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The reliability of supervised machine learning systems depends on theaccuracy and availability of ground truth labels. However, the process of humanannotation, being prone to error, introduces the potential for noisy labels,which can impede the practicality of these systems. While training with noisylabels is a significant consideration, the reliability of test data is alsocrucial to ascertain the dependability of the results. A common approach toaddressing this issue is repeated labeling, where multiple annotators label thesame example, and their labels are combined to provide a better estimate of thetrue label. In this paper, we propose a novel localization algorithm thatadapts well-established ground truth estimation methods for object detectionand instance segmentation tasks. The key innovation of our method lies in itsability to transform combined localization and classification tasks intoclassification-only problems, thus enabling the application of techniques suchas Expectation-Maximization (EM) or Majority Voting (MJV). Although our mainfocus is the aggregation of unique ground truth for test data, our algorithmalso shows superior performance during training on the TexBiG dataset,surpassing both noisy label training and label aggregation using Weighted BoxesFusion (WBF). Our experiments indicate that the benefits of repeated labelsemerge under specific dataset and annotation configurations. The key factorsappear to be (1) dataset complexity, the (2) annotator consistency, and (3) thegiven annotation budget constraints.

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