Disclosed are a method and device for predicting face beauty based on migration and weak supervision and a storage medium. The method includes: preprocessing an inputted face image; training a source domain network by using the preprocessed image, and migrating a parameter of the source domain network to a target domain network; inputting a noise image marked with a noise label and a truth-value image marked with a truth-value label into the target domain network to obtain an image feature; and inputting the image feature into a classification network to obtain a final face beauty prediction result.

Techniques for determining value of a home by applying one or more neural network models to images of spaces in the home. The techniques include: obtaining at least one image of a first space inside or outside of a home; determining a type of the first space by processing the at least one image of the first space with a first neural network model; identifying at least one feature in the first space by processing the at least one image with a second neural network model different from the first neural network model and trained using images of spaces of a same type as the first space; and determining a value of the home at least in part by using the at least one feature as input to a machine learning model different from the first neural network model and the second neural network model.

Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

A method for improving robustness of a visual-inertial navigation system includes: determining a reference frame; correcting the reference frame; and performing non-linear optimization for frames other than the reference frame and an oldest frame according to the oldest frame and the corrected reference frame.

A road information processing method, an electronic device and a storage medium are provided, relates to the field of intelligent transportation, and may be used for the field of cloud computing or cloud. The method includes: acquiring a road image, and identifying a street lamp in the road image to obtain a street lamp identification result; associating the road image with an electronic map to obtain a road corresponding to the road image in the electronic map; and marking a street lamp attribute for the road corresponding to the road image in the electronic map based on the street lamp identification result.

A processor associated with a vehicle receives sensor data from a plurality of sensors associated with a vehicle, where each sensor generates data corresponding to a different parameter of a passenger in the vehicle. Based on the sensor data, one or more primitive emotional indications are generated. The processor applies a model to the one or more primitive emotional indications that when applied outputs a contextualized emotional indication associated with the passenger that includes an assessment of an emotional state of the passenger and a reason for the emotional state. A contextual response is selected based on the contextualized emotional indication, and the processor causes an output by the vehicle to enact the contextual response.

The present embodiments relate to selection and execution of one or more output actions relating to a modification of at least one feature of a vehicle. A series of sensors on a vehicle can acquire data that can be used to identify vehicle environment characteristics indicative of a status of a vehicle environment and an emotional state of the user. The vehicle environment characteristics and the emotional state can be processed using a user model that corresponds to a user to generate one or more selected output actions. The output actions can be executed on the vehicle to increase user experience. The output actions can relate to any of entertainment features, safety features, and/or comfort features of the vehicle.

Embodiments described herein provide a two-stage online detection of action start system including a classification module and a localization module. The classification module generates a set of action scores corresponding to a first video frame from the video, based on the first video frame and video frames before the first video frames in the video. Each action score indicating a respective probability that the first video frame contains a respective action class. The localization module is coupled to the classification module for receiving the set of action scores from the classification module and generating an action-agnostic start probability that the first video frame contains an action start. A fusion component is coupled to the localization module and the localization module for generating, based on the set of action scores and the action-agnostic start probability, a set of action-specific start probabilities, each action-specific start probability corresponding to a start of an action belonging to the respective action class.

An apparatus and method for recognizing a driving lane of a vehicle are provided. The apparatus includes an information acquisition device that acquires forward information of a road on which the vehicle is driving, and a processor that recognizes an entrance section and an exit section on a front left side of the vehicle based on the forward information, and determines a driving lane of the vehicle by correcting a number of lanes of the road in the entrance section and the exit section.

A computer program product is embodied on a non-transitory computer readable medium. The computer readable medium has instructions stored thereon that, when executed by a computer, causes the computer to perform receiving image data that is to be recognized by the at least one classifier. The image data captures an occurrence of a fossil species. The computer can also perform generating an output via the at least one classifier based on the received image data. The computer can also perform comparing the output of the at least one classifier with a desired output. The computer can also perform modifying the at least one classifier so that the output of the classifier corresponds to the desired output.