Claudio Gennaro

Italian National Research Council | CNR · Institute of Information Science and Technology "Alessandro Faedo" ISTI

Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative syndrome whose clinical diagnosis remains a challenging task especially in the early stage of the disease. Currently, the presence of frontal and anterior temporal lobe atrophies on magnetic resonance imaging (MRI) is part of the diagnostic criteria for bvFTD. However, MRI da...

Learning algorithms for Deep Neural Networks are typically based on supervised end-to-end Stochastic Gradient Descent (SGD) training with error backpropagation (backprop). Backprop algorithms require a large number of labelled training samples to achieve high performance. However, in many realistic applications, even if there is plenty of image sam...

Deepfake Generation Techniques are evolving at a rapid pace, making it possible to create realistic manipulated images and videos and endangering the serenity of modern society. The continual emergence of new and varied techniques brings with it a further problem to be faced, namely the ability of deepfake detection models to update themselves prom...

Features extracted from Deep Neural Networks (DNNs) have proven to be very effective in the context of Content Based Image Retrieval (CBIR). In recent work, biologically inspired \textit{Hebbian} learning algorithms have shown promises for DNN training. In this contribution, we study the performance of such algorithms in the development of feature...

Deepfakes are the result of digital manipulation to forge realistic yet fake imagery. With the astonishing advances in deep generative models, fake images or videos are nowadays obtained using variational autoencoders (VAEs) or Generative Adversarial Networks (GANs). These technologies are becoming more accessible and accurate, resulting in fake vi...

Exploiting well-labeled training sets has led deep learning models to astonishing results for counting biological structures in microscopy images. However, dealing with weak multi-rater annotations, i.e., when multiple human raters disagree due to non-trivial patterns, remains a relatively unexplored problem. More reliable labels can be obtained by...

Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel scheduler for real-time multimedia delivery in multipath systems based on an Actor-Critic (AC) RL algorithm. We focus on a challen...

In this paper, we investigate Hebbian learning strategies applied to Convolutional Neural Network (CNN) training. We consider two unsupervised learning approaches, Hebbian Winner-Takes-All (HWTA), and Hebbian Principal Component Analysis (HPCA). The Hebbian learning rules are used to train the layers of a CNN in order to extract features that are t...

In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate numb...

Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting p...

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...

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 reso...

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 improv...

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 vehic...

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 acti...

We propose a semi-supervised learning strategy for deep Convolutional Neural Networks (CNNs) in which an unsupervised pre-training stage, performed using biologically inspired Hebbian learning algorithms, is followed by supervised end-to-end backprop fine-tuning. We explored two Hebbian learning rules for the unsupervised pre-training stage: soft-W...

We explore competitive Hebbian learning strategies to train feature detectors in Convolutional Neural Networks (CNNs), without supervision. We consider variants of the Winner-Takes-All (WTA) strategy explored in previous works, i.e. k-WTA, e-soft-WTA and p-soft-WTA, performing experiments on different object recognition datasets. Results suggest th...

Image-based automatic cell counting is an essential yet challenging task, crucial for the diagnosing of many diseases. Current solutions rely on Convolutional Neural Networks and provide astonishing results. However, their performance is often measured only considering counting errors, which can lead to masked mistaken estimations; a low counting e...

VISIONE is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). It uses a full-text search engine as a search backend. In the latest version of our system, we modified the user interface, and we made some changes to the techniq...

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 activiti...

Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level. Specifically, we present a nove...

Although convolutional neural networks (CNNs) showed remarkable results in many vision tasks, they are still strained by simple yet challenging visual reasoning problems. Inspired by the recent success of the Transformer network in computer vision, in this paper, we introduce the Recurrent Vision Transformer (RViT) model. Thanks to the impact of re...

Space exploration has always been a source of inspiration for humankind, and thanks to modern telescopes, it is now possible to observe celestial bodies far away from us. With a growing number of real and imaginary images of space available on the web and exploiting modern deep Learning architectures such as Generative Adversarial Networks, it is n...

In the domain of approximate metric search, the Permutation-based Indexing (PBI) approaches have been proved to be particularly suitable for dealing with large data collections. These methods employ a permutation-based representation of the data, which can be efficiently indexed using data structures such as inverted files. In the literature, the d...

We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semi-supervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully c...

Deepfakes are the result of digital manipulation to obtain credible videos in order to deceive the viewer. This is done through deep learning techniques based on autoencoders or GANs that become more accessible and accurate year after year, resulting in fake videos that are very difficult to distinguish from real ones. Traditionally, CNN networks h...

This paper presents a communication framework built to simplify the construction of robotic ecologies, i.e., networks of heterogeneous computational nodes interfaced with sensors, actuators, and mobile robots. Building integrated ambient intelligence (AmI) solutions out of such a wide range of heterogeneous devices is a key requirement for a range...

This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focuses on the analysis of single images, this paper proposes the use of multiple visual sources to monitor a wider parking area from different perspectives. The proposed mul...

