A computing system receives, from a client device, image data describing an image captured by an optical sensor of the client device. The computing system compares the image to a set of reference images that include associated metadata describing a real-world feature depicted by the respective reference image. The computing system determines, based on the comparison, a subset of reference images that are similar to the image, and then determines, based on associated metadata of the subset of reference images, that the image captured by the optical sensor of the client device depicts a first real-world feature. The computing system selects a subset of media overlays related to the first real-world feature based on metadata associated with each media overlay that describes the respective media overlay. The computing system transmits the subset of media overlays to the client device.

A method includes obtaining a set of image segment identigens for each image segment of an image to produce sets of image segment identigens. The sets of image segment identigens are possible interpretations of an image segment. The method further includes generating a set of relationships between image segments. The relationships provide a list of one or more ways in which the image segments are related. The method further includes processing different permutations of the sets of image segment identigens in accordance with the set of relationships to generate an entigen group. The entigen group represents a most likely interpretation of the image.

An approach is provided in which the approach trains a machine learning model using reference entries included in a reference dataset. During the training, the machine learning model learns a first set of unidirectional associations between the reference entries. The approach inputs a user dataset into the trained machine learning model and generates a second set of unidirectional associations between user dataset entries included in the user dataset. The approach builds a hierarchical relationship of the user dataset based on the second set of unidirectional associations and manages the user dataset based on the hierarchical relationship.

An information processing apparatus includes a processor configured to set a first reference object, and if a second reference object identical or similar to the first reference object is recognized, virtually display a target object in relation to the second reference object. The target object is recognized in advance together with the first reference object.

Systems, computer-implemented methods, and computer program products that can facilitate quantum topological classification are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a topological component that employs one or more quantum computing operations to identify one or more persistent homology features of a topological simplicial structure. The computer executable components can further comprise a topological classifier component that employs one or more machine learning models to classify the topological simplicial structure based on the one or more persistent homology features.

A content classification method includes receiving a set of content items from categories of a specific discipline, and extracting respective features from each content item. A labeling of the content items of the specific discipline is received, performed by human viewers, the labeling indicating a respective category assigned to the content item by the human viewers. A general-content brain-response model is uploaded, the model estimated using measurements of brains of humans presented with a general-content database defined using a set of features and includes a mapping between the set of features and a set of extracted brain activities. The model is applied to the extracted features, to calculate, using the labeling, a set of brain-responses for the specific discipline. Given a new content item associated with the discipline, a category of the discipline best matching the new content item is estimated, based on the model and the discipline-specific brain responses.

A system and method for method for identifying key points in a multimedia data element (MMDE). The method includes: identifying, via a computer vision system, a plurality of candidate key points in the MMDE, wherein a size of each candidate key point is equal to a predetermined size, wherein a scale of each candidate key point is equal to a predetermined scale; analyzing the plurality of candidate key points to determine a set of properties for each candidate key point; comparing the sets of properties of the plurality of candidate key points; and selecting, based on the comparison, a plurality of key points from among the candidate key points.

In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the uncompressed image is associated with a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage device.

An information processing apparatus includes a processor configured to set a first reference object, and if a second reference object identical or similar to the first reference object is recognized, virtually display a target object in relation to the second reference object. The target object is recognized in advance together with the first reference object.

A sensor assembly for determining one or more features of a local area is presented herein. The sensor assembly includes a plurality of stacked sensor layers. A first sensor layer of the plurality of stacked sensor layers located on top of the sensor assembly includes an array of pixels. The top sensor layer can be configured to capture one or more images of light reflected from one or more objects in the local area. The sensor assembly further includes one or more sensor layers located beneath the top sensor layer. The one or more sensor layers can be configured to process data related to the captured one or more images. Different sensor architectures featuring various arrangements of memory and computing devices are described, some of which feature in-memory computing. A plurality of sensor assemblies can be integrated into an artificial reality system, e.g., a head-mounted display.