Cost-aware Active Learning for Semantic Segmentation

Abstract

With the recent developments of deep learning, the performance of semantic segmentation has been greatly improved. Creating a large set of traning data requires high annotation costs. One of the ways to reduce the annotation cost is active learning, which selects the data that is uncertain for the current model. Most of the active learning methods assume that the annotation cost of each data is constant; however, the annotation cost varies according to the data. This paper proposes an active learning strategy to select image regions that are expected to be informative and the annotation cost of which is low. Our method predicts the annotation time of each region and combines it to the uncertainty to calculate the score. The results of our preliminary experiment demonstrate that the proposed method is able to reduce the annotation cost in the early stage of training.