Active learning automatically selects samples for annotation from a data pool to achieve maximum performance with minimum annotation cost. This is particularly critical for semantic segmentation, where annotations are costly. In this work, we show in the context of semantic segmentation that the data distribution is decisive for the performance of the various active learning objectives proposed in the literature. Particularly, redundancy in the data, as it appears in most driving scenarios and video datasets, plays a large role. We demonstrate that the integration of semi-supervised learning with active learning can improve performance when the two objectives are aligned. Our experimental study shows that current active learning benchmarks for segmentation in driving scenarios are not realistic since they operate on data that is already curated for maximum diversity. Accordingly, we propose a more realistic evaluation scheme in which the value of active learning becomes clearly visible, both by itself and in combination with semi-supervised learning.

翻译：主动学习从数据池中自动选择样本进行标注，以期用最少的标注成本获得最优性能。这一点对于标注成本高昂的语义分割任务尤为关键。在本研究中，我们表明，在语义分割的背景下，数据分布对于文献中提出的各种主动学习目标的性能具有决定性作用。尤其值得注意的是，大多数驾驶场景和视频数据集中存在的冗余性发挥了重要作用。我们证明，当半监督学习与主动学习的目标一致时，两者的结合能够提升性能。我们的实验研究表明，当前针对驾驶场景分割的主动学习基准并不现实，因为这些基准所操作的数据已经过预先筛选，具有最大多样性。为此，我们提出了一种更真实的评估方案，在该方案中，主动学习（无论是单独使用还是与半监督学习结合）的价值得以清晰显现。