Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting …
This work investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which …
Most nighttime semantic segmentation studies are based on domain adaptation approaches and image input. However, limited by the low dynamic range of conventional cameras …
H Gao, J Guo, G Wang… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
The performance of nighttime semantic segmentation is restricted by the poor illumination and a lack of pixel-wise annotation, which severely limit its application in autonomous …
The performance of nighttime semantic segmentation has been significantly improved thanks to recent unsupervised methods. However, these methods still suffer from complex …
The recent advances in deep neural networks have convincingly demonstrated high capability in learning vision models on large datasets. Nevertheless, collecting expert …
Q Xu, Y Ma, J Wu, C Long… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Autonomous driving needs to ensure all-weather safety, especially in unfavorable environments such as night and rain. However, the current daytime-trained semantic …
G Yang, Z Zhong, H Tang, M Ding, N Sebe… - arXiv preprint arXiv …, 2021 - arxiv.org
In autonomous driving, learning a segmentation model that can adapt to various environmental conditions is crucial. In particular, copying with severe illumination changes is …
Comprehensive semantic segmentation is one of the key components for robust scene understanding and a requirement to enable autonomous driving. Driven by large scale …