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2020
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AIMH lab website
Bolettieri P
AIMH (Artificial Intelligence for Media and Humanities) laboratory Web site.
Bolettieri P
AIMH (Artificial Intelligence for Media and Humanities) laboratory Web site.
See at:
aimh.isti.cnr.it 
| CNR IRIS
2022
Conference article
Open Access
AIMH Lab for Cybersecurity
Vairo C, Coccomini Da, Falchi F, Gennaro C, Massoli Fv, Messina N, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Cy-bersecurity. We discuss about our active research fields, their applications and challenges. We focus on face recognition and detection of adversarial examples and deep fakes. We also present our activities on the detection of persuasion techniques combining image and text analysis.
Vairo C, Coccomini Da, Falchi F, Gennaro C, Massoli Fv, Messina N, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Cy-bersecurity. We discuss about our active research fields, their applications and challenges. We focus on face recognition and detection of adversarial examples and deep fakes. We also present our activities on the detection of persuasion techniques combining image and text analysis.
See at:
CNR IRIS 
| ISTI Repository | www.ital-ia2022.it | CNR IRIS
2020
Other
Open Access
AIMH research activities 2020
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.DOI: https://doi.org/10.32079/isti-ar-2020/001
DOI: 10.32079/isti-ar-2020/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.DOI: https://doi.org/10.32079/isti-ar-2020/001
DOI: 10.32079/isti-ar-2020/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
AIMH Lab for Healthcare and Wellbeing
Di Benedetto M, Carrara F, Ciampi L, Falchi F, Gennaro C, Amato G
In this work we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Healthcare and Wellbeing. By exploiting the advances of recent machine learning methods and the compute power of desktop and mobile platforms, we will show how artificial intelligence tools can be used to improve healthcare systems in various parts of disease treatment. In particular we will see how deep neural networks can assist doctors from diagnosis (e.g., cell counting, pupil and brain analysis) to communication to patients with Augmented Reality .
Di Benedetto M, Carrara F, Ciampi L, Falchi F, Gennaro C, Amato G
In this work we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Healthcare and Wellbeing. By exploiting the advances of recent machine learning methods and the compute power of desktop and mobile platforms, we will show how artificial intelligence tools can be used to improve healthcare systems in various parts of disease treatment. In particular we will see how deep neural networks can assist doctors from diagnosis (e.g., cell counting, pupil and brain analysis) to communication to patients with Augmented Reality .
See at:
CNR IRIS | ISTI Repository | www.ital-ia2022.it | CNR IRIS
2022
Conference article
Open Access
AIMH Lab: Smart Cameras for Public Administration
Ciampi L, Cafarelli D, Carrara F, Di Benedetto M, Falchi F, Gennaro C, Massoli Fv, Messina N, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Public Administration. In particular, we present some AI-based public services serving the citizens that help achieve common goals beneficial to the society, putting humans at the epicenter. Through the automatic analysis of images gathered from city cameras, we provide AI applications ranging from smart parking and smart mobility to human activity monitoring.
Ciampi L, Cafarelli D, Carrara F, Di Benedetto M, Falchi F, Gennaro C, Massoli Fv, Messina N, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Public Administration. In particular, we present some AI-based public services serving the citizens that help achieve common goals beneficial to the society, putting humans at the epicenter. Through the automatic analysis of images gathered from city cameras, we provide AI applications ranging from smart parking and smart mobility to human activity monitoring.
