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 embodiment relates to a method and apparatus for performing a box drawing task for data labeling, which provide a user interface for an interaction with a user to perform a data labeling-associated task corresponding to drawing of a box on an object in a photo and, on the basis of same, allow high-quality training data to be secured.

Abstract: According to the present invention, proposed is a method for inspecting a labeling operation, the method comprising, when a deep learning model for inspecting a labeling operation for a bounding box corresponding to an object included in an image is present and a computing apparatus uses the deep learning model, the steps of: performing, by the computing apparatus, first training on the deep learning model on the basis of a training image; obtaining, by the computing apparatus, an operation image and a bounding box labeling value therefor; calculating, by the computing apparatus, a score for inspection by performing a calculation while passing the operation image and the bounding box labeling value through the deep learning model; and determining, by the computing apparatus, whether the bounding box labeling value for the operation image is accurate on the basis of the score for inspection and performing any one of a pass process, a fail process, and a re-inspection process.

Abstract: According to the present invention, provided is a method for collecting filtered text data, comprising the steps in which: a computing device acquires first text data and records the first text data in a text data pool; the computing device acquires second text data; the computing device performs a calculation in a deep learning model by using the first text data and the second text data as input values and calculates a first feature vector corresponding to the first text data and a second feature vector corresponding to the second text data; and the computing device compares the degree of similarity between the first feature vector and the second feature vector, and records the second text data in the text data pool when the degree of similarity is less than a predetermined value.

Abstract: According to the present invention, proposed is a method for collecting filtered image data, the method comprising the steps of: obtaining, by a computing apparatus, first image data and recording the first image data in an image data pool; obtaining, by the computing apparatus, second image data; performing, by the computing apparatus, an operation in a deep learning model by using the first image data and the second image data as input values, and calculating a first feature vector corresponding to the first image data and a second feature vector corresponding to the second image data; and comparing, by the computing apparatus, a similarity between the first feature vector and the second feature vector, and when the similarity is less than a certain value, recording the second image data in the image data pool.