Techniques are disclosed for performing video synthesis of audiovisual content. In an example, a computing system may determine first parameters of a face and body of a source person from a first frame in a video shot. The system also determines second parameters of a face and body of a target person. The system determines that the target person is a replacement for the source person in the first frame. The system generates third parameters of the target person based on merging the first parameters with the second parameters. The system then performs deferred neural rendering of the target person based on a neural texture that corresponds to a texture space of the video shot. The system then outputs a second frame that shows the target person as the replacement for the source person.

Methods and systems are proposed herein to address the inefficiencies in the recommendation schemes available. More specifically, the embodiments disclosed herein provide a new recommendation scheme, whereby a user enters various criteria for what he/she would like to be recommended. For example, the system may receive a user preference for content recommendations from a user. The system may retrieve a user profile for the user. The system may compare the user preference to the user profile to determine a criterion for content recommendations for the user. The system may receive a content attribute for content provided by a content provider. The system may match the criterion to the content attribute. The system may, in response to matching the criterion to the content attribute, generate for display a recommendation to the user for the content.

Mechanisms for selecting formats for streaming media content items are provided. In some embodiments, methods for selecting formats for streaming media content items are provided that include: receiving, at a server from a user device, a request to begin streaming a video content item on the user device; receiving, from the user device, network information indicating a quality of a network connection of the user device to a communication network used to stream the video content item and device information related to the user device; selecting, by the server, a first format for the video content item, wherein the first format includes a first resolution of a plurality of resolutions based on the network information and the device information; transmitting, from the server, a first portion of the video content item having the first format to the user device; receiving, at the server from the user device, updated network information and updated device information; selecting, by the server, a second format for the video content item, wherein the second format includes a second resolution of the plurality of resolutions based on the updated network information and the updated device information; and transmitting, from the server, a second portion of the video content item having the second format to the user device.

A multimodal recommendation identification system analyzes data describing a sequence of past content item interactions to generate a recommendation for a content item for a user. An indication of the recommended content item is provided to a website hosting system or recommendation system so that the recommended content item is displayed or otherwise presented to the user. The multimodal recommendation identification system identifies a content item to recommend to the user by generating an encoding that encodes identifiers of the sequence of content items the user has interacted with and generating encodings that encode multimodal information for content items in the sequence of content items the user has interacted with. An aggregated information encoding for a user based on these encodings and a system analyzes the content item sequence encoding and interaction between the content item sequence encoding and the multiple modality encodings to generate the aggregated information encoding.

Example systems and methods for automated generation of banner images are disclosed. A program identifier associated with a particular media program may be received by a system, and used for accessing a set of iconic digital images and corresponding metadata associated with the particular media program. The system may select a particular iconic digital image for placing a banner of text associated with the particular media program, by applying an analytical model of banner-placement criteria to the iconic digital images. The system may apply another analytical model for banner generation to the particular iconic image to determine (i) dimensions and placement of a bounding box for containing the text, (ii) segmentation of the text for display within the bounding box, and (iii) selection of font, text size, and font color for display of the text. The system may store the particular iconic digital image and banner metadata specifying the banner.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to perform probabilistic modeling for anonymized data integration and measurement of sparse and weakly-labeled datasets are disclosed. An apparatus includes a training controller to train a neural network to produce a trained neural network to output model parameters of a probability model, a model evaluator to execute the trained neural network on input data specifying a time of day, a media source, and at least one feature different from the time of day and the media source to determine one or more first model parameters of the probability model, and a ratings metric generator to evaluate the probability model based on input census data to determine a ratings metric corresponding to the time of day, the media source, and the at least one feature, the probability model configured with the one or more first model parameters.

Various arrangements for performing dynamic volume control are provided. Audio characteristics of audio content being output to a user may be identified. Adjustments made to an audio volume setting by the user while the audio content is being output to the user can be monitored. A machine learning model can be trained based on the adjustments made to the audio volume setting by the user that are mapped with the audio characteristics of the audio content. After the machine learning model is trained, the audio volume setting can be adjusted based at least in part on the trained machine learning model analyzing audio content.

A method implemented by one or more computing systems includes accessing content viewing data associated with a first user account, wherein the first user account is associated with one or more client devices. The content viewing data includes temporal-based content viewing data. The method further includes determining, using one or more sequence models, a set of content viewing features based on the temporal-based content viewing data, and concatenating the content viewing features into a single computational array. The method further includes providing, through one or more dense layers of a deep-learning model, the single computational array to an output layer of the deep-learning model, and calculating, based on the output layer, one or more probabilities for one or more labels for the first user account. Each label includes a predicted attribute for the first user account.

A mobile artificial neural network device is provided. The mobile artificial neural network device includes a camera configured to output a video of a product at a first frame rate, an AI recognition model configured to recognize a product information by receiving the product video, an artificial neural network processor configured to drive the AI recognition model at a second frame rate, and a display module configured to display the video of the product at the first frame rate and display the product information at the second frame rate.

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation which may be efficiently processed to determine correspondence. Applications of the interface and system include a VCR, medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.