Disclosed herein are an apparatus and method for classifying clothing attributes based on deep learning. The apparatus includes memory for storing at least one program and a processor for executing the program, wherein the program includes a first classification unit for outputting a first classification result for one or more attributes of clothing worn by a person included in an input image, a mask generation unit for outputting a mask tensor in which multiple mask layers respectively corresponding to principal part regions obtained by segmenting a body of the person included in the input image are stacked, a second classification unit for outputting a second classification result for the one or more attributes of the clothing by applying the mask tensor, and a final classification unit for determining and outputting a final classification result for the input image based on the first classification result and the second classification result.

A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.

Aspects include receiving a request to perform an image classification task in a target domain. The image classification task includes identifying a feature in images in the target domain. Classification information related to the feature is transferred from a source domain to the target domain. The transferring includes receiving a plurality of pairs of task-irrelevant images that each includes a task-irrelevant image in the source domain and in the target domain. The task-irrelevant image in the source domain has a fixed correspondence to the task-irrelevant image in the target domain. A target neural network is trained to perform the image classification task in the target domain. The training is based on the plurality of pairs of task-irrelevant images. The image classification task is performed in the target domain and includes applying the target neural network to an image in the target domain and outputting an identified feature.

A vehicle includes receiving signals from a plurality of satellites; obtaining position information based on the received signal; detecting a driving speed and yaw rate; obtaining dead reckoning information based on position information about a position of a vehicle recognized in a previous cycle and the received detection information; predicting the position information based on the obtained dead reckoning information; obtaining a value of Euclidean distance based on the position information about the position of the vehicle recognized in the previous cycle and the obtained position information; generating a first outlier filter based on the value of the Euclidean distance; obtaining a value of Mahalanobis distance based on the obtained position information and the predicted position information; generating a second outlier filter based on the value of the Mahalanobis distance; recognizing a current position of the vehicle by fusing information passing through the first outlier filter and information passing through the second outlier filter; and outputting information about the current position of the recognized vehicle as an image or a sound.

Techniques and methods for training and/or using a machine learned model that identifies unsafe events. For instance, computing device(s) may receive input data, such as vehicle data generated by one or more vehicles and/or simulation data representing a simulated environment. The computing device(s) may then analyze features represented by the input data using one or more criteria in order to identify potential unsafe events represented by the input data. Additionally, the computing device(s) may receive ground truth data classifying the identified events as unsafe events or safe events. The computing device(s) may then train the machine learned model using at least the input data representing the unsafe events and the classifications. Next, when the computing device(s) and/or vehicles receive input data, the computing device(s) and/or vehicles may use the machine learned model to determine if the input data represents unsafe events.

A method performed by an electronic device is described. The method includes generating a depth map of a scene external to a vehicle. The method also includes performing first processing in a first direction of a depth map to determine a first non-obstacle estimation of the scene. The method also includes performing second processing in a second direction of the depth map to determine a second non-obstacle estimation of the scene. The method further includes combining the first non-obstacle estimation and the second non-obstacle estimation to determine a non-obstacle map of the scene. The combining includes combining comprises selectively using a first reliability map of the first processing and/or a second reliability map of the second processing The method additionally includes navigating the vehicle using the non-obstacle map.

An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of neural network models; obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models; respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class; obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class.

The present disclosure is directed toward systems and methods that enable more accurate digital object classification. In particular, disclosed systems and methods address inaccuracies in digital object classification introduced by variations in classification scores. Specifically, in one or more embodiments, disclosed systems and methods generate probability functions utilizing digital test objects and transform classifications scores into normalized classification scores utilizing probability functions. Disclosed systems and methods utilize normalized classification scores to more accurately classify and identify digital objects in a variety of applications.

Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

Disclosed is multi-modal few-shot learning device for user identification using a walking pattern based on deep learning ensemble. The device includes: a walking data collector configured to collect walking data of a user from a smart insole including any one or more of a pressure sensor, an acceleration sensor, and a gyro sensor; a preprocessor configured to convert a series of time series walking data obtained from each of the sensors included in the smart insole into a unit format data set; and an ensemble learner configured to apply an ensemble learning model that provides one final prediction by training CNN series learning and RNN series learning respectively and independently based on the unit-format data set generated by the preprocessor.