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A Novel Benchmark for Refinement of Noisy Localization Labels in Autolabeled Datasets for Object Detection
Autolabeling approaches are attractive w.r.t. time and cost as they allow fast annotation without human intervention. However, can we really trust the label quality of autolabeling? And further, which potential consequences arise from resulting label noise? In this work, we address these questions for localization, a subtask of object detection, by investigating the effects on a state-of-the-art deep neural network (DNN) for object detection and the widely used Pascal VOC 2012 dataset. Our contributions are threefold: First, we propose a method to inject noise into localization labels, enabling us to simulate localization label errors of autolabeling methods. Afterwards, we train a state-of-the-art object detection DNN with these noisy labels. Second, we propose a refinement network which takes a noisy localization label and its respective image as input and performs a localization refinement. Third, we again train a state-of-the-art object detection DNN, however, this time with refined localization labels. Our insights are: Training a state-of-the-art DNN for object detection on noisy localization labels leads to a severe performance drop. Our proposed localization label refinement network is able to refine the noisy localization labels. We are able to retain the performance to some extent by retraining the state-of-the-art DNN for object detection on the refined localization labels. Our study motivates a new challenging task "refinement of noisy localization labels" and sets a first benchmark for Pascal VOC 2012. Code is available at https://github.com/ifnspaml/LocalizationLabelNoise.