极简多模态异常合成用于分布外检测与分割
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
AI总结：
本文提出极简多模态异常合成方法Feature Mixing，通过理论支持提升OOD检测性能，实验表明其在多个数据集上达到最优效果，速度提升显著。
摘要：
分布外（OOD）检测和分割对于在安全关键应用中部署机器学习模型至关重要，如自动驾驶和机器人辅助手术。尽管先前研究主要集中在单模态图像数据上，但现实应用本质上是多模态的，需要整合多种模态以提高OOD检测效果。一个关键挑战是未知数据缺乏监督信号，导致对OOD样本的预测过于自信。为了解决这一挑战，我们提出了特征混合（Feature Mixing），一种极简且快速的多模态异常合成方法，具有理论支持，可以进一步优化以帮助模型更好地区分分布内（ID）和OOD数据。特征混合是模态无关的，适用于各种模态组合。此外，我们引入了CARLA-OOD，一个新型的多模态OOD分割数据集，包含在多样场景和天气条件下合成的OOD对象。在SemanticKITTI、nuScenes、CARLA-OOD数据集和MultiOOD基准上的大量实验表明，特征混合在性能上达到最先进的水平，速度提升达到10倍到370倍。我们的源代码和数据集将在https://github.com/mona4399/FeatureMixing上提供。
英文摘要：
Out-of-distribution (OOD) detection and segmentation are crucial for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted surgery. While prior research has primarily focused on unimodal image data, real-world applications are inherently multimodal, requiring the integration of multiple modalities for improved OOD detection. A key challenge is the lack of supervision signals from unknown data, leading to overconfident predictions on OOD samples. To address this challenge, we propose Feature Mixing, an extremely simple and fast method for multimodal outlier synthesis with theoretical support, which can be further optimized to help the model better distinguish between in-distribution (ID) and OOD data. Feature Mixing is modality-agnostic and applicable to various modality combinations. Additionally, we introduce CARLA-OOD, a novel multimodal dataset for OOD segmentation, featuring synthetic OOD objects across diverse scenes and weather conditions. Extensive experiments on SemanticKITTI, nuScenes, CARLA-OOD datasets, and the MultiOOD benchmark demonstrate that Feature Mixing achieves state-of-the-art performance with a $10 \times$ to $370 \times$ speedup. Our source code and dataset will be available at https://github.com/mona4399/FeatureMixing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
