A method for processing an information for a vehicle, a vehicle, an electronic device and a storage medium are provided, relating to fields of intelligent transportation, Internet of Vehicles, image processing, voice technology, etc. The method for processing an information for a vehicle includes: determining a provider information associated with a target scene information in response to the target scene information being detected; determining at least one object information associated with the provider information based on the provider information; recommending the at least one object information; and performing a resource ownership transferring operation on a target object information among the at least one object information, in response to an operation instruction from a user for the target object information being received.

According to an embodiment of the present disclosure, there may be provided an operation method of a server for identifying disaster affected areas. In this instance, the operation method of the server may include acquiring at least one first disaster image; deriving an affected area from each of the at least one first disaster image, and acquiring affected area related information through labeling based on the derived affected area; and training a first learning model using the at least one first disaster image and the affected area related information.

This application relates to a machine learning model training method and apparatus, and an expression image classification method and apparatus. The machine learning model training method includes: obtaining a machine learning model that includes a model parameter and that is obtained through training according to a general-purpose image training set; determining a sample of a special-purpose image and a corresponding classification label; inputting the sample of the special-purpose image to the machine learning model, to obtain an intermediate classification result; and adjusting the model parameter of the machine learning model according to a difference between the intermediate classification result and the classification label, continuing training, and ending the training in a case that a training stop condition is met. The solutions provided in this application improve the training efficiency of the machine learning model.

A method recognizes an object of a mobile unit in a digital image that shows at least one partition of the mobile unit, especially in motion, by using a method for machine learning. To provide an accurate and reliable recognition the method includes using machine learning in a categorization step for categorizing the digital image, which shows the partition of the mobile unit, with a category. By using the machine learning in a detection step the object of the mobile unit in the categorized digital image and a location of the object in the categorized digital image are determined. By using machine learning in a segmentation step positions in the categorized digital image are classified such that it is determined whether at a respective position of the categorized digital image a part of the object is present or not.

Implementations relate to causing a command to be executed based on an image. In some implementations, a computer-implemented method includes obtaining and programmatically analyzing an image to determine suggested actions. The method causes a user interface to be displayed that includes user interface elements corresponding to default actions, and to suggested actions that are determined based on analyzing the image. The method receives user input indicative of selection of a particular action from the default actions and the suggested actions. The method causes a command to be executed by a computing device for the particular action that was selected.

Systems and methods are provided for analyzing messages generated by a plurality of computing devices associated with a plurality of users in a messaging system to generate training data to train a machine learning model to determine a probability that a media content item was generated inside an enclosed location or outside, receiving a media content item from a computing device, analyzing the media content item using the trained machine learning model to determine a probability that the media content item was generated inside an enclosed location or outside, determining, based on the probability generated by the trained machine learning model, that the media content item was generated inside an enclosed location, and determining an inside temperature associated with the venue based on messages generated by a plurality of computing devices in a messaging system comprising media content items and temperature information for the venue or a similar venue type.

Provided is a notification management method of a mobile terminal including generating a screenshot image, determining a category of the screenshot image based on a text or an image included in the screenshot image, and extracting a text or an image related to the category and generating notification information using the extracted text or image. The user equipment and the AI system of this disclosure may be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, and devices related to 5G services.

A vehicle information processing device includes an arrival determination unit that determines, based on positional information of a vehicle under autonomous driving control, whether or not the vehicle arrives at a destination, an approval determination unit that determines, based on approval notification, whether or not a user approves the arrival of the vehicle at the destination, a safety determination unit that determines whether or not surroundings of the vehicle are safe to the user who unboards the vehicle based on vehicle environment information surrounding the vehicle, and a door controller that requests a door control device to open a door in a case where determination is made that the vehicle arrives at the destination, determination is made that the user approves the arrival of the vehicle at the destination, and determination is made that the surroundings of the vehicle are safe to the user who unboards the vehicle.

A method for image content classification is described herein. The method includes counting a number of distinct color numbers in an image. The method also includes clustering blocks with a same distinct color number into a same class and determining a block occupancy rate of each color number for the image. Finally, the method includes classifying the image according to the block occupancy rate via a plurality of classifiers communicatively coupled in series.

Various implementations disclosed herein include devices, systems, and methods for modeling a geographical space for a computer-generated reality (CGR) experience. In some implementations, a method is performed by a device including a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, the method includes obtaining a set of images. In some implementations, the method includes providing the set of images to an image classifier that determines whether the set of images correspond to a geographical space. In some implementations, the method includes establishing correspondences between at least a subset of the set of images in response to the image classifier determining that the subset of images correspond to the geographical space. In some implementations, the method includes synthesizing a model of the geographical space based on the correspondences between the subset of images.