A system and method for navigating digital media assets including a navigation system configured to receive a search query in response to a user input and process the search query by applying the search query to a search index of digital media asset conventional and time-based metadata and determining search results of titles of and start points in time within digital media assets that satisfy the search query. The navigation system may then display the search results to the user through the user interface. The search results may be displayed in a hierarchical format, wherein the title of the digital media asset is displayed and upon selecting the title of the digital media asset, the start points in time within that digital media asset are displayed or played as a video to the user through the user interface.

The present disclosure is related to systems and methods for storing data. The method includes obtaining a streaming data file including a first set of data frames. The method includes, in response to determining that the streaming data file satisfies one or more conditions, generating a hole frame storing an offset address of the streaming data file, and establishing a target streaming data file by adding a second set of data frames into the streaming data file based on the hole frame.

This document describes, among other things, systems, methods, devices, and other techniques for using information about how long various videos were presented at client devices to determine subsequent video recommendations and search results. In some implementations, a computing can include a modeling apparatus, a front-end server, a request manager, one or more video file storage devices, a video selector, or a combination of some or all of these. The video selector can select video content for a particular digitized video among a plurality of digitized videos to serve to a computing device responsive to a request. The selection can be based at least in part on how long the particular digitized video has been presented at client devices associated with users having characteristics that match one or more characteristics of the user that submitted the request for video content, as indicated by the modeling apparatus.

This document describes, among other things, systems, methods, devices, and other techniques for using information about how long various videos were presented at client devices to determine subsequent video recommendations and search results. In some implementations, a computing can include a modeling apparatus, a front-end server, a request manager, one or more video file storage devices, a video selector, or a combination of some or all of these. The video selector can select video content for a particular digitized video among a plurality of digitized videos to serve to a computing device responsive to a request. The selection can be based at least in part on how long the particular digitized video has been presented at client devices associated with users having characteristics that match one or more characteristics of the user that submitted the request for video content, as indicated by the modeling apparatus.

A computer-implemented method executed by at least one processor for performing mini-batching in deep learning by improving cache utilization is presented. The method includes temporally localizing a candidate clip in a video stream based on a natural language query, encoding a state, via a state processing module, into a joint visual and linguistic representation, feeding the joint visual and linguistic representation into a policy learning module, wherein the policy learning module employs a deep learning network to selectively extract features for select frames for video-text analysis and includes a fully connected linear layer and a long short-term memory (LSTM), outputting a value function from the LSTM, generating an action policy based on the encoded state, wherein the action policy is a probabilistic distribution over a plurality of possible actions given the encoded state, and rewarding policy actions that return clips matching the natural language query.

A computer-implemented method executed by at least one processor for performing mini-batching in deep learning by improving cache utilization is presented. The method includes temporally localizing a candidate clip in a video stream based on a natural language query, encoding a state, via a state processing module, into a joint visual and linguistic representation, feeding the joint visual and linguistic representation into a policy learning module, wherein the policy learning module employs a deep learning network to selectively extract features for select frames for video-text analysis and includes a fully connected linear layer and a long short-term memory (LSTM), outputting a value function from the LSTM, generating an action policy based on the encoded state, wherein the action policy is a probabilistic distribution over a plurality of possible actions given the encoded state, and rewarding policy actions that return clips matching the natural language query.

A media file processing method includes: recognizing content features of a target media file, wherein the content features include an image feature and/or a sound feature; determining a target aggregation theme of the target media file according to the recognized content features of the target media file; determining the target media file as media files under the target aggregation theme; and synthesizing the media files under the target aggregation theme in response to a video clip instruction with respect to the target aggregation theme, to obtain a target video corresponding to the target aggregation theme.

A media file processing method includes: recognizing content features of a target media file, wherein the content features include an image feature and/or a sound feature; determining a target aggregation theme of the target media file according to the recognized content features of the target media file; determining the target media file as media files under the target aggregation theme; and synthesizing the media files under the target aggregation theme in response to a video clip instruction with respect to the target aggregation theme, to obtain a target video corresponding to the target aggregation theme.

The embodiments relate to a method for writing, in a container file, two or more subpicture tracks; writing, in the container file, a base track, which is intended to be resolved into a video bitstream; indicating, in the base track, a layout of subpictures; writing, in the container file, a sample group description entry indicative of a first subpicture track or a group of subpicture tracks for each subpicture position in the layout of subpictures, wherein the first subpicture track includes the subpicture sequence for the respective subpicture position and wherein any track among the group of subpicture tracks includes a valid subpicture sequence for the respective subpicture position; and indicating in the container file, samples of the base track for which the sample group description entry is intended to be used for reconstructing the video bitstream. The embodiments also relate to a method for parsing, as well as technical equipment for implementing the method for writing and the method for parsing.

A method for searching a video and equipment with a video search function are provided. The method for searching a video includes constructing a video DB by analyzing continuity of a tag given to an appearing object and extracting section information about the tag, and detecting video information. An object may be recognized, a video database may be constructed, and a video may be searched on the basis of analysis based on an artificial intelligence (AI) model through a 5G network.