133 citations · 235 across the 17 of their papers we have counts for
18 papers
VidCLearn: A Continual Learning Approach for Text-to-Video Generation
Luca Zanchetta, Lorenzo Papa, Luca Maiano +1
Text-to-video generation is an emerging field in generative AI, enabling the creation of realistic, semantically accurate videos from text prompts. While current models achieve imp…
Shedding Light on Depth: Explainability Assessment in Monocular Depth Estimation
Lorenzo Cirillo, Claudio Schiavella, Lorenzo Papa +2
Explainable artificial intelligence is increasingly employed to understand the decision-making process of deep learning models and create trustworthiness in their adoption. However…
Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection
Luca Maiano, Fabrizio Casadei, Irene Amerini
Detecting deepfakes has become a critical challenge in Computer Vision and Artificial Intelligence. Despite significant progress in detection techniques, generalizing them to open-…
Z-SASLM: Zero-Shot Style-Aligned SLI Blending Latent Manipulation
Alessio Borgi, Luca Maiano, Irene Amerini
We introduce Z-SASLM, a Zero-Shot Style-Aligned SLI (Spherical Linear Interpolation) Blending Latent Manipulation pipeline that overcomes the limitations of current multi-style ble…
Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic Segmentation
Emanuele Mule, Matteo Pannacci, Ali Ghasemi Goudarzi +4
The recent advancements in generative AI techniques, which have significantly increased the online dissemination of altered images and videos, have raised serious concerns about th…
Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization
Corrado Coppola, Lorenzo Papa, Irene Amerini +1
Adaptive gradient methods have been increasingly adopted by deep learning community due to their fast convergence and reduced sensitivity to hyper-parameters. However, these method…
On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation
Lorenzo Papa, Alessandro Sebastianelli, Gabriele Meoni +1
Quantum computing has introduced novel perspectives for tackling and improving machine learning tasks. Moreover, the integration of quantum technologies together with well-known de…
Continuous fake media detection: adapting deepfake detectors to new generative techniques
Francesco Tassone, Luca Maiano, Irene Amerini
Generative techniques continue to evolve at an impressively high rate, driven by the hype about these technologies. This rapid advancement severely limits the application of deepfa…
A Semantic Segmentation-guided Approach for Ground-to-Aerial Image Matching
Francesco Pro, Nikolaos Dionelis, Luca Maiano +2
Nowadays the accurate geo-localization of ground-view images has an important role across domains as diverse as journalism, forensics analysis, transports, and Earth Observation. T…
Computational issues in Optimization for Deep networks
Corrado Coppola, Lorenzo Papa, Marco Boresta +2
The paper aims to investigate relevant computational issues of deep neural network architectures with an eye to the interaction between the optimization algorithm and the classific…
Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images
Nikolaos Dionelis, Francesco Pro, Luca Maiano +2
Data from satellites or aerial vehicles are most of the times unlabelled. Annotating such data accurately is difficult, requires expertise, and is costly in terms of time. Even if…
METER: a mobile vision transformer architecture for monocular depth estimation
L. Papa, P. Russo, I. Amerini
Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth…
D4D: An RGBD diffusion model to boost monocular depth estimation
L. Papa, P. Russo, I. Amerini
Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce. A co…
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It
Federico Siciliano, Luca Maiano, Lorenzo Papa +3
Fake news detection models are critical to countering disinformation but can be manipulated through adversarial attacks. In this position paper, we analyze how an attacker can comp…
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking
Lorenzo Papa, Paolo Russo, Irene Amerini +1
Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract g…
Learning Double-Compression Video Fingerprints Left from Social-Media Platforms
Irene Amerini, Aris Anagnostopoulos, Luca Maiano +1
Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication…
DepthFake: a depth-based strategy for detecting Deepfake videos
Luca Maiano, Lorenzo Papa, Ketbjano Vocaj +1
Fake content has grown at an incredible rate over the past few years. The spread of social media and online platforms makes their dissemination on a large scale increasingly access…
Identification of Social-Media Platform of Videos through the Use of Shared Features
Luca Maiano, Irene Amerini, Lorenzo Ricciardi Celsi +1
Videos have become a powerful tool for spreading illegal content such as military propaganda, revenge porn, or bullying through social networks. To counter these illegal activities…