57 citations · 71 across the 11 of their papers we have counts for
11 papers
Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model
Claudia Cuttano, Antonio Tavera, Fabio Cermelli +2
Many practical applications require training of semantic segmentation models on unlabelled datasets and their execution on low-resource hardware. Distillation from a trained source…
Hierarchical Instance Mixing across Domains in Aerial Segmentation
Edoardo Arnaudo, Antonio Tavera, Fabrizio Dominici +2
We investigate the task of unsupervised domain adaptation in aerial semantic segmentation and discover that the current state-of-the-art algorithms designed for autonomous driving…
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
Donald Shenaj, Eros Fanì, Marco Toldo +6
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the co…
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images
Antonio Tavera, Edoardo Arnaudo, Carlo Masone +1
In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without con…
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
Lidia Fantauzzo, Eros Fanì, Debora Caldarola +4
Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. Howe…
Learning Semantics for Visual Place Recognition through Multi-Scale Attention
Valerio Paolicelli, Antonio Tavera, Carlo Masone +2
In this paper we address the task of visual place recognition (VPR), where the goal is to retrieve the correct GPS coordinates of a given query image against a huge geotagged galle…
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images
Edoardo Arnaudo, Fabio Cermelli, Antonio Tavera +2
Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets. A major issue concerning cur…
Incremental Learning in Semantic Segmentation from Image Labels
Fabio Cermelli, Dario Fontanel, Antonio Tavera +2
Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore…
Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation
Antonio Tavera, Carlo Masone, Barbara Caputo
Semantic segmentation models have reached remarkable performance across various tasks. However, this performance is achieved with extremely large models, using powerful computation…
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
Antonio Tavera, Fabio Cermelli, Carlo Masone +1
In this paper we consider the task of semantic segmentation in autonomous driving applications. Specifically, we consider the cross-domain few-shot setting where training can use o…
IDDA: a large-scale multi-domain dataset for autonomous driving
Emanuele Alberti, Antonio Tavera, Carlo Masone +1
Semantic segmentation is key in autonomous driving. Using deep visual learning architectures is not trivial in this context, because of the challenges in creating suitable large sc…