Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime

— a novel method to progressive adapt the semantic models trained on daytime scenes to nighttime scenes via the bridge of twilight time

Dengxin Dai and Luc Van Gool

The 21st IEEE International Conference on Intelligent Transportation Systems, 2018.

Abstract

This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time — the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for knowledge transfer from daytime scenes to nighttime scenes, without using extra human annotation

Results

Note: due to GDPR (General Data Protection Regulation), the releasing of training data is delayed. We are working on it. Sorry for inconvenience.

This page has been edited by Dengxin Dai.