See at:
CNR IRIS | ISTI Repository | www.ital-ia2022.it | CNR IRIS
2023
Conference article
Open Access
AIMH Lab approaches for deepfake detection
Coccomini Da, Caldelli R, Esuli A, Falchi F, Gennaro C, Messina N, Amato G
The creation of highly realistic media known as deepfakes has been facilitated by the rapid development of artificial intelligence technologies, including deep learning algorithms, in recent years. Concerns about the increasing ease of creation and credibility of deepfakes have then been growing more and more, prompting researchers around the world to concentrate their efforts on the field of deepfake detection. In this same context, researchers at ISTI-CNR's AIMH Lab have conducted numerous researches, investigations and proposals to make their own contribution to combating this worrying phenomenon. In this paper, we present the main work carried out in the field of deepfake detection and synthetic content detection, conducted by our researchers and in collaboration with external organizations.Source: CEUR WORKSHOP PROCEEDINGS, pp. 432-436. Pisa, Italy, 29-31/05/2023
Project(s): AI4Media
Coccomini Da, Caldelli R, Esuli A, Falchi F, Gennaro C, Messina N, Amato G
The creation of highly realistic media known as deepfakes has been facilitated by the rapid development of artificial intelligence technologies, including deep learning algorithms, in recent years. Concerns about the increasing ease of creation and credibility of deepfakes have then been growing more and more, prompting researchers around the world to concentrate their efforts on the field of deepfake detection. In this same context, researchers at ISTI-CNR's AIMH Lab have conducted numerous researches, investigations and proposals to make their own contribution to combating this worrying phenomenon. In this paper, we present the main work carried out in the field of deepfake detection and synthetic content detection, conducted by our researchers and in collaboration with external organizations.Source: CEUR WORKSHOP PROCEEDINGS, pp. 432-436. Pisa, Italy, 29-31/05/2023
Project(s): AI4Media
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
AIMH Lab for a susteinable bio-inspired AI
Lagani G, Falchi F, Gennaro C, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Sustainable AI. In particular, we discuss the problem of the environmental impact of AI research, and we discuss a research direction aimed at creating effective intelligent systems with a reduced ecological footprint. The proposal is based on bio-inspired learning, which takes inspiration from the biological processes underlying human intelligence in order to produce more energy-efficient AI systems. In fact, biological brains are able to perform complex computations, with a power consumption which is orders of magnitude smaller than that of traditional AI. The ability to control and replicate these biological processes reveals promising results towards the realization of sustainable AISource: CEUR WORKSHOP PROCEEDINGS, pp. 575-584. Pisa, Italy, 29-30/05/2023
Lagani G, Falchi F, Gennaro C, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Sustainable AI. In particular, we discuss the problem of the environmental impact of AI research, and we discuss a research direction aimed at creating effective intelligent systems with a reduced ecological footprint. The proposal is based on bio-inspired learning, which takes inspiration from the biological processes underlying human intelligence in order to produce more energy-efficient AI systems. In fact, biological brains are able to perform complex computations, with a power consumption which is orders of magnitude smaller than that of traditional AI. The ability to control and replicate these biological processes reveals promising results towards the realization of sustainable AISource: CEUR WORKSHOP PROCEEDINGS, pp. 575-584. Pisa, Italy, 29-30/05/2023
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2021
Conference article
Open Access
AIMH at SemEval-2021 - Task 6: multimodal classification using an ensemble of transformer models
Messina N, Falchi F, Gennaro C, Amato G
This paper describes the system used by the AIMH Team to approach the SemEval Task 6. We propose an approach that relies on an architecture based on the transformer model to process multimodal content (text and images) in memes. Our architecture, called DVTT (Double Visual Textual Transformer), approaches Subtasks 1 and 3 of Task 6 as multi-label classification problems, where the text and/or images of the meme are processed, and the probabilities of the presence of each possible persuasion technique are returned as a result. DVTT uses two complete networks of transformers that work on text and images that are mutually conditioned. One of the two modalities acts as the main one and the second one intervenes to enrich the first one, thus obtaining two distinct ways of operation. The two transformers outputs are merged by averaging the inferred probabilities for each possible label, and the overall network is trained end-to-end with a binary cross-entropy loss.Source: PROCEEDINGS OF THE CONFERENCE - ASSOCIATION FOR COMPUTATIONAL LINGUISTICS. MEETING, pp. 1020-1026. Bangkok, Thailand, 5-6/08/2021
