Artificial Intelligence for Media and Humanities (AIMH)
Head: Giuseppe Amato
Home
Web Site: http://aimh.isti.cnr.it/
Artificial intelligence is changing our life in an unprecedented way and will have an impact on society comparable to the advent of the television, personal computers, and the world wide web. Artificial intelligence-enabled technology is increasingly becoming present in daily used devices and services like smartphones, smartwatches, smart tv sticks, personal computers, on-line shopping services, online entertainment services, on-line infotainment services.
The explosion of artificial intelligence, mostly driven by the advances in deep learning, has been significantly favored by the availability of powerful AI specialized hardware and very large datasets to be used to train AI algorithms. In fact, on one hand, GPU-powered devices allow processing huge amounts of training data in reasonable time. On the other hand, digital data produced by people, for instance with their smartphones, and shared on the world wide web and social networks, offer a valuable source of (noisily) annotated data that can be used to teach AI algorithms to perform a wide range of non-trivial tasks.
The Artificial Intelligence for Media and Humanities (AIMH) lab 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.
Specifically, the AIMH lab pursues the following research lines:
AI and visual data: investigating new AI-based solutions to image and video content analysis, understanding, and classification. This includes techniques for detection, recognition (object, pedestrian, face, etc), classification, feature extraction (low- and high-level, relational, cross-media, etc), anomaly detection also considering adversarial machine learning threats.
AI and textual data: investigating AI-based solutions to textual data analysis, understanding, and classification. This includes representation learning for text classification, transfer learning for cross-lingual and cross-domain text classification, sentiment classification, sequence learning for information extraction, text quantification, transductive text classification, and applications of the above to domains such as authorship analysis and technology-assisted review.
AI and digital humanities: investigating AI-based solutions to represent, access, archive, and manage tangible and intangible cultural heritage data. This includes solutions based on ontologies, with a special focus on narratives, and solutions based on multimedia content analysis, recognition, and retrieval.
AI and large-scale multimedia information retrieval: investigating efficient, effective, and scalable AI-based solutions for searching multimedia content in large datasets of non-annotated data. This includes techniques for multimedia content extraction and representation, scalable access methods for similarity search, multimedia database management.
People
- Amato Giuseppe Research Staff
- Bartalesi Lenzi Valentina Research Staff
- Bolettieri Paolo Technical Staff
- Carraglia Michele Research Staff
- Carrara Fabio Research Staff
- Ciampi Luca Research Staff
- Concordia Cesare Research Staff
- Di Benedetto Marco Research Staff
- Esuli Andrea Research Staff
- Falchi Fabrizio Research Staff
- Gennaro Claudio Research Staff
- Lagani Gabriele Research Staff
- Messina Nicola Research Staff
- Moreo Fernandez Alejandro David Research Staff
- Nardi Alessandro Technical Staff
- Sebastiani Fabrizio Research Staff
- Trupiano Luca Research Staff
- Vadicamo Lucia Research Staff
- Vairo Claudio Francesco Research Staff
- Aloia Nicola Research Associate
- Bianchi Lorenzo Research Associate
- Casarosa Vittore Research Associate
- Cassese Maria Research Associate
- Coccomini Davide Alessandro Graduate Fellow
- De Martino Claudio Graduate Fellow
- Fazzari Edoardo Research Associate
- Federico Giulio Research Associate
