Context-Aware Video QA Using Staged Controller
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Solution Overview
Problem
Current question answering systems lack context awareness and are resource-intensive, making them inefficient for providing accurate answers to queries related to video content, especially when paused at specific positions, as they fail to utilize supplementary materials effectively.
Innovation Solution
A context-aware method and device that analyzes supplementary materials like film literature, summaries, and closed captions to provide answers relevant to the paused position of a video, using a Staged QA controller algorithm to focus on the most relevant context, offloading processing to the cloud for lightweight and efficient real-time responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional QA models are used for video content querying, then general information can be provided, but context awareness is lacking and processing time is excessive
Solution Approach 1:
The patent segments the video content into discrete frames and extracts key visual elements (objects, actions, scenes) from each frame. This segmentation allows the system to process only relevant portions of the video content rather than analyzing the entire video, significantly reducing processing time while maintaining answer accuracy through focused context analysis.
Solution Approach 2:
The system performs preliminary action by pre-processing video content to generate frame-level captions and extract visual concepts before QA queries are submitted. This advance preparation creates an indexed structure of video content that enables rapid retrieval and comparison when questions are asked, eliminating the need for real-time video analysis and reducing processing time while improving answer reliability.
2Reliability
If comprehensive context information is analyzed, then answer quality improves, but system complexity increases
Solution Approach 1:
The patent applies local quality by focusing analysis on specific local regions of video frames where relevant visual elements are detected. Instead of analyzing entire frames uniformly, the system identifies and concentrates processing resources on localized areas containing key objects or actions, improving answer quality through targeted analysis while reducing overall system complexity by avoiding unnecessary processing of irrelevant regions.
Solution Approach 2:
The system introduces an intermediary layer of frame-level captions and visual concept extraction that mediates between raw video content and the QA system. This intermediary representation simplifies the complexity by transforming complex video data into structured, searchable text descriptions, enabling the QA system to work with simplified representations while maintaining comprehensive context awareness.
3Reliability
If supplementary materials are utilized, then context awareness improves, but processing overhead increases
Solution Approach 1:
The patent applies partial action by selectively utilizing only the most relevant supplementary materials (such as frame captions, scene descriptions, or specific metadata) rather than processing all available supplementary content. This selective approach maintains context awareness by focusing on pertinent information while reducing processing overhead by excluding unnecessary materials from the analysis pipeline.
Data Source
AI summary
A context-aware method for answering a question about a video includes: receiving the question about the video that is paused at a pausing position; obtaining and analyzing context information at the pausing position of the video, the context information including supplementary materials of the video; and automatically searching an answer to the question based on the context information at the pausing position of the video.