CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation

TL;DR

CEREALS is an active learning strategy that reduces annotation effort in semantic segmentation by selectively labeling image regions, achieving high performance with significantly less human annotation.

Contribution

It introduces a novel region-based active learning method that minimizes annotation effort while maintaining segmentation accuracy.

Findings

Reduces annotation effort to 17% of full labeling

Maintains 95% of the full-data segmentation performance

Effective on Cityscapes dataset

Abstract

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.