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2024
Conference article
Open Access
VISIONE 5.0: enhanced user interface and AI models for VBS2024
Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, Claudio Vairo
In this paper, we introduce the fifth release of VISIONE, an advanced video retrieval system offering diverse search functionalities. The user can search for a target video using textual prompts, drawing objects and colors appearing in the target scenes in a canvas, or images as query examples to search for video keyframes with similar content. Compared to the previous version of our system, which was runner-up at VBS 2023, the forthcoming release, set to participate in VBS 2024, showcases a refined user interface that enhances its usability and updated AI models for more effective video content analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 14557, pp. 332-339. Amsterdam, NL, 29/01-2/02/2024
DOI: 10.1007/978-3-031-53302-0_29
DOI: https://doi.org/10.1007/978-3-031-53302-0_29
Project(s): AI4Media
, SUN
Metrics:
Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, Claudio Vairo
In this paper, we introduce the fifth release of VISIONE, an advanced video retrieval system offering diverse search functionalities. The user can search for a target video using textual prompts, drawing objects and colors appearing in the target scenes in a canvas, or images as query examples to search for video keyframes with similar content. Compared to the previous version of our system, which was runner-up at VBS 2023, the forthcoming release, set to participate in VBS 2024, showcases a refined user interface that enhances its usability and updated AI models for more effective video content analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 14557, pp. 332-339. Amsterdam, NL, 29/01-2/02/2024
DOI: 10.1007/978-3-031-53302-0_29
DOI: https://doi.org/10.1007/978-3-031-53302-0_29
Project(s): AI4Media
, SUN Metrics:
2024
Conference article
Open Access
Vibration monitoring of historical towers: new contributions from data science
Girardi M., Gurioli G., Messina N., Padovani C., Pellegrini D.
Deep neural networks are used to study the ambient vibrations of the medieval towers of the San Frediano Cathedral and the Guinigi Palace in the historic centre of Lucca. The towers have been continuously monitored for many months via high-sensitivity seismic stations. The recorded data sets integrated with environmental parameters are employed to train a Temporal Fusion Transformer network and forecast the dynamic behaviour of the monitored structures. The results show that the adopted algorithm can learn the main features of the towers’ dynamic response, predict its evolution over time, and detect anomalies.Source: LECTURE NOTES IN CIVIL ENGINEERING, vol. 514, pp. 15-24. Naples, Italy, 21-24/05/2024
DOI: 10.1007/978-3-031-61421-7_2
Metrics:
Girardi M., Gurioli G., Messina N., Padovani C., Pellegrini D.
Deep neural networks are used to study the ambient vibrations of the medieval towers of the San Frediano Cathedral and the Guinigi Palace in the historic centre of Lucca. The towers have been continuously monitored for many months via high-sensitivity seismic stations. The recorded data sets integrated with environmental parameters are employed to train a Temporal Fusion Transformer network and forecast the dynamic behaviour of the monitored structures. The results show that the adopted algorithm can learn the main features of the towers’ dynamic response, predict its evolution over time, and detect anomalies.Source: LECTURE NOTES IN CIVIL ENGINEERING, vol. 514, pp. 15-24. Naples, Italy, 21-24/05/2024
DOI: 10.1007/978-3-031-61421-7_2
Metrics:
See at:
IRIS Cnr 
| IRIS Cnr | IRIS Cnr | doi.org 
| CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Deep learning and structural health monitoring: temporal fusion transformers for anomaly detection in masonry towers
Falchi F., Girardi M., Gurioli G., Messina N., Padovani C., Pellegrini D.
