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Found 9 papers, 5 papers with code
Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation
In a second step, we train a generalizing model by adapting towards this pseudo-target domain.
A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation
We present a new augmentation-driven approach to domain generalization for semantic segmentation using a re-parameterized vision transformer (ReVT) with weight averaging of multiple models after training.
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving
DNNs play a significant role in environment perception for the challenging application of automated driving and are employed for tasks such as detection, semantic segmentation, and sensor fusion.
On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models
The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving.
Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network
The rectangular shape of the RIS and the spatial correlation of channels with adjacent RIS antennas due to the short distance between them encourage us to apply it for the RIS configuration.
Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches
Hence, their detection is highly safety-critical, and detection methods can be applied to vast amounts of collected data to select suitable training data.
Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method for Semantic Segmentation Without Using Source Domain Representations
In this paper we present a solution to the task of "unsupervised domain adaptation (UDA) of a given pre-trained semantic segmentation model without relying on any source domain representations".
openDD: A Large-Scale Roundabout Drone Dataset
Analyzing and predicting the traffic scene around the ego vehicle has been one of the key challenges in autonomous driving.
Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance
Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene information from single camera images, which is trainable on arbitrary image sequences without requiring depth labels, e. g., from a LiDAR sensor.
