when relevant temporal information is sparse or masked by noise. In this work, we investigate how explainable attribution methods can improve the performance of randomized RNNs in classification tasks. In particular, we adopt gradient-based attribution explainability techniques to weigh reservoir states according to their relevance to the final prediction. We theoretically justify the effectiveness of our approach through linear stability analysis, offering geometric intuition via an estimation of the variability of the recurrent dynamics by means of explainability techniques. Our experimental evaluation spans 30 binary and 10 multiclass time series classification tasks, comparing several randomized recurrent models. Results show that explainability-guided weighting can improve classification performance in noisy scenarios.Source: NEUROCOMPUTING, vol. 666
DOI: 10.1016/j.neucom.2025.132318
Metrics:
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CNR IRIS | www.sciencedirect.com | CNR IRIS
2026
Conference article
Open Access
ViSketch-GPT: collaborative multi-scale feature extraction for hand-drawn sketch retrieval
Federico Giulio, Carrara Fabio, Gennaro Claudio, Di Benedetto Marco
Understanding the nature of hand-drawn sketches is challenging due to the wide variation in their creation. Federico et al. [10] demonstrated that recognizing complex structural patterns enhances both sketch recognition and generation. Building on this foundation, we explore how the extracted features can also be leveraged for hand-drawn sketch retrieval. In this work, we extend ViSketch-GPT, a multi-scale context extraction model originally designed for classification and generation, to the task of retrieval. The model’s ability to capture intricate details at multiple scales allows it to learn highly discriminative representations, making it well-suited for retrieval applications. Through extensive experiments on the QuickDraw and TU-Berlin datasets, we show that ViSketch-GPT surpasses state-of-the-art methods in sketch retrieval, achieving substantial improvements across multiple evaluation metrics. Our results show that the extracted feature representations, originally designed for classification and generation, are also highly effective for retrieval tasks. This highlights ViSketch-GPT as a versatile and high-powerful framework for various applications in computer vision and sketch analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16134, pp. 3-13. Reykjavik, Iceland, 1–3 october 2025
DOI: 10.1007/978-3-032-06069-3_1
Project(s): Italian Strengthening of ESFRI RI RESILIENCE, SUN
Metrics:
Federico Giulio, Carrara Fabio, Gennaro Claudio, Di Benedetto Marco
Understanding the nature of hand-drawn sketches is challenging due to the wide variation in their creation. Federico et al. [10] demonstrated that recognizing complex structural patterns enhances both sketch recognition and generation. Building on this foundation, we explore how the extracted features can also be leveraged for hand-drawn sketch retrieval. In this work, we extend ViSketch-GPT, a multi-scale context extraction model originally designed for classification and generation, to the task of retrieval. The model’s ability to capture intricate details at multiple scales allows it to learn highly discriminative representations, making it well-suited for retrieval applications. Through extensive experiments on the QuickDraw and TU-Berlin datasets, we show that ViSketch-GPT surpasses state-of-the-art methods in sketch retrieval, achieving substantial improvements across multiple evaluation metrics. Our results show that the extracted feature representations, originally designed for classification and generation, are also highly effective for retrieval tasks. This highlights ViSketch-GPT as a versatile and high-powerful framework for various applications in computer vision and sketch analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16134, pp. 3-13. Reykjavik, Iceland, 1–3 october 2025
DOI: 10.1007/978-3-032-06069-3_1
Project(s): Italian Strengthening of ESFRI RI RESILIENCE, SUN
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS