Robust autonomous driving requires agents to accurately identify unexpected areas in urban scenes. To this end, some critical issues remain open: how to design advisable metric to measure anomalies, and how to properly generate training samples of anomaly data? Previous effort usually resorts to uncertainty estimation and sample synthesis from classification tasks, which ignore the context information and sometimes requires auxiliary datasets with fine-grained annotations. On the contrary, in this paper, we exploit the strong context-dependent nature of segmentation task and design an energy-guided self-supervised frameworks for anomaly segmentation, which optimizes an anomaly head by maximizing the likelihood of self-generated anomaly pixels. To this end, we design two estimators for anomaly likelihood estimation, one is a simple task-agnostic binary estimator and the other depicts anomaly likelihood as residual of task-oriented energy model. Based on proposed estimators, we further incorporate our framework with likelihood-guided mask refinement process to extract informative anomaly pixels for model training. We conduct extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks, demonstrating that without any auxiliary data or synthetic models, our method can still achieves competitive performance to other SOTA schemes.
翻译:快速自主驾驶要求代理商准确识别城市景色中的意外地区。 为此,一些关键问题仍然有待解决:如何设计衡量异常现象的明智衡量标准,以及如何适当生成异常数据的培训样本? 先前的努力通常采用分类任务中的不确定性估计和样本合成,这些任务忽视了背景信息,有时需要附带细微分化注释的辅助数据集。 相反,在本文件中,我们利用分解任务的强力因地制宜性质,为异常现象分化设计一种由能源指导的自我监督框架,通过尽量扩大自产异常现象像素的可能性,优化异常现象的头部。我们为此设计了两个用于异常可能性估计的估测器,一个是简单的任务-综合二进制估量器,另一个是描述以任务为导向的能源模型剩余可能的异常可能性。根据拟议的估计器,我们进一步采用我们的框架,用可能受导的掩码改进程序,为模型培训提取信息性异常现象。我们进行了广泛的反常态试验,通过尽量提高自生异常现象的像素的可能性。我们为异常现象基准设计了两种估测算器,一个简单的任务-,即二进取半成像仪,我们的方法仍然可以达到其他的SOTA。