Video Question Answering via Audio Context Vector Matching
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Solution Overview
Problem
Conventional video systems that provide instructional videos and answer user questions during playback are limited in their ability to provide accurate answers, as they often rely solely on natural language processing and lack context, leading to frustration and incorrect responses.
Innovation Solution
A video system that determines the context of a video at the time a question is asked by processing audio sentences and generates a classification vector by concatenating question and context representations, allowing it to match with answer vectors to provide accurate answers, utilizing a domain knowledge base and answer encoder configured based on a graph structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the video system processes only the natural language question by itself, then the processing complexity is low, but the answer accuracy deteriorates because sufficient information is not contained in the question alone
Solution Approach 1:
The patent combines the question representation with the context representation (audio sentences from the video) to form a unified input for answer generation. This merging allows the system to leverage both the explicit question and the implicit contextual information from the video content, thereby improving answer accuracy without requiring separate complex processing pipelines.
Solution Approach 2:
The system performs preliminary processing of the audio sentences from the video to extract contextual information before the question is even asked. By pre-processing and storing the context representation, the system reduces the complexity of real-time processing when a question is submitted, while still having access to comprehensive contextual data for accurate answer generation.
2Reliability
If the video system uses only the natural language question for answer generation, then the system complexity is low, but the reliability of answers deteriorates due to insufficient context
Solution Approach 1:
The patent merges the question representation with the context representation derived from audio sentences in the video. This combination ensures that answer generation relies on both the explicit query and the implicit contextual information, significantly improving answer reliability by grounding responses in the actual video content rather than relying solely on the potentially ambiguous natural language question.
3Measurement precision
If the video system processes context and question together to generate answers, then the answer accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary processing of the audio sentences to extract and store context representations in advance. When a question is submitted, the system can quickly retrieve the pre-processed context and combine it with the question representation, rather than processing the entire audio content from scratch. This significantly reduces processing time while maintaining high answer accuracy.
4Reliability
If the video system generates answers based on domain knowledge base with graph structure, then the answer reliability improves, but the device complexity increases
Solution Approach 1:
The patent introduces a domain knowledge base with graph structure as an intermediary between the question-context input and the answer generation process. This knowledge base acts as a mediator that guides and constrains the answer generation, ensuring that responses are grounded in domain-specific facts and relationships. While this adds some complexity, it significantly improves answer reliability by preventing hallucinations and ensuring factual accuracy.
Data Source
AI summary
In implementations of answering questions during video playback, a video system can receive a question related to a video at a timepoint of the video during playback of the video, and determine audio sentences of the video that occur within a segment of the video that includes the timepoint. The video system can generate a classification vector from words of the question and the audio sentences, and determine an answer to the question utilizing the classification vector. The video system can obtain answer candidates, and the answer to the question can be selected as one of the answer candidates based on matching the classification vector to one of the answer vectors.