Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation
This addresses the challenge of adapting segmentation models from daytime to nighttime conditions in autonomous driving, which is crucial for safety but incremental in method.
The paper tackles the problem of Domain Adaptive Nighttime Semantic Segmentation (DANSS) for autonomous driving by proposing a Bidirectional Mixing (Bi-Mix) framework, which improves nighttime image relighting and bridges the day-night distribution gap, achieving state-of-the-art performance on Dark Zurich and Nighttime Driving datasets.
In autonomous driving, learning a segmentation model that can adapt to various environmental conditions is crucial. In particular, copying with severe illumination changes is an impelling need, as models trained on daylight data will perform poorly at nighttime. In this paper, we study the problem of Domain Adaptive Nighttime Semantic Segmentation (DANSS), which aims to learn a discriminative nighttime model with a labeled daytime dataset and an unlabeled dataset, including coarsely aligned day-night image pairs. To this end, we propose a novel Bidirectional Mixing (Bi-Mix) framework for DANSS, which can contribute to both image translation and segmentation adaptation processes. Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting. On the other hand, in the segmentation adaptation stage, Bi-Mix effectively bridges the distribution gap between day and night domains for adapting the model to the night domain. In both processes, Bi-Mix simply operates by mixing two samples without extra hyper-parameters, thus it is easy to implement. Extensive experiments on Dark Zurich and Nighttime Driving datasets demonstrate the advantage of the proposed Bi-Mix and show that our approach obtains state-of-the-art performance in DANSS. Our code is available at https://github.com/ygjwd12345/BiMix.