Detecting anomalies in the vibrational features of age-old buildings is crucial within the Structural Health Monitoring (SHM) framework. The SHM techniques can leverage information from onsite measurements and environmental sources to identify the dynamic properties (such as the frequencies) of the monitored structure, searching for possible deviations or unusual behavior over time. In this paper, the Temporal Fusion Transformer (TFT) network, a deep learning algorithm initially designed for multi-horizon time series forecasting and tested on electricity, traffic, retail, and volatility problems, is applied to SHM. The TFT approach is adopted to investigate the behavior of the Guinigi Tower located in Lucca (Italy) and subjected to a long-term dynamic monitoring campaign. The TFT network is trained on the tower's experimental frequencies enriched with other environmental parameters. The transformer is then employed to predict the vibrational features (natural frequencies, root mean squares values of the velocity time series) and detect possible anomalies or unexpected events by inspecting how much the actual frequencies deviate from the predicted ones. The TFT technique is used to detect the effects of the Viareggio earthquake that occurred on 6 February 2022, and the structural damage induced by three simulated damage scenarios.Source: MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 215 (issue 111382)
DOI: 10.1016/j.ymssp.2024.111382
Metrics:
Falchi F., Girardi M., Gurioli G., Messina N., Padovani C., Pellegrini D.
Detecting anomalies in the vibrational features of age-old buildings is crucial within the Structural Health Monitoring (SHM) framework. The SHM techniques can leverage information from onsite measurements and environmental sources to identify the dynamic properties (such as the frequencies) of the monitored structure, searching for possible deviations or unusual behavior over time. In this paper, the Temporal Fusion Transformer (TFT) network, a deep learning algorithm initially designed for multi-horizon time series forecasting and tested on electricity, traffic, retail, and volatility problems, is applied to SHM. The TFT approach is adopted to investigate the behavior of the Guinigi Tower located in Lucca (Italy) and subjected to a long-term dynamic monitoring campaign. The TFT network is trained on the tower's experimental frequencies enriched with other environmental parameters. The transformer is then employed to predict the vibrational features (natural frequencies, root mean squares values of the velocity time series) and detect possible anomalies or unexpected events by inspecting how much the actual frequencies deviate from the predicted ones. The TFT technique is used to detect the effects of the Viareggio earthquake that occurred on 6 February 2022, and the structural damage induced by three simulated damage scenarios.Source: MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 215 (issue 111382)
DOI: 10.1016/j.ymssp.2024.111382
Metrics:
See at:
CNR IRIS | ISTI Repository | www.sciencedirect.com | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Cascaded transformer-based networks for Wikipedia large-scale image-caption matching
Messina N, Coccomini Da, Esuli A, Falchi F
With the increasing importance of multimedia and multilingual data in online encyclopedias,novel methods are needed to fill domain gaps and automatically connect different modalitiesfor increased accessibility. For example,Wikipedia is composed of millions of pages writtenin multiple languages. Images, when present, often lack textual context, thus remainingconceptually floating and harder to find and manage. In this work, we tackle the novel taskof associating images from Wikipedia pages with the correct caption among a large poolof available ones written in multiple languages, as required by the image-caption matchingKaggle challenge organized by theWikimedia Foundation.Asystem able to perform this taskwould improve the accessibility and completeness of the underlying multi-modal knowledgegraph in online encyclopedias. We propose a cascade of two models powered by the recentTransformer networks able to efficiently and effectively infer a relevance score betweenthe query image data and the captions. We verify through extensive experiments that theproposed cascaded approach effectively handles a large pool of images and captions whilemaintaining bounded the overall computational complexity at inference time.With respect toother approaches in the challenge leaderboard,we can achieve remarkable improvements overthe previous proposals (+8% in nDCG@5 with respect to the sixth position) with constrainedresources. The code is publicly available at https://tinyurl.com/wiki-imcap.Source: MULTIMEDIA TOOLS AND APPLICATIONS, vol. 83, pp. 62915-62935
