Universal Noise Annotation: Unveiling the Impact of Noisy annotation on
  Object Detection
Kwangrok Ryoo Yeonsik Jo Seungjun Lee Mira Kim Ahra Jo Seung Hwan Kim Seungryong Kim Soonyoung Lee
For object detection task with noisy labels, it is important to consider not
only categorization noise, as in image classification, but also localization
noise, missing annotations, and bogus bounding boxes. However, previous studies
have only addressed certain types of noise (e.g., localization or
categorization). In this paper, we propose Universal-Noise Annotation (UNA), a
more practical setting that encompasses all types of noise that can occur in
object detection, and analyze how UNA affects the performance of the detector.
We analyzed the development direction of previous works of detection algorithms
and examined the factors that impact the robustness of detection model learning
method. We open-source the code for injecting UNA into the dataset and all the
training log and weight are also shared.