Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
This addresses safety-critical applications like autonomous driving by improving OOD detection in multimodal settings, though it is incremental as it builds on prior unimodal approaches.
The paper tackles the problem of multimodal out-of-distribution detection and segmentation by proposing Feature Mixing, a simple and fast method for outlier synthesis, which achieves state-of-the-art performance with speedups of 10x to 370x on benchmarks like SemanticKITTI and nuScenes.
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.