DOI: 10.1007/s11042-023-17977-0
Project(s): AI4Media
Metrics:
Messina N, Coccomini Da, Esuli A, Falchi F
With the increasing importance of multimedia and multilingual data in online encyclopedias,novel methods are needed to fill domain gaps and automatically connect different modalitiesfor increased accessibility. For example,Wikipedia is composed of millions of pages writtenin multiple languages. Images, when present, often lack textual context, thus remainingconceptually floating and harder to find and manage. In this work, we tackle the novel taskof associating images from Wikipedia pages with the correct caption among a large poolof available ones written in multiple languages, as required by the image-caption matchingKaggle challenge organized by theWikimedia Foundation.Asystem able to perform this taskwould improve the accessibility and completeness of the underlying multi-modal knowledgegraph in online encyclopedias. We propose a cascade of two models powered by the recentTransformer networks able to efficiently and effectively infer a relevance score betweenthe query image data and the captions. We verify through extensive experiments that theproposed cascaded approach effectively handles a large pool of images and captions whilemaintaining bounded the overall computational complexity at inference time.With respect toother approaches in the challenge leaderboard,we can achieve remarkable improvements overthe previous proposals (+8% in nDCG@5 with respect to the sixth position) with constrainedresources. The code is publicly available at https://tinyurl.com/wiki-imcap.Source: MULTIMEDIA TOOLS AND APPLICATIONS, vol. 83, pp. 62915-62935
DOI: 10.1007/s11042-023-17977-0
Project(s): AI4Media
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS
2024
Conference article
Open Access
Will VISIONE remain competitive in lifelog image search?
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE is a versatile video retrieval system supporting diverse search functionalities, including free-text, similarity, and temporal searches. Its recent success in securing first place in the 2024 Video Browser Showdown (VBS) highlights its effectiveness. Originally designed for analyzing, indexing, and searching diverse video content, VISIONE can also be adapted to images from lifelog cameras thanks to its reliance on frame-based representations and retrieval mechanisms. In this paper, we present an overview of VISIONE's core characteristics and the adjustments made to accommodate lifelog images. These adjustments primarily focus on enhancing result visualization within the GUI, such as grouping images by date or hour to align with lifelog dataset imagery. It's important to note that while the GUI has been updated, the core search engine and visual content analysis components remain unchanged from the version presented at VBS 2024. Specifically, metadata such as local time, GPS coordinates, and concepts associated with images are not indexed or utilized in the system. Instead, the system relies solely on the visual content of the images, with date and time information extracted from their filenames, which are utilized exclusively within the GUI for visualization purposes. Our objective is to evaluate the system's performance within the Lifelog Search Challenge, emphasizing reliance on visual content analysis without additional metadata.DOI: 10.1145/3643489.3661122
Project(s): AI4Media
Metrics:
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE is a versatile video retrieval system supporting diverse search functionalities, including free-text, similarity, and temporal searches. Its recent success in securing first place in the 2024 Video Browser Showdown (VBS) highlights its effectiveness. Originally designed for analyzing, indexing, and searching diverse video content, VISIONE can also be adapted to images from lifelog cameras thanks to its reliance on frame-based representations and retrieval mechanisms. In this paper, we present an overview of VISIONE's core characteristics and the adjustments made to accommodate lifelog images. These adjustments primarily focus on enhancing result visualization within the GUI, such as grouping images by date or hour to align with lifelog dataset imagery. It's important to note that while the GUI has been updated, the core search engine and visual content analysis components remain unchanged from the version presented at VBS 2024. Specifically, metadata such as local time, GPS coordinates, and concepts associated with images are not indexed or utilized in the system. Instead, the system relies solely on the visual content of the images, with date and time information extracted from their filenames, which are utilized exclusively within the GUI for visualization purposes. Our objective is to evaluate the system's performance within the Lifelog Search Challenge, emphasizing reliance on visual content analysis without additional metadata.DOI: 10.1145/3643489.3661122
