Scaling up instance annotation via label propagation

TL;DR

This paper introduces a scalable annotation method that leverages hierarchical clustering and label propagation to efficiently generate large-scale object segmentation datasets with minimal manual effort.

Contribution

It presents a novel hierarchical clustering-based scheme for large-scale object mask annotation, significantly reducing manual annotation time while maintaining quality.

Findings

Generated 1 million masks in 290 hours

Reduced annotation time by 76 times

Achieved segmentation quality comparable to manual datasets

Abstract

Manually annotating object segmentation masks is very time-consuming. While interactive segmentation methods offer a more efficient alternative, they become unaffordable at a large scale because the cost grows linearly with the number of annotated masks. In this paper, we propose a highly efficient annotation scheme for building large datasets with object segmentation masks. At a large scale, images contain many object instances with similar appearance. We exploit these similarities by using hierarchical clustering on mask predictions made by a segmentation model. We propose a scheme that efficiently searches through the hierarchy of clusters and selects which clusters to annotate. Humans manually verify only a few masks per cluster, and the labels are propagated to the whole cluster. Through a large-scale experiment to populate 1M unlabeled images with object segmentation masks for 80 object classes, we show that (1) we obtain 1M object segmentation masks with an total annotation time of only 290 hours; (2) we reduce annotation time by 76x compared to manual annotation; (3) the segmentation quality of our masks is on par with those from manually annotated datasets. Code, data, and models are available online.