This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural adversarial scenarios. The report presents the results of 19 submitted entries, with numerous techniques drawing inspiration from cutting-edge uncertainty quantification methodologies presented at prominent conferences in the fields of computer vision and machine learning and journals over the past few years. Within this document, the challenge is introduced, shedding light on its purpose and objectives, which primarily revolved around enhancing the robustness of semantic segmentation in urban scenes under varying natural adversarial conditions. The report then delves into the top-performing solutions. Moreover, the document aims to provide a comprehensive overview of the diverse solutions deployed by all participants. By doing so, it seeks to offer readers a deeper insight into the array of strategies that can be leveraged to effectively handle the inherent uncertainties associated with autonomous driving and semantic segmentation, especially within urban environments.

该论文总结了在ICCV 2023举行的MUAD不确定性量化挑战中采用的获胜解决方案，重点关注城市环境中的语义分割，特别关注自然对抗情景。报告呈现了19个提交条目的结果，许多技术借鉴了近几年在计算机视觉和机器学习领域的重要会议和期刊中介绍的前沿不确定性量化方法。在文中介绍了挑战的目的和目标，主要集中在增强城市场景下语义分割在不同的自然对抗条件下的鲁棒性。报告还深入探讨了表现最好的解决方案，并旨在全面概述所有参与者采用的不同解决方案，以便为读者提供更深入的洞察力，了解在自动驾驶和语义分割中与城市环境中固有不确定性处理相关的各种策略。

鲁棒语义分割UNCV2023挑战结果