DOI: 10.18653/v1/2021.semeval-1.140
Project(s): AI4EU, AI4Media
Metrics:
Messina N, Falchi F, Gennaro C, Amato G
This paper describes the system used by the AIMH Team to approach the SemEval Task 6. We propose an approach that relies on an architecture based on the transformer model to process multimodal content (text and images) in memes. Our architecture, called DVTT (Double Visual Textual Transformer), approaches Subtasks 1 and 3 of Task 6 as multi-label classification problems, where the text and/or images of the meme are processed, and the probabilities of the presence of each possible persuasion technique are returned as a result. DVTT uses two complete networks of transformers that work on text and images that are mutually conditioned. One of the two modalities acts as the main one and the second one intervenes to enrich the first one, thus obtaining two distinct ways of operation. The two transformers outputs are merged by averaging the inferred probabilities for each possible label, and the overall network is trained end-to-end with a binary cross-entropy loss.Source: PROCEEDINGS OF THE CONFERENCE - ASSOCIATION FOR COMPUTATIONAL LINGUISTICS. MEETING, pp. 1020-1026. Bangkok, Thailand, 5-6/08/2021
DOI: 10.18653/v1/2021.semeval-1.140
Project(s): AI4EU
, AI4Media Metrics:
See at:
aclanthology.org | CNR IRIS | ISTI Repository | ISTI Repository | CNR IRIS | CNR IRIS
2021
Other
Open Access
AIMH research activities 2021
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.DOI: https://doi.org/10.32079/isti-ar-2021/003
DOI: 10.32079/isti-ar-2021/003
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.DOI: https://doi.org/10.32079/isti-ar-2021/003
DOI: 10.32079/isti-ar-2021/003
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
AIMH Lab for Trustworthy AI
Messina N, Carrara F, Coccomini D, Falchi F, Gennaro C, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Trustworthy AI. Artificial Intelligence is becoming more and more pervasive in our society, controlling recommendation systems in social platforms as well as safety-critical systems like autonomous vehicles. In order to be safe and trustworthy, these systems require to be easily interpretable and transparent. On the other hand, it is important to spot fake examples forged by malicious AI generative models to fool humans (through fake news or deep-fakes) or other AI systems (through adversarial examples). This is required to enforce an ethical use of these powerful new technologies. Driven by these concerns, this paper presents three crucial research directions contributing to the study and the development of techniques for reliable, resilient, and explainable deep learning methods. Namely, we report the laboratory activities on the detection of adversarial examples, the use of attentive models as a way towards explainable deep learning, and the detection of deepfakes in social platforms.
Messina N, Carrara F, Coccomini D, Falchi F, Gennaro C, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Trustworthy AI. Artificial Intelligence is becoming more and more pervasive in our society, controlling recommendation systems in social platforms as well as safety-critical systems like autonomous vehicles. In order to be safe and trustworthy, these systems require to be easily interpretable and transparent. On the other hand, it is important to spot fake examples forged by malicious AI generative models to fool humans (through fake news or deep-fakes) or other AI systems (through adversarial examples). This is required to enforce an ethical use of these powerful new technologies. Driven by these concerns, this paper presents three crucial research directions contributing to the study and the development of techniques for reliable, resilient, and explainable deep learning methods. Namely, we report the laboratory activities on the detection of adversarial examples, the use of attentive models as a way towards explainable deep learning, and the detection of deepfakes in social platforms.
See at:
CNR IRIS | ISTI Repository | www.ital-ia2022.it | CNR IRIS
2022
Conference article
Open Access
AIMH Lab for the Industry
Carrara F, Ciampi L, Di Benedetto M, Falchi F, Gennaro C, Massoli Fv, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Industry. The massive digitalization affecting all the stages of product design, production, and control calls for data-driven algorithms helping in the coordination of humans, machines, and digital resources in Industry 4.0. In this context, we developed AI-based Computer-Vision technologies of general interest in the emergent digital paradigm of the fourth industrial revolution, fo-cusing on anomaly detection and object counting for computer-assisted testing and quality control. Moreover, in the automotive sector, we explore the use of virtual worlds to develop AI systems in otherwise practically unfeasible scenarios, showing an application for accident avoidance in self-driving car AI agents.
