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20192024
most citedUnsupervised Domain Adaptation in Semantic Segmentation: a Review

28 citations · 42 across the 17 of their papers we have counts for

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24 papers · 1 filter

cs.CV2024★ 2 cited

A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation

Francesco Barbato, Umberto Michieli, Mehmet Kerim Yucel +2

In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three…

cs.CV2023

RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation

Chang Liu, Giulia Rizzoli, Francesco Barbato +5

Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies. However, existing methods strug…

cs.CV2023

SynDrone -- Multi-modal UAV Dataset for Urban Scenarios

Giulia Rizzoli, Francesco Barbato, Matteo Caligiuri +1

The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs) imagery heavily relies on the availability of annotated high-resolution aerial data. However, the…

cs.CV2023★ 1 cited

Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network

Francesco Barbato, Elena Camuffo, Simone Milani +1

State-of-the-art multimodal semantic segmentation strategies combining LiDAR and color data are usually designed on top of asymmetric information-sharing schemes and assume that bo…

cs.CV2023

Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers

Giulia Rizzoli, Donald Shenaj, Pietro Zanuttigh

With the increasing availability of depth sensors, multimodal frameworks that combine color information with depth data are gaining interest. However, ground truth data for semanti…

cs.CV2022

DepthFormer: Multimodal Positional Encodings and Cross-Input Attention for Transformer-Based Segmentation Networks

Francesco Barbato, Giulia Rizzoli, Pietro Zanuttigh

Most approaches for semantic segmentation use only information from color cameras to parse the scenes, yet recent advancements show that using depth data allows to further improve…