A system for identifying perishable products in a retail store based on analysis of image data and for automatically generating suggestions relating to the identified products is provided. The system may comprise at least one processor configured to: receive a set of images depicting a plurality of perishable products displayed on at least one shelving unit in a retail store; analyze the set of images to determine information about a displayed inventory of the plurality of perishable products; obtain information about additional perishable products scheduled to be displayed on the at least one shelving unit; use the information about the displayed inventory and the information about the additional perishable products to determine at least one suggestion regarding placement of perishable products in the retail store; and provide the at least one suggestion to an entity associated with the retail store.

Implementations described herein relate to methods, devices, and computer-readable media to generate and provide image-based creations. A computer-implemented method includes obtaining a plurality of episodes, each episode associated with a corresponding time period and including a respective set of images and person identifiers for each image. The method further includes forming a respective cluster for each episode that includes at least two person identifiers. The method further includes determining whether one or more person identifiers are included in less than a threshold number of clusters, and in response, removing the one or more person identifiers from the clusters that the one or more person identifiers that are included in. The method further includes merging identical clusters to obtain a plurality of people groups that each include two or more person identifiers and providing a user interface that includes an image-based creation based on a particular people group.

A method includes obtaining first data representing a subterranean domain, and creating a geotag associated with a three-dimensional location of interest in the subterranean domain and a feature at the location of interest. The geotag is further associated with second data that describes the location of interest in the subterranean domain. The method also includes generating metadata describing a portion of the first data, the second data, or both, storing the geotag in a database including a plurality of geotags. The method may also include searching for an analog to the geotag.

Systems and methods are disclosed herein for recovering inaccurate or missing base map data for an area while providing riders with incentive to refrain from selecting an alternate transportation service while experiencing increased wait times. A service receives a request for a driver and locates a driver to provide the service. The service determines whether the wait time for the rider exceeds a threshold wait time established by the service. If the service determines that the wait time exceeds the threshold wait time, the service identifies an object within a threshold vicinity of the rider known to have inaccurate or missing base map data. The service transmits a prompt for the rider to capture an image of the object. The service receives a rendering of the captured images from the client device, and activates a function via the client device if the images accurately capture the identified objects included in the prompt.

A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

A system for using metadata from a video signal to associate advertisements therewith, comprising (i) a segmentation system to divide the video signal into video clips, (ii) a digitizing system for digitizing the video clips, (iii) a feature extraction system for extracting audio and video features from each video clip, associating each audio feature with respective video clips, associating each video feature with respective video clips, and saving the audio and video features into an associated metadata file, (iv) a web interface to the feature extraction system for receiving the video clips, and (v) a database, wherein video signals and associated metadata files are stored and indexed, wherein the associated metadata file is provided when a video player requests the corresponding video signal, enabling selection of a relevant advertisement for presentment in conjunction with respective video clips based on the associated audio and video features of the respective video clip.

A system for using metadata from a video signal to associate advertisements therewith, comprising (i) a segmentation system to divide the video signal into video clips, (ii) a digitizing system for digitizing the video clips, (iii) a feature extraction system for extracting audio and video features from each video clip, associating each audio feature with respective video clips, associating each video feature with respective video clips, and saving the audio and video features into an associated metadata file, (iv) a web interface to the feature extraction system for receiving the video clips, and (v) a database, wherein video signals and associated metadata files are stored and indexed, wherein the associated metadata file is provided when a video player requests the corresponding video signal, enabling selection of a relevant advertisement for presentment in conjunction with respective video clips based on the associated audio and video features of the respective video clip.

Systems and methods are provided for automatically generating a digital imaging and communications in medicine (DICOM) conformance statement. In one example, a method includes determining a set of DICOM query/retrieve capabilities of a DICOM application implemented on a DICOM device by performing a list of queries to the DICOM application, and populating a DICOM conformance statement with the determined set of DICOM query/retrieve capabilities.

Systems and methods are provided for automatically generating a digital imaging and communications in medicine (DICOM) conformance statement. In one example, a method includes determining a set of DICOM query/retrieve capabilities of a DICOM application implemented on a DICOM device by performing a list of queries to the DICOM application, and populating a DICOM conformance statement with the determined set of DICOM query/retrieve capabilities.