We present a novel region based active learning method for semantic image
segmentation, called MetaBox+. For acquisition, we train a meta regression
model to estimate the segment-wise Intersection over Union (IoU) of each
predicted segment of unlabeled images. This can be understood as an estimation
of segment-wise prediction quality. Queried regions are supposed to minimize to
competing targets, i.e., low predicted IoU values / segmentation quality and
low estimated annotation costs. For estimating the latter we propose a simple
but practical method for annotation cost estimation. We compare our method to
entropy based methods, where we consider the entropy as uncertainty of the
prediction. The comparison and analysis of the results provide insights into
annotation costs as well as robustness and variance of the methods. Numerical
experiments conducted with two different networks on the Cityscapes dataset
clearly demonstrate a reduction of annotation effort compared to random
acquisition. Noteworthily, we achieve 95%of the mean Intersection over Union
(mIoU), using MetaBox+ compared to when training with the full dataset, with
only 10.47% / 32.01% annotation effort for the two networks, respectively.

本文提出一种新颖的区域活性学习方法 MetaBox + 用于语义图像分割，该方法采用元回归模型训练估计未标注图像中每个预测分割的交集联合（IoU），并提出了一种简单但实用的注释成本估计方法，通过对基于熵的方法进行比较和分析，本文获得了新的关于注释成本、鲁棒性和方法方差的见解，并在 Cityscapes 数据集上进行了数值实验，明显证明了与随机获得相比，MetaBox + 减少了注释工作量，并且只有两个不同网络的 10.47％/32.01％注释工作量的情况下，相比于完整数据集训练可以达到 95％的平均交集联合 (mIoU)。