Assessing the reliability of perception models to covariate shifts and out-of-distribution (OOD) detection is crucial for safety-critical applications such as autonomous vehicles. By nature of the task, however, the relevant data is difficult to collect and annotate. In this paper, we challenge cutting-edge generative models to automatically synthesize data for assessing reliability in semantic segmentation. By fine-tuning Stable Diffusion, we perform zero-shot generation of synthetic data in OOD domains or inpainted with OOD objects. Synthetic data is employed to provide an initial assessment of pretrained segmenters, thereby offering insights into their performance when confronted with real edge cases. Through extensive experiments, we demonstrate a high correlation between the performance on synthetic data and the performance on real OOD data, showing the validity approach. Furthermore, we illustrate how synthetic data can be utilized to enhance the calibration and OOD detection capabilities of segmenters.

本文挑战了最先进的生成模型，通过对稳定扩散进行微调，在语义分割方面自动生成合成数据以评估其可靠性。通过广泛实验，我们表明合成数据与真实异常数据的表现之间存在高度相关性，从而显示了该方法的有效性。此外，我们说明了如何利用合成数据来提高分割器的校准和异常检测能力。

语义分割中的可靠性问题：我们能否使用合成数据？