Dual-Path Consistency Unsupervised Domain Adaptation for Nighttime Semantic Segmentation

Abstract

Nighttime semantic segmentation is an indispensable component in practical applications, such as automated vehicles. However, it is often hindered by the lack of annotations due to interference caused by inadequate lighting or exposure. To overcome these difficulties, we propose a Dual-Path Consistency (DPC) unsupervised domain adaptation (UDA) approach. One path is Image Darkening Path (IDP), in which feature representations of original images and darkened images extracted from the feature encoder are leveraged to maintain cross-domain style consistency. Another path is Image Masking Path (IMP), in which the masked images are reconstructed under the guidance of pseudo-labels, aiming to maintain content consistency in an entirely identical scenario. Extensive experiments on four bench-marks demonstrate the superior performance of the proposed DPC for nighttime semantic segmentation.