25 citations · 59 across the 11 of their papers we have counts for
11 papers
CalibBEV: LiDAR-Camera Calibration via BEV Alignment
Filippo D'Addeo, Lorenzo Cipelli, Adriano Cardace +3
We present CalibBEV, a novel Bird's Eye View (BEV) alignment approach for LiDAR-camera calibration. Our method unifies LiDAR and camera data into a shared 3D spatial representation…
WaveMAE: Wavelet decomposition Masked Auto-Encoder for Remote Sensing
Vittorio Bernuzzi, Leonardo Rossi, Tomaso Fontanini +2
Self-supervised learning (SSL) has recently emerged as a key strategy for building foundation models in remote sensing, where the scarcity of annotated data limits the applicabilit…
CoLoR-GAN: Continual Few-Shot Learning with Low-Rank Adaptation in Generative Adversarial Networks
Munsif Ali, Leonardo Rossi, Massimo Bertozzi
Continual learning (CL) in the context of Generative Adversarial Networks (GANs) remains a challenging problem, particularly when it comes to learn from a few-shot (FS) samples wit…
SISMA: Semantic Face Image Synthesis with Mamba
Filippo Botti, Alex Ergasti, Tomaso Fontanini +3
Diffusion Models have become very popular for Semantic Image Synthesis (SIS) of human faces. Nevertheless, their training and inference is computationally expensive and their compu…
U-Shape Mamba: State Space Model for faster diffusion
Alex Ergasti, Filippo Botti, Tomaso Fontanini +3
Diffusion models have become the most popular approach for high-quality image generation, but their high computational cost still remains a significant challenge. To address this p…
FLAV: Rolling Flow matching for infinite Audio Video generation
Alex Ergasti, Giuseppe Gabriele Tarollo, Filippo Botti +4
Joint audio-video (AV) generation is still a significant challenge in generative AI, primarily due to three critical requirements: quality of the generated samples, seamless multim…
CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks
Munsif Ali, Leonardo Rossi, Massimo Bertozzi
Few-shot and continual learning face two well-known challenges in GANs: overfitting and catastrophic forgetting. Learning new tasks results in catastrophic forgetting in deep learn…
Mamba-ST: State Space Model for Efficient Style Transfer
Filippo Botti, Alex Ergasti, Leonardo Rossi +4
The goal of style transfer is, given a content image and a style source, generating a new image preserving the content but with the artistic representation of the style source. Mos…
MARS: Paying more attention to visual attributes for text-based person search
Alex Ergasti, Tomaso Fontanini, Claudio Ferrari +2
Text-based person search (TBPS) is a problem that gained significant interest within the research community. The task is that of retrieving one or more images of a specific individ…
Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing
Leonardo Rossi, Vittorio Bernuzzi, Tomaso Fontanini +2
Due to the limitations of current optical and sensor technologies and the high cost of updating them, the spectral and spatial resolution of satellites may not always meet desired…
Controllable Face Synthesis with Semantic Latent Diffusion Models
Alex Ergasti, Claudio Ferrari, Tomaso Fontanini +2
Semantic Image Synthesis (SIS) is among the most popular and effective techniques in the field of face generation and editing, thanks to its good generation quality and the versati…