标题： 探索基于区域的主动学习的空间多样性 显示英文标题

标题： Exploring Spatial Diversity for Region-based Active Learning

摘要： 当前最先进的语义分割方法基于在大规模标注数据集上训练的深度神经网络。获取这样的数据集会产生较大的标注成本，尤其是在密集像素级预测任务如语义分割中。我们考虑基于区域的主动学习作为一种在保持高性能的同时减少标注成本的策略。在这种情况下，选择有信息量的图像区域而不是整个图像进行标注。重要的是，我们认为在这种情况下的主动学习中，强制局部空间多样性是有益的，并且为了将空间多样性与传统的主动选择标准（例如数据样本不确定性）结合在一个统一的优化框架中，用于基于区域的主动学习。我们将这个框架应用于Cityscapes和PASCAL VOC数据集，并证明空间多样性的引入有效提高了基于不确定性和特征多样性的主动学习方法的性能。我们的框架仅使用了$95\%$的标注像素就达到了完全监督方法的$5-9\%$性能，超过了所有最先进的语义分割基于区域的主动学习方法。 显示英文摘要

摘要： State-of-the-art methods for semantic segmentation are based on deep neural networks trained on large-scale labeled datasets. Acquiring such datasets would incur large annotation costs, especially for dense pixel-level prediction tasks like semantic segmentation. We consider region-based active learning as a strategy to reduce annotation costs while maintaining high performance. In this setting, batches of informative image regions instead of entire images are selected for labeling. Importantly, we propose that enforcing local spatial diversity is beneficial for active learning in this case, and to incorporate spatial diversity along with the traditional active selection criterion, e.g., data sample uncertainty, in a unified optimization framework for region-based active learning. We apply this framework to the Cityscapes and PASCAL VOC datasets and demonstrate that the inclusion of spatial diversity effectively improves the performance of uncertainty-based and feature diversity-based active learning methods. Our framework achieves $95\%$ performance of fully supervised methods with only $5-9\%$ of the labeled pixels, outperforming all state-of-the-art region-based active learning methods for semantic segmentation.