Deep learning has achieved impressive results in many machine learning tasks such as image recognition and computer vision. Its applicability to supervised problems is however constrained by the availability of high-quality training data consisting of large numbers of humans annotated examples (e.g. millions). To overcome this problem, recently, th...

Cross-modal retrieval is an important functionality in modern search engines, as it increases the user experience by allowing queries and retrieved objects to pertain to different modalities. In this paper, we focus on the image-sentence retrieval task, where the objective is to efficiently find relevant images for a given sentence (image-retrieval...

This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' n...

Emotions play a central role in the social life of every human being, and their study, which represents a multidisciplinary subject, embraces a great variety of research fields. Especially concerning the latter, the analysis of facial expressions represents a very active research area due to its relevance to human-computer interaction applications....

We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semisupervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully co...

Facial Expression Recognition(FER) is one of the most important topic in Human-Computer interactions(HCI). In this work we report details and experimental results about a facial expression recognition method based on state-of-the-art methods. We fine-tuned a SeNet deep learning architecture pre-trained on the well-known VGGFace2 dataset, on the Aff...

Deep learning demonstrated major abilities in solving many kinds of different real-world problems in computer vision literature. However, they are still strained by simple reasoning tasks that humans consider easy to solve. In this work, we probe current state-of-the-art convolutional neural networks on a difficult set of tasks known as the same-di...

This paper presents the second release of VISIONE, a tool for effective video search on large-scale collections. It allows users to search for videos using textual descriptions, keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity. One of the main features of our...

Deep learning demonstrated major abilities in solving many kinds of different real-world problems in computer vision literature. However, they are still strained by simple reasoning tasks that humans consider easy to solve. In this work, we probe current state-of-the-art convolutional neural networks on a difficult set of tasks known as the same-di...

Convolutional Neural Networks have produced state-of-the-art results for a multitude of computer vision tasks under supervised learning. However, the crux of these methods is the need for a massive amount of labeled data to guarantee that they generalize well to diverse testing scenarios. In many real-world applications, there is indeed a large dom...

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 2020 activiti...

Recent work has shown that biologically plausible Hebbian learning can be integrated with backpropagation learning (backprop), when training deep convolutional neural networks. In particular, it has been shown that Hebbian learning can be used for training the lower or the higher layers of a neural network. For instance, Hebbian learning is effecti...

Previous work has shown that it is possible to train neuronal cultures on Multi-Electrode Arrays (MEAs), to recognize very simple patterns. However, this work was mainly focused to demonstrate that it is possible to induce plasticity in cultures, rather than performing a rigorous assessment of their pattern recognition performance. In this paper, w...

Deep learned models are now largely adopted in different fields, and they generally provide superior performances with respect to classical signal-based approaches. Notwithstanding this, their actual reliability when working in an unprotected environment is far enough to be proven. In this work, we consider a novel deep neural network architecture,...

Deep Learning models proved to be able to generate highly discriminative image descriptors, named deep features, suitable for similarity search tasks such as Person Re-Identification and Image Retrieval. Typically, these models are trained by employing high-resolution datasets, therefore reducing the reliability of the produced representations when...

Efficient indexing and retrieval in generic metric spaces often translate into the search for approximate methods that can retrieve relevant samples to a query performing the least amount of distance computations. To this end, when indexing and fulfilling queries, distances are computed and stored only against a small set of reference points (also...

Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there has been an increasing interest in convolutional neural network-based architectures to execute such a task. One of these supervised networks' critical goals is to generalize the knowledge learned during the training phase to new scenari...

Monitoring vehicle flows in cities is crucial to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and assess the flow of vehicles in large areas. Current technologies for vehicle counting in images hinge on large quantities of annotated data, preventing their scalability to city-scale a...

Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the problem of accurate cross-media retrieval through image-sentence matching based on word-region alignments using supervision only at the global image-sentence level. In particular, we pr...

Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there was an increasing interest in Convolutional Neural Network-based architectures for the execution of such a task. One of these supervised networks' critical goals is to generalize the knowledge learned during the training phase to new sc...

In this paper, we describe VISIONE, a video search system that allows users to search for videos using textual keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and satisfy user needs. The p...

The telecommunication industry has seen rapid growth in the last few decades. This trendhas been fostered by the diffusion of wireless communication technologies. In the city of Matera,Italy (European capital of culture 2019), two applications of 5G for public security have been testedby using an aerial drone: the recognition of objects and people...

Recent works in deep-learning research highlighted remarkable relational reasoning capabilities of some carefully designed architectures. In this work, we employ a relationship-aware deep learning model to extract compact visual features used relational image descriptors. In particular, we are interested in relational content-based image retrieval...

Monitoring vehicle flow in cities is a crucial issue to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and asses the flow of vehicles in large areas. Current technologies for vehicle counting in images hinge on large quantities of annotated data, preventing their scalability to city-s...

Approximate Nearest Neighbor (ANN) search is a prevalent paradigm for searching intrinsically high dimensional objects in large-scale data sets. Recently, the permutation-based approach for ANN has attracted a lot of interest due to its versatility in being used in the more general class of metric spaces. In this approach, the entire database is ra...