Autonomous robots deal with unexpected scenarios in real environments. Given
input images, various visual perception tasks can be performed, e.g., semantic
segmentation, depth estimation and normal estimation. These different tasks
provide rich information for the whole robotic perception system. All tasks
have their own characteristics while sharing some latent correlations. However,
some of the task predictions may suffer from the unreliability dealing with
complex scenes and anomalies. We propose an attention-based failure detection
approach by exploiting the correlations among multiple tasks. The proposed
framework infers task failures by evaluating the individual prediction, across
multiple visual perception tasks for different regions in an image. The
formulation of the evaluations is based on an attention network supervised by
multi-task uncertainty estimation and their corresponding prediction errors.
Our proposed framework generates more accurate estimations of the prediction
error for the different task's predictions.

本文提出一种基于关注机制的多任务联合失败检测方法，该方法能够评估多个视觉感知任务在图像不同区域的预测准确率和失效率，利用监督式多任务不确定性估计及其对应的预测误差，实现更准确的预测误差估计。