Methods for training auto labeling device and performing auto labeling related to segmentation while performing automatic verification by using uncertainty scores and devices using the same

Abstract

A method for training an auto labeling device performing automatic verification using uncertainty of labels is provided. The method includes steps of: a learning device (a) (i) inputting unlabeled training images into (i-1) an object detection network to generate bounding boxes for training and (i-2) a convolution network to generate feature maps for training, and (ii) allowing an ROI pooling layer to generate pooled feature maps for training and inputting the pooled feature maps for training into a deconvolution network to generate segmentation masks for training; and (b) (i) inputting the pooled feature maps for training into at least one of (i-1) a first classifier to generate first per-pixel class scores for training and first mask uncertainty scores for training and (i-2) a second classifier to generate second per-pixel class scores for training and second mask uncertainty scores for training and (ii) training the first classifier or the second classifier.