Time-Indexed Video Anchors Using Entity Labels for Fast Skimming
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Solution Overview
Problem
Videos cannot be efficiently skimmed for specific content, leading to users spending excessive time searching for key moments, which consumes network bandwidth and processing resources.
Innovation Solution
A video anchor model is trained to generate time-indexed anchors with descriptive labels, allowing users to quickly navigate to important parts of a video, reducing the need for full playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually scrub through video to find key moments, then they can locate specific content, but it consumes excessive time and network bandwidth
Solution Approach 1:
The system performs preliminary analysis of video content during indexing, extracting key moments, entities, and captions before user viewing. This pre-processing creates a searchable structure that enables rapid navigation without requiring users to manually scrub through the entire video, thus resolving the contradiction between time efficiency and ease of finding specific content
Solution Approach 2:
The patent introduces an intermediary indexing system that acts as a bridge between the full video content and user search queries. This intermediary layer pre-extracts and organizes key information (entities, captions, timestamps) so users can efficiently locate content without directly interacting with the raw video stream, reducing both time and bandwidth consumption
2Measurement precision
If the video anchor model processes all video content, then accurate anchors are generated, but network bandwidth and processing resources are consumed
Solution Approach 1:
The system extracts only the essential and relevant portions of video content for anchor generation, rather than processing the entire video. By identifying and extracting key moments, prominent entities, and significant captions, the system achieves accurate anchors while minimizing the processing of unnecessary video data, thus reducing network bandwidth and computational resource consumption
Solution Approach 2:
The patent applies local quality by focusing processing resources on specific segments of the video that contain key moments or prominent entities, rather than uniformly processing the entire video. This selective approach concentrates computational effort where it is most needed, maintaining anchor accuracy while reducing overall resource consumption
Data Source
AI summary
In one aspect, a method includes obtaining videos and for each video: obtaining a set of anchors for the video, each anchor beginning at the playback time and including anchor text; identifying, from text generated from audio of the video, a set of entities specified in the text, wherein each entity in the set of entities is associated with a times stamp at which the entity is mentioned; determining, by a language model and from the text generated from the audio of the video, an importance value for each entity; for a subset of the videos, receiving rater data that describes, for each anchor, the accuracy of the anchor text in describing subject matter of the video; and training, using the human rater data, the importance values, the text, and the set of entities, an anchor model that predicts an entity label for an anchor for a video.