Carrara F, Ciampi L, Di Benedetto M, Falchi F, Gennaro C, Massoli Fv, Amato G
In this short paper, we report the activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR related to Industry. The massive digitalization affecting all the stages of product design, production, and control calls for data-driven algorithms helping in the coordination of humans, machines, and digital resources in Industry 4.0. In this context, we developed AI-based Computer-Vision technologies of general interest in the emergent digital paradigm of the fourth industrial revolution, fo-cusing on anomaly detection and object counting for computer-assisted testing and quality control. Moreover, in the automotive sector, we explore the use of virtual worlds to develop AI systems in otherwise practically unfeasible scenarios, showing an application for accident avoidance in self-driving car AI agents.
See at:
CNR IRIS | ISTI Repository | www.ital-ia2022.it | CNR IRIS
2022
Other
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: https://doi.org/10.32079/isti-ar-2022/002
DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: https://doi.org/10.32079/isti-ar-2022/002
DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
AIMH Lab 2022 activities for Vision
Ciampi L, Amato G, Bolettieri P, Carrara F, Di Benedetto M, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
The explosion of smartphones and cameras has led to a vast production of multimedia data. Consequently, Artificial Intelligence-based tools for automatically understanding and exploring these data have recently gained much attention. In this short paper, we report some activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR, tackling some challenges in the field of Computer Vision for the automatic understanding of visual data and for novel interactive tools aimed at multimedia data exploration. Specifically, we provide innovative solutions based on Deep Learning techniques carrying out typical vision tasks such as object detection and visual counting, with particular emphasis on scenarios characterized by scarcity of labeled data needed for the supervised training and on environments with limited power resources imposing miniaturization of the models. Furthermore, we describe VISIONE, our large-scale video search system designed to search extensive multimedia databases in an interactive and user-friendly manner.Source: CEUR WORKSHOP PROCEEDINGS, pp. 538-543. Pisa, Italy, 29-31/05/2023
Project(s): AI4Media, Future Artificial Intelligence Research
Ciampi L, Amato G, Bolettieri P, Carrara F, Di Benedetto M, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
The explosion of smartphones and cameras has led to a vast production of multimedia data. Consequently, Artificial Intelligence-based tools for automatically understanding and exploring these data have recently gained much attention. In this short paper, we report some activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR, tackling some challenges in the field of Computer Vision for the automatic understanding of visual data and for novel interactive tools aimed at multimedia data exploration. Specifically, we provide innovative solutions based on Deep Learning techniques carrying out typical vision tasks such as object detection and visual counting, with particular emphasis on scenarios characterized by scarcity of labeled data needed for the supervised training and on environments with limited power resources imposing miniaturization of the models. Furthermore, we describe VISIONE, our large-scale video search system designed to search extensive multimedia databases in an interactive and user-friendly manner.Source: CEUR WORKSHOP PROCEEDINGS, pp. 538-543. Pisa, Italy, 29-31/05/2023
Project(s): AI4Media
, Future Artificial Intelligence Research
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Other
Open Access
AIMH Research Activities 2023
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: https://doi.org/10.32079/isti-ar-2023/001
DOI: 10.32079/isti-ar-2023/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: https://doi.org/10.32079/isti-ar-2023/001
DOI: 10.32079/isti-ar-2023/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2024
Other
Open Access
AIMH Research Activities 2024
