This paper proposes a new active learning method for semantic segmentation.
The core of our method lies in a new annotation query design. It samples
informative local image regions (e.g., superpixels), and for each of such
regions, asks an oracle for a multi-hot vector indicating all classes existing
in the region. This multi-class labeling strategy is substantially more
efficient than existing ones like segmentation, polygon, and even dominant
class labeling in terms of annotation time per click. However, it introduces
the class ambiguity issue in training since it assigns partial labels (i.e., a
set of candidate classes) to individual pixels. We thus propose a new algorithm
for learning semantic segmentation while disambiguating the partial labels in
two stages. In the first stage, it trains a segmentation model directly with
the partial labels through two new loss functions motivated by partial label
learning and multiple instance learning. In the second stage, it disambiguates
the partial labels by generating pixel-wise pseudo labels, which are used for
supervised learning of the model. Equipped with a new acquisition function
dedicated to the multi-class labeling, our method outperformed previous work on
Cityscapes and PASCAL VOC 2012 while spending less annotation cost