Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels

TL;DR

This paper introduces a novel localization algorithm that enhances object detection and segmentation by effectively aggregating repeated noisy labels, improving reliability in test data and training scenarios under certain conditions.

Contribution

It adapts ground truth estimation methods for object detection, transforming localization and classification into classification-only problems for better label aggregation.

Findings

The algorithm outperforms noisy label training and Weighted Boxes Fusion on the TexBiG dataset.

Repeated labeling benefits depend on dataset complexity, annotator consistency, and annotation budget.

The method improves test data reliability and training performance in specific annotation settings.

Abstract

The reliability of supervised machine learning systems depends on the accuracy and availability of ground truth labels. However, the process of human annotation, being prone to error, introduces the potential for noisy labels, which can impede the practicality of these systems. While training with noisy labels is a significant consideration, the reliability of test data is also crucial to ascertain the dependability of the results. A common approach to addressing this issue is repeated labeling, where multiple annotators label the same example, and their labels are combined to provide a better estimate of the true label. In this paper, we propose a novel localization algorithm that adapts well-established ground truth estimation methods for object detection and instance segmentation tasks. The key innovation of our method lies in its ability to transform combined localization and classification tasks into classification-only problems, thus enabling the application of techniques such as Expectation-Maximization (EM) or Majority Voting (MJV). Although our main focus is the aggregation of unique ground truth for test data, our algorithm also shows superior performance during training on the TexBiG dataset, surpassing both noisy label training and label aggregation using Weighted Boxes Fusion (WBF). Our experiments indicate that the benefits of repeated labels emerge under specific dataset and annotation configurations. The key factors appear to be (1) dataset complexity, the (2) annotator consistency, and (3) the given annotation budget constraints.