Abstract: An apparatus of labeling for object detection according to an embodiment of the present disclosure includes an image selector that determines a plurality of labeling target images from among a plurality of unlabeled images, and determines a labeling order of the plurality of labeling target images, a feedback obtainer that obtains label inspection information on the plurality of labeling target images from a user, and a model trainer that learns the label inspection information input from the user by using the labeling target images, obtains a pseudo label for supervised learning based on a learning result using the label inspection information, and re-determines the labeling order of the labeling target images based on the pseudo label.

Abstract: An image segmentation model training apparatus according to a disclosed embodiment includes a predictor, which generates a plurality of original masks using an unlabeled original image set and a pre-trained image segmentation model, a label generator, which generates a synthesized image set based on the original image set and the plurality of original masks, and generates a plurality of pseudo labels based on the synthesized image set, a pre-processor, which generates a training image set by performing pseudo labeling on the original image set and the synthesized image set using the plurality of pseudo labels, and a model trainer, which further trains the image segmentation model based on the training image set.

Abstract: An apparatus of labeling for object detection according to an embodiment of the present disclosure includes an image selector that determines a plurality of labeling target images from among a plurality of unlabeled images, and determines a labeling order of the plurality of labeling target images, a feedback obtainer that obtains label inspection information on the plurality of labeling target images from a user, and a model trainer that learns the label inspection information input from the user by using the labeling target images, obtains a pseudo label for supervised learning based on a learning result using the label inspection information, and re-determines the labeling order of the labeling target images based on the pseudo label.

Abstract: An apparatus for labeling data according to an embodiment of the present disclosure includes a data acquisitor that acquires a plurality of unlabeled data, a predicted label acquisitor that acquires predicted labels for the unlabeled data from a plurality of pre-training models pre-trained under different learning schemes, a sampler that selects a part of the unlabeled data as an initial review target, an initial review label acquisitor that acquires an initial review label for the part of the unlabeled data from a user, a model trainer that trains a labeling model based on the part of the unlabeled data, predicted labels for the part of the unlabeled data, and the initial review label, and a predictor that predicts labels of a part of the remaining unlabeled data excluding the part of the unlabeled data by applying the labeling model to the part of the remaining unlabeled data.

Abstract: An image segmentation model training apparatus according to a disclosed embodiment includes a predictor, which generates a plurality of original masks using an unlabeled original image set and a pre-trained image segmentation model, a label generator, which generates a synthesized image set based on the original image set and the plurality of original masks, and generates a plurality of pseudo labels based on the synthesized image set, a pre-processor, which generates a training image set by performing pseudo labeling on the original image set and the synthesized image set using the plurality of pseudo labels, and a model trainer, which further trains the image segmentation model based on the training image set.