CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation
This reduces annotation costs for semantic segmentation tasks, making it more accessible for applications like autonomous driving, though it is incremental as it builds on existing active learning methods.
The paper tackles the high cost of fully labeling images for semantic segmentation by proposing CEREALS, an active learning strategy that selects specific regions for human annotation, reducing annotation effort to 17% while maintaining 95% of the mIoU performance on Cityscapes.
State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images. However, gathering such a corpus is expensive, due to human annotation effort, in contrast to gathering unlabeled data. We propose an active learning-based strategy, called CEREALS, in which a human only has to hand-label a few, automatically selected, regions within an unlabeled image corpus. This minimizes human annotation effort while maximizing the performance of a semantic image segmentation method. The automatic selection procedure is achieved by: a) using a suitable information measure combined with an estimate about human annotation effort, which is inferred from a learned cost model, and b) exploiting the spatial coherency of an image. The performance of CEREALS is demonstrated on Cityscapes, where we are able to reduce the annotation effort to 17%, while keeping 95% of the mean Intersection over Union (mIoU) of a model that was trained with the fully annotated training set of Cityscapes.