Project(s): AI4Media
Metrics:
2024
Conference article
Open Access
Visione 5.0: toward evaluation with novice users
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE is a video search system that integrates multiple search functionalities, allowing users to search for video segments using textual and visual queries, complemented by temporal search capabilities. It exploits state-of-the-art Artificial Intelligence approaches for visual content analysis and highly efficient indexing techniques to ensure fast response and scalability. In the recently concluded Video Browser Showdown (VBS2024) - a well-established international competition in interactive video retrieval - VISIONE ranked first and scored as the best interactive video search system in four out of seven tasks carried out in the competition.This paper provides an overview of the VISIONE system, emphasizing the improvements made to the system in the last year to improve its usability for novice users. A demonstration video showcasing the system's capabilities across 2,300 hours of diverse video content is available online, as well as a simplified demo of VISIONE.DOI: 10.1109/cbmi62980.2024.10859203
Project(s): AI4Media, National Centre for HPC, Big Data and Quantum Computing, a MUltimedia platform for Content Enrichment and Search in audiovisual archives
Metrics:
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE is a video search system that integrates multiple search functionalities, allowing users to search for video segments using textual and visual queries, complemented by temporal search capabilities. It exploits state-of-the-art Artificial Intelligence approaches for visual content analysis and highly efficient indexing techniques to ensure fast response and scalability. In the recently concluded Video Browser Showdown (VBS2024) - a well-established international competition in interactive video retrieval - VISIONE ranked first and scored as the best interactive video search system in four out of seven tasks carried out in the competition.This paper provides an overview of the VISIONE system, emphasizing the improvements made to the system in the last year to improve its usability for novice users. A demonstration video showcasing the system's capabilities across 2,300 hours of diverse video content is available online, as well as a simplified demo of VISIONE.DOI: 10.1109/cbmi62980.2024.10859203
Project(s): AI4Media
, National Centre for HPC, Big Data and Quantum Computing, a MUltimedia platform for Content Enrichment and Search in audiovisual archivesMetrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
The devil is in the fine-grained details: evaluating open-vocabulary object detectors for fine-grained understanding
Bianchi L., Carrara F., Messina N., Gennaro C., Falchi F.
Recent advancements in large vision-language models enabled visual object detection in open-vocabulary scenar-ios, where object classes are defined in free-text formats during inference. In this paper, we aim to probe the state-of-the-art methods for open-vocabulary object detection to determine to what extent they understand finegrained prop-erties of objects and their parts. To this end, we intro-duce an evaluation protocol based on dynamic vocabulary generation to test whether models detect, discern, and as-sign the correct fine-grained description to objects in the presence of hard-negative classes. We contribute with a benchmark suite of increasing difficulty and probing dif-ferent properties like color, pattern, and material. We fur-ther enhance our investigation by evaluating several state-of-the-art open-vocabulary object detectors using the proposed protocol and find that most existing solutions, which shine in standard open-vocabulary benchmarks, struggle to accurately capture and distinguish finer object details. We conclude the paper by highlighting the limitations of current methodologies and exploring promising research directions to overcome the discovered drawbacks. Data and code are available at https://lorebianchi98.github.io/FG-OVD/.Source: PROCEEDINGS IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, pp. 22520-22529. Seattle (USA), 17-21/06/2024
DOI: 10.1109/cvpr52733.2024.02125
DOI: 10.48550/arxiv.2311.17518
Project(s): SUN, a MUltimedia platform for Content Enrichment and Search in audiovisual archives
Metrics:
Bianchi L., Carrara F., Messina N., Gennaro C., Falchi F.
