DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

by   Xinyi Wu, et al.

Semantic segmentation of nighttime images plays an equally important role as that of daytime images in autonomous driving, but the former is much more challenging due to poor illuminations and arduous human annotations. In this paper, we propose a novel domain adaptation network (DANNet) for nighttime semantic segmentation without using labeled nighttime image data. It employs an adversarial training with a labeled daytime dataset and an unlabeled dataset that contains coarsely aligned day-night image pairs. Specifically, for the unlabeled day-night image pairs, we use the pixel-level predictions of static object categories on a daytime image as a pseudo supervision to segment its counterpart nighttime image. We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects. The proposed DANNet is the first one stage adaptation framework for nighttime semantic segmentation, which does not train additional day-night image transfer models as a separate pre-processing stage. Extensive experiments on Dark Zurich and Nighttime Driving datasets show that our method achieves state-of-the-art performance for nighttime semantic segmentation.


page 1

page 3

page 4

page 7

page 8


Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation

In autonomous driving, learning a segmentation model that can adapt to v...

GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data

Semantic segmentation for autonomous driving should be robust against va...

Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

We address the problem of semantic nighttime image segmentation and impr...

Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters

Semantic segmentation on driving-scene images is vital for autonomous dr...

Calibrated Adversarial Refinement for Multimodal Semantic Segmentation

Ambiguities in images or unsystematic annotation can lead to multiple va...

I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation

Adversarial training has been recently employed for realizing structured...

NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night

The semantic segmentation of nighttime scenes is a challenging problem t...

Please sign up or login with your details

Forgot password? Click here to reset