解决异常物体定位难题，让AI能精准识别并分割未知异常
From Pixel to Mask: A Survey of Out-of-Distribution Segmentation
- 按测试时、训练暴露、重建和大模型四类梳理方法
- 强调自动驾驶中像素级异常定位对安全的关键作用
- 适合关注AI鲁棒性与感知安全的研究者阅读
随着对人工智能安全性的关注上升，异常分布（OoD）检测与分割受到越来越多重视。传统OoD检测仅能识别异常存在，缺乏空间定位能力，限制了其在下游任务中的应用。OoD分割通过像素级定位异常物体，弥补这一缺陷。该能力对自动驾驶等安全关键场景尤为重要，使感知模块不仅能检测异常，还能精准分割，支持针对性控制决策，提升系统整体鲁棒性。本文将现有OoD分割方法分为四类：(i) 测试时OoD分割，(ii) 基于异常暴露的监督训练，(iii) 基于重建的方法，(iv) 利用强大模型的方法。系统综述自动驾驶场景下OoD分割的最新进展，识别新兴挑战，并探讨有前景的未来研究方向。
原文摘要 · Abstract (English)
Out-of-distribution (OoD) detection and segmentation have attracted growing attention as concerns about AI security rise. Conventional OoD detection methods identify the existence of OoD objects but lack spatial localization, limiting their usefulness in downstream tasks. OoD segmentation addresses this limitation by localizing anomalous objects at pixel-level granularity. This capability is crucial for safety-critical applications such as autonomous driving, where perception modules must not only detect but also precisely segment OoD objects, enabling targeted control actions and enhancing overall system robustness. In this survey, we group current OoD segmentation approaches into four categories: (i) test-time OoD segmentation, (ii) outlier exposure for supervised training, (iii) reconstruction-based methods, (iv) and approaches that leverage powerful models. We systematically review recent advances in OoD segmentation for autonomous-driving scenarios, identify emerging challenges, and discuss promising future research directions.
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