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Found 5 papers, 2 papers with code
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather
In Federated Learning (FL), multiple clients collaboratively train a global model without sharing private data.
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers
With the increasing availability of depth sensors, multimodal frameworks that combine color information with depth data are gaining interest.
Asynchronous Federated Continual Learning
We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots.
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
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 collected data.
Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation
In this paper we introduce the novel task of coarse-to-fine learning of semantic segmentation architectures in presence of domain shift.
