KLIEP-based Density Ratio Estimation for Semantically Consistent Synthetic to Real Images Adaptation in Urban Traffic Scenes
This work addresses domain adaptation issues for autonomous driving systems by enhancing synthetic data translation, though it is incremental as it builds on existing adversarial methods.
The paper tackled the problem of semantic inconsistencies in synthetic-to-real image translation for urban traffic scenes, showing that their proposed density prematching strategy using KLIEP-based density ratio estimation improves translation quality and usability for semantic segmentation tasks in autonomous driving.
Synthetic data has been applied in many deep learning based computer vision tasks. Limited performance of algorithms trained solely on synthetic data has been approached with domain adaptation techniques such as the ones based on generative adversarial framework. We demonstrate how adversarial training alone can introduce semantic inconsistencies in translated images. To tackle this issue we propose density prematching strategy using KLIEP-based density ratio estimation procedure. Finally, we show that aforementioned strategy improves quality of translated images of underlying method and their usability for the semantic segmentation task in the context of autonomous driving.