48 citations · 138 across the 24 of their papers we have counts for
27 papers
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…
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…
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…
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…
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…
Asynchronous Federated Continual Learning
Donald Shenaj, Marco Toldo, Alberto Rigon +1
The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and predefined order. This is not very realistic in federated l…
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…
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains
Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Deep learning models dealing with image understanding in real-world settings must be able to adapt to a wide variety of tasks across different domains. Domain adaptation and class…
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…
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps
Xiaowen Jiang, Valerio Cambareri, Gianluca Agresti +4
Sparse active illumination enables precise time-of-flight depth sensing as it maximizes signal-to-noise ratio for low power budgets. However, depth completion is required to produc…
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints
Paolo Testolina, Francesco Barbato, Umberto Michieli +3
Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task is mainly performed through data-hungry…
End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation
Mazen Mel, Muhammad Siddiqui, Pietro Zanuttigh
Monocular depth estimation is still an open challenge due to the ill-posed nature of the problem at hand. Deep learning based techniques have been extensively studied and proved ca…
Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation
Donald Shenaj, Francesco Barbato, Umberto Michieli +1
Deep neural networks are typically trained in a single shot for a specific task and data distribution, but in real world settings both the task and the domain of application can ch…
Lightweight Deep Learning Architecture for MPI Correction and Transient Reconstruction
Adriano Simonetto, Gianluca Agresti, Pietro Zanuttigh +1
Indirect Time-of-Flight cameras (iToF) are low-cost devices that provide depth images at an interactive frame rate. However, they are affected by different error sources, with the…
RECALL: Replay-based Continual Learning in Semantic Segmentation
Andrea Maracani, Umberto Michieli, Marco Toldo +1
Deep networks allow to obtain outstanding results in semantic segmentation, however they need to be trained in a single shot with a large amount of data. Continual learning setting…
Road Scenes Segmentation Across Different Domains by Disentangling Latent Representations
Francesco Barbato, Umberto Michieli, Marco Toldo +1
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training. Additionally, such models do…
Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation
Francesco Barbato, Marco Toldo, Umberto Michieli +1
Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to…
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
Umberto Michieli, Pietro Zanuttigh
Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in…
Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings
Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adapt…
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
Umberto Michieli, Edoardo Borsato, Luca Rossi +1
The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in t…
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
Marco Toldo, Andrea Maracani, Umberto Michieli +1
The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attract…
Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training
Teo Spadotto, Marco Toldo, Umberto Michieli +1
Unsupervised Domain Adaptation (UDA) aims at improving the generalization capability of a model trained on a source domain to perform well on a target domain for which no labeled d…
Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
Marco Toldo, Umberto Michieli, Gianluca Agresti +1
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use…
Knowledge Distillation for Incremental Learning in Semantic Segmentation
Umberto Michieli, Pietro Zanuttigh
Deep learning architectures have shown remarkable results in scene understanding problems, however they exhibit a critical drop of performances when they are required to learn incr…