Systems and methods are disclosed herein for monitoring a location of a client device associated with a transportation service and generating augmented reality images for display on the client device. The systems and methods use sensor data from the client device and a device localization process to monitor the location of the client device by comparing renderings of images captured by the client device to renderings of the vicinity of the pickup location. The systems and methods determine navigation instructions from the user's current location to the pickup location and select one or more augmented reality elements associated with the navigation instructions and/or landmarks along the route to the pickup location. The systems and methods instruct the client device to overlay the selected augmented reality elements on a video feed of the client device.

The present disclosure provides a photo album management method. The method includes obtaining voice search information from a user, performing intent recognition on the voice search information to obtain an intent recognition result which indicates an intent of the user for a photo album, obtaining a voiceprint feature from the voice search information to determine identity information of the user, sending the intent recognition result and the identity information of the user, and opening the photo album according to the intent recognition result and the identity information.

A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.

A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.

An image processing apparatus, an image processing method, a program, and a recording medium storing the program capable of extracting, from an input first image group, an image similar to an image extracted from a reference image group are provided. A first image group including a plurality of images of a user is transmitted to an order reception server. A second image group similar to the first image group is searched from a plurality of reference image groups stored in the order reception server. An image similar to an image previously extracted from the second image group is extracted from the first image group. An album is generated by arranging the image extracted from the first image group in a layout similar to a layout of an album generated from the second image group.

A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.

A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.

Even if an object to be detected is not remarkable in images, and the input includes images including regions that are not the object to be detected and have a common appearance on the images, a region indicating the object to be detected is accurately detected. A local feature extraction unit 20 extracts a local feature of a feature point from each image included in an input image set. An image-pair common pattern extraction unit 30 extracts, from each image pair selected from images included in the image set, a common pattern constituted by a set of feature point pairs that have similar local features extracted by the local feature extraction unit 20 in images constituting the image pair, the set of feature point pairs being geometrically similar to each other. A region detection unit 50 detects, as a region indicating an object to be detected in each image included in the image set, a region that is based on a common pattern that is omnipresent in the image set, of common patterns extracted by the image-pair common pattern extraction unit 30.

System, method and architecture for providing improved visual recognition by modeling visual content, semantic content and an implicit social network representing individuals depicted in a collection of content, such as visual images, photographs, etc., which network may be determined based on co-occurrences of individuals represented by the content, and/or other data linking the individuals. In accordance with one or more embodiments, using images as an example, a relationship structure may comprise an implicit structure, or network, determined from co-occurrences of individuals in the images. A kernel jointly modeling content, semantic and social network information may be built and used in automatic image annotation and/or determination of relationships between individuals, for example.

Computer-implemented methods and systems of updating user identifiers in an image-sharing environment include features for facilitating blocking, permitting, sharing and/or modifying content such as images and videos. User identification vectors providing data representative of a user and information about one or more facial characteristics of the user are broadcasted by a modular computing device. Information about one or more additional characteristics of the user (e.g., body characteristics and/or contextual characteristics) as determined from images of the user obtained by one or more image capture devices are received. An updated user identification vector including the information about one or more additional characteristics of the user is stored at and subsequently broadcasted by the modular computing device.