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Cassese M., Ciampi L., Coccomini D. A., Concordia C., Connor R., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Iannello L., Negi K., Lagani G., Lenzi E., Leocata M., Malvaldi M., Meghini C., Messina N., Moreo Fernandez A., Nardi A., Pacini G., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Scotti F., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L., Volpi L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is committed to advancing the field of Artificial Intelligence, with a special emphasis on its applications in digital media and the humanities. The lab aims to improve AI technologies, particularly in areas such as deep learning, text analysis, computer vision, multimedia information retrieval, content analysis, recognition, and retrieval. This report summarizes the laboratory’s achievements and activities over the course of 2024.DOI: 10.32079/isti-ar-2024/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Cassese M., Ciampi L., Coccomini D. A., Concordia C., Connor R., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Iannello L., Negi K., Lagani G., Lenzi E., Leocata M., Malvaldi M., Meghini C., Messina N., Moreo Fernandez A., Nardi A., Pacini G., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Scotti F., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L., Volpi L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is committed to advancing the field of Artificial Intelligence, with a special emphasis on its applications in digital media and the humanities. The lab aims to improve AI technologies, particularly in areas such as deep learning, text analysis, computer vision, multimedia information retrieval, content analysis, recognition, and retrieval. This report summarizes the laboratory’s achievements and activities over the course of 2024.DOI: 10.32079/isti-ar-2024/001
Metrics:
2023
Conference article
Open Access
AIMH at MULTI-Fake-DetectIVE: system report
Puccetti G, Esuli A
This report describes our contribution to the EVALITA 2023 shared task MULTI-Fake-DetectIVE which involves the classification of news including textual and visual components. To experiment on this task we focus on textual data augmentation, extending the Italian text and the Images available in the training set using machine translation models and image captioning ones. To train using different set of input features, we use different transformer encoders for each variant of text (Italian, English) and modality (Image). For Task 1, among the models we test, we find that using the Italian text together with its translation improves the model performance while the captions don't provide any improvement. We test the same architecture also on Task 2 although in this case we achieve less satisfactory resultsSource: CEUR WORKSHOP PROCEEDINGS. Parma, Italy, 7-9/09/2023
Project(s): SoBigData
Puccetti G, Esuli A
This report describes our contribution to the EVALITA 2023 shared task MULTI-Fake-DetectIVE which involves the classification of news including textual and visual components. To experiment on this task we focus on textual data augmentation, extending the Italian text and the Images available in the training set using machine translation models and image captioning ones. To train using different set of input features, we use different transformer encoders for each variant of text (Italian, English) and modality (Image). For Task 1, among the models we test, we find that using the Italian text together with its translation improves the model performance while the captions don't provide any improvement. We test the same architecture also on Task 2 although in this case we achieve less satisfactory resultsSource: CEUR WORKSHOP PROCEEDINGS. Parma, Italy, 7-9/09/2023
Project(s): SoBigData
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2024
Conference article
Open Access
Robustness and generalization of synthetic images detectors
Coccomini D. A., Caldelli R., Gennaro C., Fiameni G., Amato G., Falchi F.
In recent times, the increasing spread of synthetic media, known as deepfakes has been made possible by the rapid progress in artificial intelligence technologies, especially deep learning algorithms. Growing worries about the increasing availability and believability of deepfakes have spurred researchers to concentrate on developing methods to detect them. In this field researchers at ISTI CNR’s AIMH Lab, in collaboration with researchers from other organizations, have conducted research, investigations, and projects to contribute to combating this trend, exploring new solutions and threats. This article summarizes the most recent efforts made in this area by our researchers and in collaboration with other institutions and experts.
Coccomini D. A., Caldelli R., Gennaro C., Fiameni G., Amato G., Falchi F.
In recent times, the increasing spread of synthetic media, known as deepfakes has been made possible by the rapid progress in artificial intelligence technologies, especially deep learning algorithms. Growing worries about the increasing availability and believability of deepfakes have spurred researchers to concentrate on developing methods to detect them. In this field researchers at ISTI CNR’s AIMH Lab, in collaboration with researchers from other organizations, have conducted research, investigations, and projects to contribute to combating this trend, exploring new solutions and threats. This article summarizes the most recent efforts made in this area by our researchers and in collaboration with other institutions and experts.
