3 citations · 3 across the 7 of their papers we have counts for
8 papers
From Open-Vocabulary to Vocabulary-Free Semantic Segmentation
Klara Reichard, Giulia Rizzoli, Stefano Gasperini +4
Open-vocabulary semantic segmentation enables models to identify novel object categories beyond their training data. While this flexibility represents a significant advancement, cu…
Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation
Chang Liu, Giulia Rizzoli, Pietro Zanuttigh +2
Current weakly-supervised incremental learning for semantic segmentation (WILSS) approaches only consider replacing pixel-level annotations with image-level labels, while the train…
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather
Giulia Rizzoli, Matteo Caligiuri, Donald Shenaj +2
In Federated Learning (FL), multiple clients collaboratively train a global model without sharing private data. In semantic segmentation, the Federated source Free Domain Adaptatio…
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…
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…
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
HyunJun Jung, Guangyao Zhai, Shun-Cheng Wu +10
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation…
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…