- Lenzi Emanuele Research Associate
- Leocata Martina Graduate Fellow
- Meghini Carlo Research Associate
- Negi Kajal Research Associate
- Pedrotti Andrea Graduate Fellow
- Pierucci Matteo Student
- Pratelli Nicolò Graduate Fellow
- Puccetti Giovanni Graduate Fellow
- Rabitti Fausto Research Associate
- Savino Pasquale Research Associate
- Sperduti Gianluca Graduate Fellow
- Thanos Costantino Research Associate
- Volpi Lorenzo Graduate Fellow
Projects
AI-SAFETY
AI-SAFETY: AI-Based Smart System for Operator Safety in Manufacturing Processes
AI4Media
A European Excellence Centre for Media, Society and Democracy
CN_CNMS_Spoke6_AIMH- CUP B43C22000440001
CN - Sustainable Mobility Center (Centro Nazionale per la Mobilità Sostenibile – CNMS) Spoke 6
CRAEFT
Craft Understanding, Education, Training, and Preservation for Posterity and Prosperity
ECS_THE_Spoke3_WN -CUP B83C22003930001
ECS - Tuscany Health Ecosystem Spoke 3
ECS_THE_Spoke5_WN -CUP B83C22003930001
ECS - Tuscany Health Ecosystem Spoke 5
ECS_THE_Spoke8_AIMH -CUP B83C22003930001
ECS - Tuscany Health Ecosystem Spoke 8 - Tuscany Health Environment
IMAGO
Index Medii Aevi Geographiae Operum
ITSERR - CUP: B53C22001770006
Italian Strengthening of Esfri RI Resilience (ITSERR)
PE07_SERICS_Spoke2_AIMH -B53C22003950001
PE07 - SERICS - Security and Rights in the CyberSpace Spoke 2
SoBigData RI PPP
SoBigData RI Preparatory Phase Project
SoBigData++
SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics’ — ‘SoBigData-PlusPlus’
SoBigData++ (AIMH)
Social Mining and Big Data ++
SoBigData.it - CUP B53C22001760006
SoBigData.it: Strengthening the Italian RI for Social Mining and Big Data Analytics
SUN
Social and hUman ceNtered XR (SUN)
Tuscany X.0
Tuscany EU Digital Innovation Hub
News
Learning to Quantify: Methods and Applications (LQ 2024)
Events
2024-03-19 h.11:31
Learning to Quantify (LQ - also known as “quantification“, or “supervised prevalence estimation“, or “class prior estimation“, or “unfolding”) is the task of training class prevalence est...
VISIONE won the 13th Video Browser Showdown competition
Achievements
2024-02-13 h.07:23
VISIONE is a system for fast and effective video search on a large-scale dataset, developed by Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro, Nicola Me...
CRAEFT – Let’s kickoff
Events
2023-03-15 h.07:12
On 7, 8 and 9 March, the Kickoff of the CRAEFT project took place. CRAEFT, short for Craft Understanding, Education, Training, and Preservation for Posterity and Prosperity, will deepen ...
Al via il progetto europeo CRAEFT
Events
2023-03-15 h.07:09
Nei giorni 7, 8 e 9 marzo ha avuto luogo il Kickoff del progetto CRAEFT. Il progetto Craft Understanding, Education, Training, and Preservation for Posterity and Prosperity (abbreviato i...
The VISIONE video retrieval system runner-up at VBS 2023 competition
Achievements
2023-01-23 h.11:30
VISIONE is a system for fast and effective video search on a large-scale dataset, developed by Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro, Nicola Me...
ITSERR – Let’s kickoff
Events
2022-11-21 h.08:04
Il 17 novembre 2022 il progetto ITSERR – Italian Strengthening of the RESILIENCE RI ha finalmente preso il via (vedi Allegato).
ITSERR – Let’s kickoff
Events
2022-11-21 h.08:03
On November 17, 2022, the ITSERR - Italian Strengthening of the RESILIENCE RI project finally kicked off (see Annex)
A Fabio Carrara del Cnr-Isti il premio ERCIM "Cor Baayen" 2022 per giovani ricercatori
Achievements
2022-10-24 h.16:24
Fabio Carrara, 32 anni, ricercatore del Cnr-Isti, è stato selezionato come vincitore dell'ERCIM Cor Baayen Award 2022. Il premio annuale, creato nel 1995 per onorare il primo presidente d...
Conosci la MIA ORMA #1 – Settembre 2021
News
2021-10-04 h.13:45
Nell'ambito del progetto ORMA - Alta fORMAzione e ricerca-azione presso enti di ricerca toscani, nasce la rubrica “Conosci la MIA ORMA”, leggi la prima intervista.