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Found 6 papers, 1 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.
Augmentation-based Domain Generalization for Semantic Segmentation
Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are two research areas that aim to tackle the lack of generalization of Deep Neural Networks (DNNs) towards unseen domains.
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.
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data.
Self-Supervised Domain Mismatch Estimation for Autonomous Perception
Autonomous driving requires self awareness of its perception functions.
GAN- vs. JPEG2000 Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation
The high amount of sensors required for autonomous driving poses enormous challenges on the capacity of automotive bus systems.
