An exemplary geospatial image processing system generates, based on multiple detections of an object of interest detected by a computer vision model in multiple, correlated images of a geospatial location captured from different camera viewpoints, user interface content that includes a visual indication of the detected object of interest superimposed at an object position on a view of the geospatial location. The system provides the user interface content for display in a graphical user interface view of a user interface and provides, by way of the user interface, a user interface tool configured to facilitate user validation of one or more of the multiple detections of the object of interest. The system may receive, a user validation of one or more of the multiple detections of the object of interest and may train the computer vision model based on the user validation. Corresponding methods and systems are also disclosed.

The invention provides an information processing apparatus comprising a first acquiring unit which acquires, from an external apparatus which can communicate with the information processing apparatus via a network, a first learning model which outputs recognition information in response to input of image information; a learning unit which causes the first model to learn a result of control by the information processing apparatus using recognition information that is output from a second learning model in response to input of the image information in an execution environment; and an output unit which causes the first learning model learned by the learning unit to output recognition information by inputting image information in the execution environment to the first learning model.

An interactive autonomous driving system for an autonomous driving vehicle may include: a target mapping device that determines whether an obstacle is present in a predetermined range of a target selected by a passenger and outputting obstacle information; a target attribute determination device that determines a target attribute based on the obstacle information and outputs target controllable item information; and a processor that generates control mode recommendation information selectable by the passenger based on the target controllable item information and outputs target attribute information and a selected control mode when control mode selection information is received from the passenger.

Approaches for automated fashion designing are described. A computer-implemented method for automated fashion designing includes: training, by a computer device, computer models using deep learning based computer vision; identifying, by the computer device, at least one gap using cognitively determined fashionability scores (F-scores); and creating, by the computer device, a new fashion design using the computer models and the at least one identified gap.

A system for updating a descriptor trail using artificial intelligence. The system is configured to display on a graphical user interface operating on a processor connected to a memory an element of diagnostic data. The system is configured to receive from a user client device an element of user constitutional data. The system is configured to display on a graphical user interface the element of user constitutional data. The system is configured to prompt an advisor input on a graphical user interface. The system is configured to receive from an advisor client device an advisor input containing an element of advisory data. The system is configured to generate an updated descriptor trail as a function of the advisor input. The system is configured to display the updated descriptor trail on a graphical user interface.

Methods, systems, and media for selecting candidates for annotation for use in training classifiers are provided. In some embodiments, the method comprises: identifying, for a trained Convolutional Neural Network (CNN), a group of candidate training samples, wherein each candidate training sample includes a plurality of patches; for each patch of the plurality of patches, determining a plurality of probabilities, each probability being a probability that the patch corresponds to a label of a plurality of labels; identifying a subset of the patches in the plurality of patches; for each patch in the subset of the patches, calculating a metric that indicates a variance of the probabilities assigned to each patch; selecting a subset of the candidate training samples based on the metric; labeling candidate training samples in the subset of the candidate training samples by querying an external source; and re-training the CNN using the labeled candidate training samples.

Systems and methods for clustering data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a first client device, a classification data associated with a classification model and receiving feature data corresponding to the classification model output. The operations may include training a meta-model to predict the classification data based on the feature data and/or additional data associated with the classification data such as location data or environmental data. The operations may include generating a meta-model output based on the classification data , the feature data, and/or the additional data. The operations may include updating the classification model based on the meta-model output and transmitting the updated classification model to at least one of the first client device or a second client device.

Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines. Computer-readable apparatus and computing systems are also disclosed.

Methods, systems, and computer program products for generating concept images of human poses using machine learning models are provided herein. A computer-implemented method includes identifying one or more events from input data by applying a machine learning recognition model to the input data, wherein the identifying comprises (i) detecting multiple entities from the input data and (ii) determining one or more behavioral relationships among the multiple entities in the input data; generating, using a machine learning interpretability model and the identified events, one or more images illustrating one or more human poses related to the identified events; outputting the one or more generated images to at least one user; and updating the machine learning recognition model based at least in part on (i) the one or more generated images and (ii) input from the at least one user.

Systems and methods are disclosed for supplementing computer-generated results with third party feedback and educational information. In embodiments, a method includes: receiving user input from a user during an automated response-generating event; determining whether to present educational information with a result based on user data, wherein the educational information is information automatically generated by the computing device regarding a decision-making process utilized to generate the result; determining whether to present third party feedback with the result based on the user data, wherein the third party feedback includes information obtained from a human participant; and presenting a response to the user including the result, wherein content of the response is based on the determining whether to present the educational information with the result and the determining whether to present the third party feedback with the result.