133 citations · 188 across the 11 of their papers we have counts for
12 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 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…
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…
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…
Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images
Marco Corrias, Lorenzo Papa, Igor Sokolović +7
Analyzing atomically resolved images is a time-consuming process requiring solid experience and substantial human intervention. In addition, the acquired images contain a large amo…
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…