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 neural network, a system using this neural network and a method for training a neural network to output a description of the environment in the vicinity of at least one sound acquisition device on the basis of an audio signal acquired by the sound acquisition device, the method including: obtaining audio and image training signals of a scene showing an environment with objects generating sounds, obtaining a target description of the environment seen on the image training signal, inputting the audio training signal to the neural network so that the neural network outputs a training description of the environment, and comparing the target description of the environment with the training description of the environment.

To improve the accuracy of the recognition processing used in an image sensor. A solid-state imaging device includes a pixel array, a converter, an image processing unit, a digital signal processing unit, and a control unit. The pixel array has a plurality of pixels that perform photoelectric conversion. The converter converts an analog pixel signal output from the pixel array into digital image data. The image processing unit performs image processing on the digital image data. The digital signal processing unit performs recognition processing on the digital image data output by the image processing unit. The control unit performs optimization regarding at least one acquisition processing operation among acquisition of the analog pixel signal, acquisition of the digital image data, and acquisition of a result of the recognition processing based on the result of the recognition processing.

A method for driver identification including recording a first image of a vehicle driver; extracting a set of values for a set of facial features of the vehicle driver from the first image; determining a filtering parameter; selecting a cluster of driver identifiers from a set of clusters, based on the filtering parameter; computing a probability that the set of values is associated with each driver identifier of the cluster; determining, at the vehicle sensor system, driving characterization data for the driving session; and in response to the computed probability exceeding a first threshold probability: determining that the new set of values corresponds to one driver identifier within the selected cluster, and associating the driving characterization data with the one driver identifier.

Systems and methods for distributed training of deep learning models are disclosed. An example local device to train deep learning models includes a reference generator to label input data received at the local device to generate training data, a trainer to train a local deep learning model and to transmit the local deep learning model to a server that is to receive a plurality of local deep learning models from a plurality of local devices, the server to determine a set of weights for a global deep learning model, and an updater to update the local deep learning model based on the set of weights received from the server.

A circuit system and a method of analyzing audio or video input data that is capable of detecting, classifying, and post-processing patterns in an input data stream. The circuit system may consist of one or more digital processors, one or more configurable spiking neural network circuits, and digital logic for the selection of two-dimensional input data. The system may use the neural network circuits for detecting and classifying patterns and one or more the digital processors to perform further detailed analyses on the input data and for signaling the result of an analysis to outputs of the system.

A vehicle-searching guidance method includes: displaying a vehicle-searching guidance interface in response to a first trigger operation on a first control, the vehicle-searching guidance interface being used for providing guidance information for vehicle searching; transmitting a multimedia content obtaining request to an electronic device in response to a second trigger operation on a second control on the vehicle-searching guidance interface; receiving multimedia guidance content returned by the electronic device according to the multimedia content obtaining request, the multimedia guidance content including environmental information of a parking space in which a vehicle is parked; and presenting the multimedia guidance content for a user to search for the vehicle.

The present disclosure relates to communication technology, and provides a resource configuration method and apparatus, a device, and a storage medium. The method includes: receiving first resource configuration information from a network device. The first resource configuration information includes n resource configuration combinations each including first-type resource configuration information and second-type resource configuration information, where n is positive integer. The first-type resource configuration information indicates a radio resource configuration, and the second-type resource configuration information indicates an Artificial Intelligence (AI) resource configuration. The combined configuration solution according to the embodiments of the present disclosure can reduce the number of times the network device performs resource scheduling, and reduce the processing overhead of the network device.

This invention provides a system and method that uses a hybrid model for transportation-based (e.g. maritime) visual event detection of events. In operation, video data is reduced by detecting change and exclusively transmitting images to the deep learning model when changes are detected, or alternatively, based upon a timer that samples at selected intervals. Relatively straightforward deep learning models are used, which operate on sparse individual frames, instead of employing complex deep learning models that operate on multiple frames/videos. This approach reduces the need for specialized models. Independent, rule-based classifiers are used, based on the output of the deep learning model into visual events that, in turn, allows highly specialized events to be constructed. For example, multiple detections can be combined into higher-level single events, and thus, the existence maintenance procedures, cargo activities, and/or inspection rounds can be derived from combining multiple events or multiple detections.

A travel support system includes a server configured to support the travel of a vehicle. The server comprises a recognition unit configured to recognize an obstacle on a travel path of the vehicle, an obtainment unit configured to obtain, upon detecting an approaching vehicle which is approaching the obstacle, a blind spot region which occurs due to the obstacle recognized by the recognition unit, and a notification unit configured to notify the approaching vehicle of information of the blind spot region obtained by the obtainment unit. The server is arranged in an apparatus other than the approaching vehicle.