Segment Anything Model (SAM) 在恶劣天气条件下自动驾驶中的鲁棒性
Robustness of Segment Anything Model (SAM) for Autonomous Driving in Adverse Weather Conditions
- Microsoft STCA（微软亚洲互联网工程院）
- Kyung Hee University（庆熙大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文研究SAM在自动驾驶恶劣天气下的鲁棒性，旨在集成前评估其表现，为未来应用提供见解。
AI中文摘要：
Segment Anything Model (SAM) 近期因其卓越的性能而引起了广泛关注，并已成为计算机视觉领域的基础模型。它已被集成到各种下游任务中，展示了其强大的零样本迁移能力。鉴于其令人印象深刻的表现，人们强烈希望将 SAM 应用于自动驾驶，以提升视觉任务的性能，尤其是在恶劣天气条件下驾驶等具有挑战性的场景中。然而，其在恶劣天气条件下的鲁棒性仍不确定。在这项工作中，我们研究了 SAM 在自动驾驶中的应用，并特别探讨了其在恶劣天气条件下的鲁棒性。总体而言，这项工作旨在将 SAM 集成到自动驾驶视觉任务之前，增强对其在挑战性场景中鲁棒性的理解，为未来的应用提供有价值的见解。
英文摘要：
Segment Anything Model (SAM) has gained considerable interest in recent times for its remarkable performance and has emerged as a foundational model in computer vision. It has been integrated in diverse downstream tasks, showcasing its strong zero-shot transfer capabilities. Given its impressive performance, there is a strong desire to apply SAM in autonomous driving to improve the performance of vision tasks, particularly in challenging scenarios such as driving under adverse weather conditions. However, its robustness under adverse weather conditions remains uncertain. In this work, we investigate the application of SAM in autonomous driving and specifically explore its robustness under adverse weather conditions. Overall, this work aims to enhance understanding of SAM's robustness in challenging scenarios before integrating it into autonomous driving vision tasks, providing valuable insights for future applications.
