Artificial Intelligence for Media and Humanities (AIMH)

Head: Giuseppe Amato

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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

News

A Lorenzo Volpi il Premio 2024 dell’Associazione Italiana di Intelligenza Artificiale

Achievements

2024-10-28 h.14:26

Lorenzo Volpi, dottorando presso il Laboratorio AIMH, ha vinto il premio per neolaureati “Leonardo Lesmo” 2024 dell'Associazione Italiana per l'Intelligenza Artificiale, per la sua tesi d...

È del Cnr-Isti il vincitore del premio Ercim per giovani ricercatori

Achievements

2024-10-25 h.08:23

Nicola Messina, ricercatore del Laboratorio AIMH, è stato selezionato come vincitore dell'ERCIM Cor Baayen Award 2024 (leggi di più).

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.