Recent advancements in large vision-language models enabled visual object detection in open-vocabulary scenar-ios, where object classes are defined in free-text formats during inference. In this paper, we aim to probe the state-of-the-art methods for open-vocabulary object detection to determine to what extent they understand finegrained prop-erties of objects and their parts. To this end, we intro-duce an evaluation protocol based on dynamic vocabulary generation to test whether models detect, discern, and as-sign the correct fine-grained description to objects in the presence of hard-negative classes. We contribute with a benchmark suite of increasing difficulty and probing dif-ferent properties like color, pattern, and material. We fur-ther enhance our investigation by evaluating several state-of-the-art open-vocabulary object detectors using the proposed protocol and find that most existing solutions, which shine in standard open-vocabulary benchmarks, struggle to accurately capture and distinguish finer object details. We conclude the paper by highlighting the limitations of current methodologies and exploring promising research directions to overcome the discovered drawbacks. Data and code are available at https://lorebianchi98.github.io/FG-OVD/.Source: PROCEEDINGS IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, pp. 22520-22529. Seattle (USA), 17-21/06/2024
DOI: 10.1109/cvpr52733.2024.02125
DOI: 10.48550/arxiv.2311.17518
Project(s): SUN
, a MUltimedia platform for Content Enrichment and Search in audiovisual archivesMetrics:
See at:
arXiv.org e-Print Archive | IRIS Cnr | ieeexplore.ieee.org | doi.org | doi.org | CNR IRIS | 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:
2024
Journal article
Open Access
Evaluating performance and trends in interactive video retrieval: insights from the 12th VBS competition
Vadicamo L., Arnold R., Bailer W., Carrara F., Gurrin C., Hezel N., Li X., Lokoc J., Lubos S., Ma Z., Messina N., Nguyen T., Peska L., Rossetto L., Sauter L., Schöffmann K., Spiess F., Tran M., Vrochidis S.
This paper conducts a thorough examination of the 12th Video Browser Showdown (VBS) competition, a well-established international benchmarking campaign for interactive video search systems. The annual VBS competition has witnessed a steep rise in the popularity of multimodal embedding-based approaches in interactive video retrieval. Most of the thirteen systems participating in VBS 2023 utilized a CLIP-based cross-modal search model, allowing the specification of free-form text queries to search visual content. This shared emphasis on joint embedding models contributed to balanced performance across various teams. However, the distinguishing factors of the top-performing teams included the adept combination of multiple models and search modes, along with the capabilities of interactive interfaces to facilitate and refine the search process. Our work provides an overview of the state-of-the-art approaches employed by the participating systems and conducts a thorough analysis of their search logs, which record user interactions and results of their queries for each task. Our comprehensive examination of the VBS competition offers assessments of the effectiveness of the retrieval models, browsing efficiency, and user query patterns. Additionally, it provides valuable insights into the evolving landscape of interactive video retrieval and its future challenges.Source: IEEE ACCESS, vol. 12, pp. 79342-79366
DOI: 10.1109/access.2024.3405638
Project(s): AI4Media, SUN
Metrics:
Vadicamo L., Arnold R., Bailer W., Carrara F., Gurrin C., Hezel N., Li X., Lokoc J., Lubos S., Ma Z., Messina N., Nguyen T., Peska L., Rossetto L., Sauter L., Schöffmann K., Spiess F., Tran M., Vrochidis S.
This paper conducts a thorough examination of the 12th Video Browser Showdown (VBS) competition, a well-established international benchmarking campaign for interactive video search systems. The annual VBS competition has witnessed a steep rise in the popularity of multimodal embedding-based approaches in interactive video retrieval. Most of the thirteen systems participating in VBS 2023 utilized a CLIP-based cross-modal search model, allowing the specification of free-form text queries to search visual content. This shared emphasis on joint embedding models contributed to balanced performance across various teams. However, the distinguishing factors of the top-performing teams included the adept combination of multiple models and search modes, along with the capabilities of interactive interfaces to facilitate and refine the search process. Our work provides an overview of the state-of-the-art approaches employed by the participating systems and conducts a thorough analysis of their search logs, which record user interactions and results of their queries for each task. Our comprehensive examination of the VBS competition offers assessments of the effectiveness of the retrieval models, browsing efficiency, and user query patterns. Additionally, it provides valuable insights into the evolving landscape of interactive video retrieval and its future challenges.Source: IEEE ACCESS, vol. 12, pp. 79342-79366
DOI: 10.1109/access.2024.3405638
Project(s): AI4Media
, SUN Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS