论文标题
CLAD：自动驾驶的现实持续学习基准
CLAD: A realistic Continual Learning benchmark for Autonomous Driving
论文作者
论文摘要
在本文中，我们描述了促使新的自动驾驶持续学习基准（CLAD）的设计和想法，该基准着重于对象分类和对象检测的问题。基准测试利用SODA10M，这是一个最近发布的大规模数据集，涉及与自动驾驶有关的问题。首先，我们审查并讨论现有的持续学习基准，它们如何相关，并表明大多数是持续学习的极端情况。为此，我们调查了在三个高级计算机视觉会议上连续学习论文中使用的基准。接下来，我们介绍了Clad-C，这是一种在线分类基准，该基准通过按时间顺序数据流实现，既带来了类和域的增量挑战；和clad-d，域增量连续对象检测基准。我们通过对ICCV 2021的Clad-Challenge研讨会中的Top-3参与者使用的技术和方法进行的调查来研究基准构成的固有困难和挑战。我们以改善当前持续学习状态的途径以及我们对未来研究的有前途的方向来结束。
In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection. The benchmark utilises SODA10M, a recently released large-scale dataset that concerns autonomous driving related problems. First, we review and discuss existing continual learning benchmarks, how they are related, and show that most are extreme cases of continual learning. To this end, we survey the benchmarks used in continual learning papers at three highly ranked computer vision conferences. Next, we introduce CLAD-C, an online classification benchmark realised through a chronological data stream that poses both class and domain incremental challenges; and CLAD-D, a domain incremental continual object detection benchmark. We examine the inherent difficulties and challenges posed by the benchmark, through a survey of the techniques and methods used by the top-3 participants in a CLAD-challenge workshop at ICCV 2021. We conclude with possible pathways to improve the current continual learning state of the art, and which directions we deem promising for future research.
