论文标题
在自动驾驶汽车中识别交通现场的上下文了解的多任务学习
Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles
论文作者
论文摘要
需要各种视觉分类任务的交通现场识别是自动驾驶汽车的关键要素。但是，大多数现有的方法彼此独立对待每个相关任务，从不考虑整个系统。因此，它们仅限于为所有可能的推理时间任务使用特定任务的功能集，该功能忽略了利用手头任务的常见任务不变的上下文知识的能力。为了解决这个问题，我们提出了一种算法，通过采用多任务学习网络来共同学习特定于任务和共享的表示。具体而言，我们为共享特征嵌入和输入之间的相互信息约束提供了一个下限，该信息被认为能够跨任务提取常见的上下文信息，同时保留每个任务的基本信息。学习的表示形式在没有其他特定于任务的网络的情况下捕获了更丰富的上下文信息。大规模数据集HSD的广泛实验证明了我们网络比最新方法的有效性和优势。
Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevant task independently from one another, never considering the entire system as a whole. Because of this, they are limited to utilizing a task-specific set of features for all possible tasks of inference-time, which ignores the capability to leverage common task-invariant contextual knowledge for the task at hand. To address this problem, we propose an algorithm to jointly learn the task-specific and shared representations by adopting a multi-task learning network. Specifically, we present a lower bound for the mutual information constraint between shared feature embedding and input that is considered to be able to extract common contextual information across tasks while preserving essential information of each task jointly. The learned representations capture richer contextual information without additional task-specific network. Extensive experiments on the large-scale dataset HSD demonstrate the effectiveness and superiority of our network over state-of-the-art methods.
