Image segmentation is a common and challenging task in autonomous driving. Availability of sufficient pixel-level annotations for the training data is a hurdle. Active learning helps learning from small amounts of data by suggesting the most promising samples for labeling. In this work, we propose a new pool-based method for active learning, which proposes promising image regions, in each acquisition step. The problem is framed in an exploration-exploitation framework by combining an embedding based on Uniform Manifold Approximation to model representativeness with entropy as uncertainty measure to model informativeness. We applied our proposed method to the challenging autonomous driving data sets CamVid and Cityscapes and performed a quantitative comparison with state-of-the-art methods. We find that our active learning method achieves better performance on CamVid compared to other methods, while on Cityscapes, the performance lift was negligible.
翻译:在自主驱动中,图像分割是一项常见且具有挑战性的任务。 培训数据有足够的像素级说明是一个障碍。 积极学习通过推荐最有希望的标签样本,有助于从少量数据中学习。 在这项工作中,我们提出了一种新的基于集合的积极学习方法,在每一获取步骤中提出有希望的图像区域。 这个问题在探索-开发框架中被设计成一个基于统一人工操作的嵌入式,以模型代表性为模型,作为模型信息性能的不确定性衡量尺度。 我们在具有挑战性的自动驱动数据集 CamVid 和 Cityscaps 中应用了我们建议的方法,并与最新方法进行了定量比较。 我们发现,我们的积极学习方法在CamVid 与其他方法相比取得了更好的业绩,而在城市景观中,性能提升是微不足道的。