In a learning apparatus, an acquisition unit acquires image data and label data corresponding to the image data. An object candidate extraction unit extracts each object candidate rectangle from the image data. A correct answer data generation unit generates a background object label corresponding to each background object included in each object candidate rectangle as correct answer data corresponding to the object candidate rectangle by using the label data. A prediction unit predicts a classification using each object candidate rectangle and outputs a prediction result. An optimization unit optimizes the object candidate extraction unit and the prediction unit using the prediction result and the correct answer data.

A system, method, and computer-readable medium for associating a person’s gestures with specific features of objects is disclosed. Using one or more image capture devices, a person’s gestures and the location of that person in an environment is determined. Using determined distances between the person and objects in the environment and scales associated with features of those objects, the list of specific features in the person’s field-of-view may be determined. Further, a facial expression of the person may be scored and that score associated with one or more specific features.

Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.

Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.

A method for optimizing a process of displaying video streams with specified event receives video streams. The video streams are sequenced based on a specified arrangement role to from a video stream queue. By analyzing each video streams, whether each video stream includes a specified event is determined. If each video stream without the specified event, the video streams are outputted based on the video stream queue. If the video stream includes the specified event, the video stream with the specified event is adjusted to be priority in the video stream queue, and the video streams are outputted based on the updated video stream queue. The video streams with the specified event can be prioritized processed and focus. A video stream processing apparatus and a computer readable storage medium applying the method are also provided.

A method for optimizing a process of displaying video streams with specified event receives video streams. The video streams are sequenced based on a specified arrangement role to from a video stream queue. By analyzing each video streams, whether each video stream includes a specified event is determined. If each video stream without the specified event, the video streams are outputted based on the video stream queue. If the video stream includes the specified event, the video stream with the specified event is adjusted to be priority in the video stream queue, and the video streams are outputted based on the updated video stream queue. The video streams with the specified event can be prioritized processed and focus. A video stream processing apparatus and a computer readable storage medium applying the method are also provided.

Described herein are methods of generating learning data to facilitate de-biasing the labeled location of an object of interest within an image. Methods may include: receiving sensor data, where the sensor data is a first image; determining reference corner locations of an object in the first image using image processing; generating observed corner locations of the object in the first image from the determined reference corner locations; generating a bias transformation based, at least in part, on a difference between the reference corner locations and the observed corner locations of the object in the first image; receiving sensor data from another image sensor of a second image; receiving observed corner locations of an object in the second image from a user; and applying the bias transformation to the observed corner locations of the object in the second image to generate de-biased corners for the object in the second image.

Provided are an object identification method, apparatus, and device. The object identification method comprises: acquiring a first image of at least part of an object; determining a feature portion of the object on the basis of the first image; acquiring a second image of the feature portion of the object; and identifying an object category of the object on the basis of the second image. According to the object identification method, apparatus, and device, in which a feature portion of an object is acquired and identification of the category of the object is performed on the basis of the feature portion, operations are simple, and the accuracy of object identification can be effectively improved.

A model compression device includes a compression unit and a determination unit. The compression unit is configured to create a compression model arrived at by compressing a first prediction model created by machine learning. The determination unit is configured to determine whether or not a second prediction model created by re-learning the compression model can be further compressed on the basis of an index related to the performance of the second prediction model.

A program and the like that make a catheter system relatively easy to use. The program including a non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process comprising: acquiring a tomographic image generated using a diagnostic imaging catheter inserted into a lumen organ; and inputting the acquired tomographic image to a first model configured to output types of a plurality of objects included in the tomographic image and ranges of the respective objects in association with each other when the tomographic image is input, and outputting the types and ranges of the objects output from the first model.