Abstract: The present invention relates to a method and a device of inputting annotation of object boundary information, and more particularly, to a method and a device of inputting annotation of object boundary information such that workers or the like efficiently and accurately input object boundary information in a service such as crowding sourcing, and preliminary work is assisted by an artificial neural network-based algorithm.

Abstract: The present invention relates to a method, a service server, and a computer-readable medium for visualizing data to select data to be used for labeling, and more particularly, to a method, a service server, and a computer-readable medium for visualizing data to select data to be used for labeling, capable of enabling a user to select data to be used as learning data by deriving a plurality of feature vector for a plurality of data included in an original dataset, reducing dimensions of each of the feature vectors to three or less dimensions, displaying the feature vectors with the reduced dimensions on a plane of the three or less dimensions, and providing a visualization interface including the plane of the three or less dimensions to a user terminal.

Abstract: The present invention relates to a method, a service server, and a computer-readable medium for selectively extracting data for labeling, and more particularly, to a method, a service server, and a computer-readable medium for selectively extracting data for labeling, capable of selectively providing data for effectively training a machine learning model that is desired to be achieved by a user by deriving a plurality of feature vectors for a plurality of data included in an original dataset, applying a preset rule to the feature vectors to select a preset number of feature vectors among the feature vectors, and plotting the selected feature vectors and unselected feature vectors on a plane of three or less dimensions to provide a plotting result to the user.

Abstract: The present invention proposes a method for distributing labeling work, wherein a computing apparatus uses a deep learning model performing bounding box labeling work, in order to find the position of an object included in an image and classify the type of the object, the method comprising the steps of: obtaining a predetermined image including at least one object by the computing apparatus; performing calculation, by the computing apparatus, while passing the predetermined image through the deep learning model, to obtain i) the coordinates of a bounding box with respect to the at least one object, ii) a classification value indicating the type of the at least one object, and iii) a loss value indicating the degree of error of the obtained bounding box; and determining, by the computing apparatus, difficulty levels of labeling work on the basis of the loss value and the classification value, and distributing the labeling work to workers according to the determined difficulty levels.

Abstract: The present invention relates to a method and a device of inputting annotation of object boundary information, and more particularly, to a method and a device of inputting annotation of object boundary information such that workers or the like efficiently and accurately input object boundary information in a service such as crowding sourcing, and preliminary work is assisted by an artificial neural network-based algorithm.

Abstract: Disclosed is a computer-readable medium including a program code that, when executed by processing circuitry, causes the processing circuitry to generate a feature map from an input image, to extract a region of interest from the feature map, and to generate a predicted mask based on the region of interest. The processing circuitry may use a predicted mask and a real mask to learn a convolutional neural network system. The real mask includes first pixels corresponding to the real boundary and second pixels corresponding to a fake boundary adjacent to the real boundary.

Abstract: Disclosed is a computer-readable medium including a program code that, when executed by processing circuitry, causes the processing circuitry to generate a feature map from an input image, to extract a region of interest from the feature map, and to generate a predicted mask based on the region of interest. The processing circuitry may use a predicted mask and a real mask to learn a convolutional neural network system. The real mask includes first pixels corresponding to the real boundary and second pixels corresponding to a fake boundary adjacent to the real boundary.