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Found 10 papers, 3 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.
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation
To this end, we design a small, modular, and efficient (just 2GFLOPs to process a Full HD image) system to enhance input data for robust downstream multimedia understanding with minimal computational cost.
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
SynDrone -- Multi-modal UAV Dataset for Urban Scenarios
The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs) imagery heavily relies on the availability of annotated high-resolution aerial data.
Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network
In this work, we re-frame the task of multimodal semantic segmentation by enforcing a tightly-coupled feature representation and a symmetric information-sharing scheme, which allows our approach to work even when one of the input modalities is missing.
DepthFormer: Multimodal Positional Encodings and Cross-Input Attention for Transformer-Based Segmentation Networks
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 performances.
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints
Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems.
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.
Road Scenes Segmentation Across Different Domains by Disentangling Latent Representations
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training.
Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation
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 distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization.
