14 citations · 52 across the 15 of their papers we have counts for
22 papers · 1 filter
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…
Modeling Missing Annotations for Incremental Learning in Object Detection
Fabio Cermelli, Antonino Geraci, Dario Fontanel +1
Despite the recent advances in the field of object detection, common architectures are still ill-suited to incrementally detect new categories over time. They are vulnerable to cat…
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…
Rethinking Visual Geo-localization for Large-Scale Applications
Gabriele Berton, Carlo Masone, Barbara Caputo
Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations. To investigate…
Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation
Fabio Cermelli, Massimiliano Mancini, Samuel Rota Buló +2
Deep neural networks have enabled major progresses in semantic segmentation. However, even the most advanced neural architectures suffer from important limitations. First, they are…