most citedLearning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning

3 citations · 4 across the 6 of their papers we have counts for

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cs.CV2022

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…

cs.CV20223 cited

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…

cs.CV2022

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…

cs.CV20221 cited

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…

cs.CV2021

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…

cs.CV2021

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…