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2022
Other
Open Access
AI and computer vision for smart cities
Amato G, Carrara F, Ciampi L, Di Benedetto M, Gennaro C, Falchi F, Messina N, Vairo C
Artificial Intelligence (AI) is increasingly employed to develop public services that make life easier for citizens. In this abstract, we present some research topics and applications carried out by the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR of Pisa about the study and development of AI-based services for Smart Cities dedicated to the interaction with the physical world through the analysis of images gathered from city cameras. Like no other sensing mechanism, networks of city cameras can 'observe' the world and simultaneously provide visual data to AI systems to extract relevant information and make/suggest decisions helping to solve many real-world problems. Specifically, we discuss some solutions in the context of smart mobility, parking monitoring, infrastructure management, and surveillance systems.Project(s): AI4Media
Amato G, Carrara F, Ciampi L, Di Benedetto M, Gennaro C, Falchi F, Messina N, Vairo C
Artificial Intelligence (AI) is increasingly employed to develop public services that make life easier for citizens. In this abstract, we present some research topics and applications carried out by the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR of Pisa about the study and development of AI-based services for Smart Cities dedicated to the interaction with the physical world through the analysis of images gathered from city cameras. Like no other sensing mechanism, networks of city cameras can 'observe' the world and simultaneously provide visual data to AI systems to extract relevant information and make/suggest decisions helping to solve many real-world problems. Specifically, we discuss some solutions in the context of smart mobility, parking monitoring, infrastructure management, and surveillance systems.Project(s): AI4Media
See at:
CNR IRIS | icities2022.unicam.it | ISTI Repository | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
AIMH Lab 2022 activities for Healthcare
Carrara F, Ciampi L, Di Benedetto M, Falchi F, Gennaro C, Amato G
The application of Artificial Intelligence technologies in healthcare can enhance and optimize medical diagnosis, treatment, and patient care. Medical imaging, which involves Computer Vision to interpret and understand visual data, is one area of healthcare that shows great promise for AI, and it can lead to faster and more accurate diagnoses, such as detecting early signs of cancer or identifying abnormalities in the brain. This short paper provides an introduction to some of the activities of the Artificial Intelligence for Media and Humanities Laboratory of the ISTI-CNR that integrate AI and medical image analysis in healthcare. Specifically, the paper presents approaches that utilize 3D medical images to detect the behavior-variant of frontotemporal dementia, a neurodegenerative syndrome that can be diagnosed by analyzing brain scans. Furthermore, it illustrates some Deep Learning-based techniques for localizing and counting biological structures in microscopy images, such as cells and perineuronal nets. Lastly, the paper presents a practical and cost-effective AI-based tool for multi-species pupillometry (mice and humans), which has been validated in various scenarios.Source: CEUR WORKSHOP PROCEEDINGS, pp. 128-133. Pisa, Italy, 29-31/05/2023
Carrara F, Ciampi L, Di Benedetto M, Falchi F, Gennaro C, Amato G
The application of Artificial Intelligence technologies in healthcare can enhance and optimize medical diagnosis, treatment, and patient care. Medical imaging, which involves Computer Vision to interpret and understand visual data, is one area of healthcare that shows great promise for AI, and it can lead to faster and more accurate diagnoses, such as detecting early signs of cancer or identifying abnormalities in the brain. This short paper provides an introduction to some of the activities of the Artificial Intelligence for Media and Humanities Laboratory of the ISTI-CNR that integrate AI and medical image analysis in healthcare. Specifically, the paper presents approaches that utilize 3D medical images to detect the behavior-variant of frontotemporal dementia, a neurodegenerative syndrome that can be diagnosed by analyzing brain scans. Furthermore, it illustrates some Deep Learning-based techniques for localizing and counting biological structures in microscopy images, such as cells and perineuronal nets. Lastly, the paper presents a practical and cost-effective AI-based tool for multi-species pupillometry (mice and humans), which has been validated in various scenarios.Source: CEUR WORKSHOP PROCEEDINGS, pp. 128-133. Pisa, Italy, 29-31/05/2023
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS