论文标题
不确定性感知的综合分段
Uncertainty-aware Panoptic Segmentation
论文作者
论文摘要
对于现代自治系统来说，可靠的场景理解是必不可少的。当前基于学习的方法通常试图根据仅考虑分割质量的细分指标来最大化其性能。但是，对于系统在现实世界中的安全操作，考虑预测的不确定性也至关重要。在这项工作中，我们介绍了不确定性感知的全景分割的新任务，该任务旨在预测每个像素语义和实例分割，以及每个像素不确定性估计值。我们定义了两个新颖的指标，以促进其定量分析，不确定性感知的综合质量（UPQ）和全景预期校准误差（PECE）。我们进一步提出了新颖的自上而下的证据分割网络（EVPSNET）来解决这一任务。我们的体系结构采用了一个简单而有效的全景融合模块，可利用预测的不确定性。此外，我们提供了几种强大的基线，将最新的全盘分割网络与无抽样的不确定性估计技术相结合。广泛的评估表明，我们的EVPSNET可以实现标准综合质量（PQ）的新最新技术，以及我们的不确定性倾斜度指标。我们将代码可用：\ url {https://github.com/kshitij3112/evpsnet}
Reliable scene understanding is indispensable for modern autonomous systems. Current learning-based methods typically try to maximize their performance based on segmentation metrics that only consider the quality of the segmentation. However, for the safe operation of a system in the real world it is crucial to consider the uncertainty in the prediction as well. In this work, we introduce the novel task of uncertainty-aware panoptic segmentation, which aims to predict per-pixel semantic and instance segmentations, together with per-pixel uncertainty estimates. We define two novel metrics to facilitate its quantitative analysis, the uncertainty-aware Panoptic Quality (uPQ) and the panoptic Expected Calibration Error (pECE). We further propose the novel top-down Evidential Panoptic Segmentation Network (EvPSNet) to solve this task. Our architecture employs a simple yet effective panoptic fusion module that leverages the predicted uncertainties. Furthermore, we provide several strong baselines combining state-of-the-art panoptic segmentation networks with sampling-free uncertainty estimation techniques. Extensive evaluations show that our EvPSNet achieves the new state-of-the-art for the standard Panoptic Quality (PQ), as well as for our uncertainty-aware panoptic metrics. We make the code available at: \url{https://github.com/kshitij3112/EvPSNet}
