A method includes receiving video segments from a transcoder of a media device. The video segments are associated with multimedia content. The method further includes detecting, during a delay associated with the multimedia content and using an artificial intelligence (AI) technique, a particular representation in at least one of the video segments. The method further includes generating metadata associated with the particular representation and sending the metadata to a multimedia player to enable the multimedia player to modify playback of the multimedia content based on the metadata.

A method for implementing a moment of a videogame for playing a portion of a videogame; it includes receiving user selection input of a moment of a particular videogame associated with starting at a particular progress point of the particular videogame; causing an emulation to start of the particular videogame for streaming on the computer of the user; performing image analysis of the image stream to generate metadata on progress of the particular game by the user; determining if one or more end conditions of the moment is met through analysis of the metadata; and causing the emulation to end, resulting in an end of the streaming of the particular game on the computer of the user, if the one or more end conditions is met.

The disclosure relates to a method for displaying information, device, system, electronic device, and storage medium. The method includes acquiring, user tag of a user account in response to a trigger operation on an information page, the user tag corresponding to one or more types of media files of the user account. The method includes displaying the user tag on the information page. The method includes displaying an associated user account corresponding to the user tag in response to an operation of selecting target user tag from the user tag.

The disclosure is for a video caption generation apparatus and method thereof to generate a natural language sentence explaining a video used as input. The disclosure is configured by including an embedding unit to perform a video embedding and a category information embedding, a stack embedding encoder block unit to select a feature by utilizing the embedded video vector and category vector, a video-category attention unit to receive a result of the stack embedding encoder, to generate a similarity matrix and a feature matrix for a video and category information, and to provide a final encoding result, and a decoder module to generate a sentence by utilizing the final encoding result.

A computer-implemented is disclosed. The method includes: receiving media data of a live media stream; detecting a trigger associated with the media data of the live media stream; in response to detecting the trigger, generating at least one of audio or video overlay content associated with the trigger; and transmitting, to viewer devices, the at least one of audio or video overlay content with the live media stream.

An example method performed by a processing system includes retrieving a digital model of a media element from a database storing a plurality of media elements, wherein the media element is to be inserted into a scene of an audiovisual media, rendering the media element in the scene of the audiovisual media, based on the digital model of the media element and on metadata associated with the digital model to produce a rendered media element, wherein the metadata describes a characteristic of the media element and a limit on the characteristic, and inserting the rendered media element into the scene of the audiovisual media.

The serving of advertisements with (e.g., on) video documents may be improved in a number of ways. For example, a system may (a) accept information defining at least one ad spot associated with at least one instance of an video document, (b) accept offers to have advertisements served in the ad spot(s), and (c) arbitrate among competing advertisements, using at least the offers, to determine at least one advertisement to be served in that ad spot(s). As another example, a system may (a) accept relevance information for an advertisement, (b) determine at least one video document using the accepted relevance information, (c) present information about the video document(s) to an advertiser associated with the advertisement, and (d) accept, from the advertiser, an offer to have its advertisement served with at least one of the video document(s) accepted. As yet another example, a system may (a) accept relevance information for an video document, (b) determine a plurality of advertisements relevant to the video document using the relevance information and serving constraints of the advertisements, and (c) select at least one of the determined relevant advertisements to be served with the video document. Examples of video documents include video files published on the Internet, television programs, live or recorded talk shows, video-voice mail, segments of an video conversation, etc.

A method and apparatus for deep content tagging. A media device receives one or more first frames of a content item, where the one or more first frames spans a duration of a scene in the content item. The media device detects one or more objects or features in each of the first frames using a neural network model and identifies one or more first genres associated with the first frames based at least in part on the detected objects or features in each of the first frames. The media device further controls playback of the content item based at least in part on the identified first genres.

A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

The present disclosure describes techniques of pushing information associated with the at least one location that is associated with a video. The disclosed techniques comprises obtaining video data, wherein the video data comprise a plurality frames of a video and information associated with the video; determining at least one location associated with at least one frame among the plurality of frames of the video based on comparing the video data with data included in a database; determining information associated with the at least one location; and pushing the information associated with the at least one location to a first computing device based on a time point of playing the at least one frame among the plurality of frames of the video.