Topic Divergence Detection Using Knowledge Encoder Neural Networks
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Solution Overview
Problem
Conventional video analysis systems face challenges in accuracy, efficiency, and flexibility when classifying digital videos, particularly due to limited and inaccurate training data, and their inability to effectively utilize domain-specific knowledge and digital text corpuses to determine topic divergence from target topics.
Innovation Solution
The implementation of a machine learning approach using a topic-specific knowledge encoder neural network that generates topic divergence classifications by comparing words from digital videos with a digital text corpus, incorporating data augmentation techniques such as synthetic and hybrid transcripts to enhance training data and improve model robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video analysis systems are used to determine classifications based on visual and verbal content, then basic video classification can be achieved, but accuracy and ability to detect topic divergence are insufficient
Solution Approach 1:
The patent introduces a text corpus as an intermediary between the video content and the classification system. The text corpus containing domain-specific knowledge acts as a mediator that enables the system to accurately detect topic divergence by comparing video transcripts against established domain knowledge, thereby resolving the contradiction between detection accuracy and adaptability.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing domain-specific knowledge in a structured text corpus before actual video classification. This preliminary preparation of reference material enables more accurate real-time topic divergence detection without requiring complex integration during the classification process itself.
2Measurement precision
If more training data is used to improve model accuracy, then detection precision improves, but data availability and processing efficiency are limited
Solution Approach 1:
The patent creates synthetic training data by generating artificial video transcripts that mimic real video content characteristics. These synthetic copies of training data expand the available dataset without requiring additional real video recordings, thereby improving model accuracy while overcoming limitations in real data availability.
Solution Approach 2:
The system performs self-service by automatically generating its own training data through synthetic transcript creation. Instead of relying entirely on external data sources, the system generates its own training materials, eliminating the bottleneck of external data availability and enabling continuous model improvement.
3Speed
If real-time classification is implemented for livestream videos, then responsiveness is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing the text corpus and preparing reference materials before real-time classification. This upfront preparation reduces the computational burden during real-time livestream analysis, enabling fast response without excessive processing complexity during critical real-time operation.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating topic divergence classifications for digital videos based on words from the digital videos and further based on a digital text corpus representing a target topic. Particularly, the disclosed systems utilize a topic-specific knowledge encoder neural network to generate a topic divergence classification for a digital video to indicate whether or not the digital video diverges from a target topic. In some embodiments, the disclosed systems determine topic divergence classifications contemporaneously in real time for livestream digital videos or for stored digital videos (e.g., digital video tutorials). For instance, to generate a topic divergence classification, the disclosed systems generate and compare contextualized feature vectors from digital videos with corpus embeddings from a digital text corpus representing a target topic utilizing a topic-specific knowledge encoder neural network.